Wind turbine drivetrain fault diagnosis and intelligent O&M: a review

4.2.1. Core transfer paradigms

To directly address the distribution discrepancies between source-domain data and target-domain operating conditions, adversarial domain adaptation has emerged as a fundamental technical route for cross-turbine diagnosis. Represented by the domain-adversarial neural network, the core philosophy of global adversarial adaptation stems from the zero-sum game theory of GANs.

Global adversarial adaptation uses a domain discriminator and gradient reversal layer to reduce marginal-distribution shifts between source and target domains. However, because it does not explicitly preserve class-conditional structures, it may blur fault boundaries and induce negative transfer when fault proportions or label spaces differ across wind farms.

Conditional subdomain adaptation instead aligns corresponding fault-related subdomains using class-aware discriminators, fault prototypes, joint feature-label distributions, or confidence-weighted pseudo-labels. By reducing intra-class variation while preserving inter-class margins, it is better suited to multi-fault, variable-speed, and noisy conditions.

Nevertheless, fine-grained alignment is not universally superior. Its performance depends heavily on the reliability of target-domain pseudo-labels and class-prototype estimation; incorrect pseudo-labels, severe class imbalance, or absent target-domain fault classes may cause erroneous local matching and amplify negative transfer. It also introduces additional computational and hyperparameter-tuning costs compared with a single global discriminator. Therefore, global alignment remains suitable for scenarios dominated by coarse operating-condition shifts with relatively consistent class structures, whereas conditional subdomain alignment is more appropriate for multi-fault and strongly nonstationary wind power scenarios, provided that confidence estimation, robust sample selection, and class-imbalance-aware mechanisms are incorporated. Global-local adaptation networks combine both mechanisms and have demonstrated effectiveness in pitch-bearing diagnosis^[56].

When source and target domains have inconsistent label spaces, partial domain adaptation uses attention-based weighting to suppress source-only classes and reduce negative transfer in gearbox diagnosis^[57]. A graph causal framework with adaptive expert ensembles provides a distinct approach to addressing distributional shifts in fault diagnosis^[58]. Adversarial adaptation has further been extended from vibration analysis to unmanned aerial vehicle (UAV)-based visual inspection through soft-mask-guided Faster R-CNN models^[59].

In real-world wind power scenarios, diagnostic systems often encounter data from multiple turbines or heterogeneous operating conditions. Multi-source domain transfer and meta-learning frameworks have significantly expanded the generalization boundaries by systematically integrating knowledge from diverse domains. For instance, a multi-domain multi-task learning framework leverages data from various experimental test rigs to construct models for deployment on real-world turbines^[60]. This approach, combined with models such as CNN-LSTM and improved GANs, enables comprehensive performance evaluation within a unified framework^[61]. Simultaneously, source-free unsupervised domain adaptation methods have demonstrated exceptional adaptability in large-scale experiments involving multiple wind farms and diverse turbine units^[62,63].

Standard transfer learning typically assumes that data from both the source and target domains are simultaneously accessible. However, in real-world wind power scenarios, commercial privacy protection and limited bandwidth for massive data transmission often constrain access to raw fault samples. To address these data silos and privacy challenges, source-free transfer strategies have emerged. For instance, source-free domain adaptation under partial information has been investigated for rotating-machinery fault diagnosis without access to the original source-domain data during adaptation^[64]. This offers a relevant methodological basis for settings in which sharing raw source data is restricted, but does not by itself establish cross-wind-farm performance.

Furthermore, considering the time-varying characteristics throughout the full life cycle of a device, a micro-transfer learning mechanism achieves more streamlined, unit-level knowledge transfer by modeling multiple differentiated distributions^[65]. Regarding multi-source heterogeneous operating conditions, domain adaptation region mechanisms facilitate matching of both global and category-wise distributions by constructing adaptive intermediate distributions^[66].

Beyond data access constraints, another extreme challenge arises when the target domain encounters previously unseen fault types, as encountered in zero-shot or few-shot scenarios. In response to such data deserts, single-domain generalization methods offer a viable solution for online diagnosis without target data by learning features that remain invariant to future domain shifts^[67]. Under few-shot conditions, strategies that combine the transformation of 1D vibration signals into 2D images with deep residual networks have proven effective. For instance, reconstructing feature representations using Gramian angular difference fields has shown high reliability for imbalanced and heterogeneous data^[68,69]. To mitigate classification bias from incomplete labeling, entropy-weighted manifold alignment dynamically adjusts sample weights^[70].

4.2.2. Generalization across dynamic operating conditions

Cross-condition diagnosis requires models to learn operating-condition-invariant features. Contrastive ensemble transfer learning uses stochastic contrastive regularization^[71]. Concurrently, the cross-machine deep subdomain adaptation network demonstrates superior performance under variable-speed conditions based on the measurement of subdomain distribution shifts^[72].

To address sample scarcity, the conditional VAE-GAN synthesizes fault data for missing operating conditions^[73]. Furthermore, methods such as the class-imbalance-aware deep adversarial adaptation network and semi-supervised weighted pseudo-labeling effectively resolve the concurrent engineering challenges of condition shifts and class imbalance. They achieve this by optimizing the imbalanced feature space and introducing pseudo-labeling mechanisms, respectively^[74,75].

At the level of large-scale deployment, cross-wind-farm transfer must bridge the generalization gap induced by geographic and microclimate disparities. Research has consequently shifted from single-turbine diagnosis to wind-farm-level modeling. For instance, through structural optimization, models such as the multi-layer bidirectional LSTM network achieve unified diagnosis for multi-class blade faults across an entire wind farm^[76]. Furthermore, integrating the maximum mean discrepancy algorithm into the alignment of SCADA spatio-temporal features has significantly enhanced cross-turbine generalization capabilities^[77].

Simultaneously, to address sample scarcity and limited computational resources for individual turbines, researchers have adopted a micro-transfer learning paradigm. This approach utilizes pre-trained CNNs to extract universal feature weights, followed by parameter fine-tuning exclusively on specific layers. By retaining the generalized knowledge from the source domain while enabling rapid adaptation to specific entities, this strategy provides a highly flexible pathway for addressing the complex and highly variable main shaft bearing faults across different wind farms^[78].

4.2.3. Physics-informed enhancement and frontier technology integration

Traditional transfer learning can overlook physical consistency across wind farms. Physics-informed machine learning offers a general route for combining data with governing knowledge^[79], while non-stationary wind-turbine diagnosis can also exploit physically meaningful, speed-varying fault-frequency trajectories through generalized demodulation^[80]. These studies demonstrate that physics-informed enhancement can reduce reliance on purely statistical correlations, although its effectiveness remains dependent on the accuracy and cross-turbine validity of the embedded physical priors.

With the widespread adoption of UAV-based visual inspection, adapting diagnostic models to rare or previously unseen blade defects has become increasingly important. Knowledge-augmented vision-language models combine drone-captured blade images with retrieved defect descriptions and expert knowledge, enabling data-efficient and interpretable classification, localization, and severity assessment for wind-turbine blade inspection^[81].

Furthermore, conventional transfer models rely heavily on feature correlations and often lack logical explanations for fault root causes. Transfer strategies incorporating causal inference aim to disentangle spurious correlations induced by environmental fluctuations from intrinsic causalities driven by component failures. Moving forward, interpretable transfer learning will not merely alert O&M personnel to a fault, but will also transparently explain why knowledge learned under one operating condition remains applicable under an unseen condition^[82]. This paradigm shift will fundamentally enhance the trustworthiness of automated decision-making within expert systems.

4.3.1. Continual learning paradigms to mitigate catastrophic forgetting

A fundamental challenge in deploying data-driven models for wind turbine health management lies in continuously adapting to novel fault patterns without erasing previously acquired knowledge-a phenomenon widely recognized as catastrophic forgetting^[84]. To address this challenge, the research community has proposed various incremental and lifelong learning frameworks, aiming to strike an optimal balance between stability and plasticity through diverse architectural designs and algorithmic mechanisms.

Among these, a representative paradigm leverages pseudo-labeling mechanisms to filter high-confidence samples from newly emerging unlabeled data streams. This approach augments the training set, thereby achieving adaptive updates with minimal manual annotation costs. For instance, uncertainty-aware pseudo-label selection can incorporate confident unlabeled bearing samples into semi-supervised transfer diagnosis under data imbalance^[85]. While this strategy is attractive in annotation-scarce scenarios, its efficacy depends on probability calibration, semi-supervised learning assumptions, and distinguishing aleatoric from epistemic uncertainty^[86-88].

Another category of approaches adheres to the philosophy of dynamic architectural expansion. By incrementally appending new branches to the network to accommodate novel fault classes, these methods explicitly preserve previously acquired knowledge at the structural level. Notable examples include the partial matching segmented CNN incremental learning framework, which continually extracts new fault features from data streams while maintaining a diagnostic accuracy exceeding 98% under varying operating conditions^[89]. Similarly, inverted transformer-based lifelong learning can add and prune branches for emerging fault classes, while regularization-based continual learning provides a complementary mechanism for retaining earlier knowledge^[90,91].

Although this branch-expansion strategy is intuitive for retaining historical knowledge and achieves exceptional accuracy in diagnosing unknown faults^[92], it comes with an inherent trade-off: the model architecture inevitably bloats as the number of tasks increases. Consequently, this raises significant concerns regarding computational and storage efficiency during long-term deployment, particularly on resource-constrained edge devices.

To mitigate these inefficiencies, exemplar-free replay approaches eliminate the need to store historical data samples. The adaptive prototype correction and separation network exemplifies this trajectory. It utilizes optimal transport theory to calibrate historical class prototypes within a newly updated feature space. By integrating contrastive learning to enhance feature separability, this method achieves exceptional diagnostic accuracies of 99.01% and 97.36% on benchmark datasets, all without retaining any legacy samples^[93]. Although this strategy alleviates data privacy concerns and storage burdens, it shifts the engineering challenge to maintaining the long-term robustness and drift resistance of these prototype representations.

Furthermore, a subset of studies integrates transfer learning principles into incremental frameworks, leveraging historical experience to accelerate the learning process. For instance, the trend-constrained incremental transfer prognosis approach uses trend constraints to guide prognostic updates under distribution drift, enhancing both the stability and efficiency of incremental predictions^[94].

Overall, spanning from pseudo-label augmentation and architectural expansion to prototype correction and transfer acceleration, these frameworks collectively constitute a multi-dimensional toolbox for overcoming catastrophic forgetting. However, practical constraints often limit their efficacy, indicating the absence of a universally optimal solution. Consequently, the selected strategy must be precisely tailored to the unique demands of the deployment scenario.

4.3.2. Online learning and dynamic model updating

As data-driven models transition into online deployment, they must handle continuous data streams, distribution drift, and time-varying noise. Practical feasibility therefore depends on concept-drift adaptation and real-time noise-robust updating^[95,96]. Under such scenarios, the theoretical capabilities of incremental learning must be operationalized into executable dynamic updating mechanisms, thereby sustaining the timeliness and precision of the prognostic outcomes.

To reduce the computational burden of large-scale industrial data streams, a key strategy is intelligent sampling, which extracts a representative subset from event logs by quantifying the significance of data trajectories. For instance, when a sampling-based next-event prediction method is applied to wind turbine maintenance logs in conjunction with an LSTM network, utilizing a 30% sampling ratio yields a 3.631-fold improvement in prediction efficiency and a concurrent 6.896% increase in accuracy^[97]. However, sampling strategies inherently carry the risk of information loss or the introduction of statistical bias. Consequently, researchers must carefully balance gains in processing speed with the preservation of information fidelity.

Simultaneously, edge-based incremental learning can support online evolution by updating models directly on inference devices. Because wind-turbine data are affected by sensor noise and uncertainty, online updating should also incorporate noise-adaptation capabilities.

Ultimately, the engineering realization of lifelong online capabilities hinges on the synergy of three pivotal components: efficient data stream management, edge-based incremental update protocols, and embedded online filtering with noise adaptation mechanisms. Collectively, these elements forge the technological bedrock required to support autonomous, real-time adaptation.

4.3.3. Meta-learning and RL-driven adaptive strategy optimization

In contrast to passive, reactive updating in response to continuous data streams, the research frontier increasingly leverages meta-learning and reinforcement learning (RL) to shift adaptation from passive updating to proactive optimization. This advancement gives models the intrinsic ability to autonomously optimize their learning strategies and decision-making processes, enabling them to navigate dynamic environments with unprecedented intelligence. Ultimately, this paradigm shift aims to alleviate the heavy reliance on manual task design and meticulous policy tuning typically required in complex, multi-objective scenarios.

The profound value of meta-learning lies in its capacity to automate the learning process itself. For instance, by automatically generating auxiliary task labels via a meta-auxiliary generation network, supplementary degradation information can be provided for RUL estimation, thereby augmenting the primary task performance^[98]. Concurrently, to address the prevalent issue of inaccurate labels in real-world data, certain meta-learning approaches dynamically aggregate online soft labels based on cross-task feature similarities. This mechanism guides model training and substantially elevates diagnostic accuracy within few-shot and highly noisy environments^[99].

While such methodologies significantly curtail manual engineering efforts and bolster robustness, their efficacy remains highly contingent upon the quality of the initial meta-knowledge. Furthermore, continuously updating the meta-learner introduces additional computational overhead.

In contrast to meta-learning’s emphasis on learning to learn^[100], RL provides a direct pathway for optimizing high-level O&M policies. RL is capable of achieving multi-objective optimization through dynamic trade-offs - for example, simultaneously managing the risk of blade fatigue failure while minimizing both maintenance costs and carbon emissions^[101].

Furthermore, when integrated with formal modeling tools such as Petri nets, RL can navigate complex state-action spaces to learn optimal condition-based maintenance policies. This synergy can yield exceptional outcomes, such as attaining a 99.4% system availability alongside minimized operational expenditures^[102]. However, effective RL application often relies on high-fidelity environmental simulations and complex reward-function engineering. Moreover, its inherently low sample efficiency can pose a significant barrier to deployment within safety-critical real-time systems.

In essence, the integration of meta-learning and RL signifies a profound leap in wind turbine diagnostics: transitioning from purely data-driven paradigms to cognitive-driven intelligence. By endowing models with the learning-to-learn capability, meta-learning facilitates rapid adaptation; conversely, RL optimizes O&M policies through the simulation of sequential decision-making processes. This evolution empowers the system to transition from passive data reception to proactive environmental adaptation, ultimately striking an optimal trade-off between diagnostic reliability and operational expenditures under highly uncertain operating conditions.

4.3.4. Adaptive model architectures and feature learning

Beyond external learning strategies, the resilience and adaptability of a model within highly dynamic and complex environments depend fundamentally on the intrinsic capacity of its underlying architecture to encode and process spatio-temporal dynamics and physical constraints^[103-105]. This perspective underscores the need to move beyond merely analyzing how a model learns to investigate the endogenous robustness of its architectural design. Ultimately, such intrinsic structural integrity endows the model with an inherent cross-domain generalization capability when confronting complex and highly variable operating conditions.

A primary architectural innovation involves explicitly modeling the relational structures within multi-sensor systems. For instance, the recurrent graph convolutional network with uncertainty estimation (RGCNU) learns spatial and temporal dependencies in condition-monitoring data. Its uncertainty model assumes Gaussian-distributed RUL predictions and estimates a predictive mean and variance using a likelihood-based loss, rather than quantile regression^[106]. However, the efficacy of graph-based models is heavily reliant on the accuracy of the constructed graph topology, a task that proves exceedingly difficult in highly non-stationary scenarios.

Complementary to purely data-driven relational modeling is anchoring the representation space by integrating domain-specific physical knowledge. Physics-informed architectures, for example, can combine physics-based fatigue and grease-degradation kernels with data-driven neural networks, improving physical consistency and uncertainty-aware long-term degradation estimation for wind turbine main bearings^[107]. More broadly, physics-informed machine learning combines observational data with scientific knowledge to improve consistency and data efficiency, but its reliability still depends on the validity of the embedded assumptions^[108]. Nevertheless, the effectiveness of such methodologies is inherently bounded by the accuracy and completeness of the integrated physical models, which may ultimately struggle to encompass the full spectrum of potential failure modes.

To achieve broad robustness without relying on specific physical laws, fusion-based ensemble learning frameworks provide an alternative architectural pathway. By constructing hybrid models that encompass diverse algorithmic components coupled with feature optimization, this class of methods demonstrates exceptional generalization capabilities across varying operating conditions. They can achieve high R² values while significantly reducing prediction errors^[109]. The associated cost, however, is a surge in model complexity. Furthermore, when compared to concise single models, these ensemble approaches often confront substantial challenges regarding interpretability and deployment maintenance.

Consequently, spanning from graph relational modeling and physics-constraint fusion to robust ensemble architectures, these advancements collectively constitute the adaptive infrastructure at the structural level of the model. Ultimately, this infrastructure endows the system with the indispensable capabilities for perception, representation, and reasoning within the dynamic, complex environments of real-world wind turbines^[110].

4.3.5. Adaptive thresholds and reliable decision-making under concept drift

The ultimate operationalization of a model’s adaptive capabilities manifests in its decision boundaries and alarm mechanisms: adaptive thresholds and concept drift detection constitute the ultimate safeguard for ensuring decision reliability. The core paradigm shift lies in transitioning from static, predefined thresholds to dynamically adjusted mechanisms that evolve concurrently with model confidence and data distributions, thereby effectively mitigating both false alarms and missed detections.

Probabilistic forecasting frameworks provide critical structural support for this transition. Through rigorous uncertainty quantification, decision-making can be translated into risk-informed scheduling based on confidence intervals. For instance, an RUL prediction model for gearbox pump failures, underpinned by a Bayesian neural network, reported that the true failure time fell within a range of approximately ±5.3 h of the predicted value with a 97.5% confidence level. Statistically, this furnishes a robust foundation for maintenance scheduling^[111]. Crucially, these probabilistic outputs can be directly used to formulate and continuously adjust dynamic alarm thresholds.

Furthermore, specific research endeavors optimize the decision-making process for hard-to-classify samples: misclassifications are most prone to occur when a sample’s probabilities of belonging to the positive and negative classes are nearly equal. To address this vulnerability, purpose-built SVM ensemble models are deployed to identify and improve the classification accuracy of these ambiguous instances, thereby substantially bolstering overall decision reliability^[112].

To sustain long-term reliability amidst slowly drifting operational baselines, a possible design is to combine normal behavior models with residual-based alarm thresholds. Threshold selection and recalibration should be evaluated against false-alarm rates and fault-detection delays under the intended operating conditions; no single residual quantile should be treated as universally appropriate.

However, unconstrained threshold adaptation may absorb slowly evolving fault signatures into the updated normal baseline, thereby delaying alarms for genuine component degradation. To distinguish benign environmental or operational drift from irreversible health deterioration, current methods increasingly employ condition-conditioned normal behavior models and dual-timescale updating mechanisms: fast adaptation compensates for variations in wind speed, load, ambient temperature, and seasonal conditions, whereas a slowly updated or fixed reference baseline preserves long-term degradation information. Threshold recalibration can also be gated by residual persistence, monotonic degradation indicators, change-point detection, and physics-informed health constraints, and should be suspended when deviations exhibit sustained, component-specific, or irreversible patterns. In safety-critical applications, adaptive thresholds should therefore be combined with conservative alarm boundaries and independent degradation detectors to prevent baseline updating from masking incipient faults.

These adaptive detection mechanisms underscore the industry’s shift toward more resilient and autonomous diagnostic systems. However, the diversity of operational environments and data characteristics necessitates a broader evaluation of available methodologies beyond individual frameworks. From a practical standpoint, the evaluation of such adaptive detection schemes should therefore go beyond isolated accuracy metrics and incorporate robustness across diverse wind regimes, alarm latency, false-alarm rates, and the ability to preserve detection sensitivity under gradual degradation.

Table 4 provides a comprehensive summary of advanced learning strategies for wind turbine predictive maintenance. This encompasses representative methodologies across various technological paradigms, their core advantages, and their practical engineering efficacy. The table not only encapsulates the applicable scenarios and performance metrics of these diverse methods under real-world operating conditions but also serves as a critical reference guide for selecting the most appropriate strategy to achieve highly efficient prognostics and decision-making.

A horizontal comparison indicates that no learning paradigm is uniformly superior, because its effectiveness depends on how well its underlying assumptions match the dominant characteristics of the available data. Incremental and online learning suit continuously evolving data streams because they emphasize knowledge updating and temporal adaptability, though they remain vulnerable to forgetting and error accumulation. Meta-learning prioritizes rapid few-shot adaptation by extracting transferable learning strategies from related tasks, whereas RL operates mainly at the decision level by optimizing sequential trade-offs among safety, maintenance cost, and operational availability. Graph-based and physics-informed architectures improve structural representation and physical consistency, but their performance depends on reliable sensor topology or prior physical knowledge. Therefore, algorithm selection should be determined by the dominant data limitation, temporal evolution pattern, and engineering objective rather than by isolated numerical performance indicators.

By integrating uncertainty-aware probabilistic outputs, targeted processing of ambiguous decision samples, and drift-resistant dynamic thresholding mechanisms, the adaptive decision-making framework can significantly reduce both false alarms and missed detections. Consequently, this comprehensive system robustly guarantees the engineering reliability of predictive maintenance systems operating within highly non-stationary environments.

5.2.1. Adaptive feature engineering at the edge

Processing high-frequency raw signals on resource-constrained edge devices can incur prohibitive latency. Edge feature engineering therefore aims to reduce computational and communication loads while retaining fault-sensitive information. Accordingly, the effectiveness of edge feature engineering should be evaluated not only by dimensionality reduction ratio, but also by its influence on diagnostic accuracy, inference latency, memory usage, and communication overhead.

Adaptive feature dimensionality reduction techniques are extensively deployed to alleviate severe communication pressure. Tailored for offshore wind power scenarios, recent studies propose executing primary feature extraction and selective screening directly at the edge gateway, thereby transmitting only the compact feature representations to the cloud. Edge-cloud collaboration can reduce raw-data transmission by moving preliminary processing closer to the data source, although the achieved bandwidth reduction depends on the task and deployment design^[127]. Complementary SCADA-based studies have combined fault detection with sensor or feature selection, showing that redundant variables can be reduced while retaining information relevant to turbine condition monitoring^[128].

Feature selection can be formulated as a multi-objective optimization problem involving a search for Pareto-efficient trade-offs between classification performance and feature-set size^[129,130]. For edge diagnosis, the selected solution must additionally be evaluated against latency and energy constraints. Moreover, the selected feature subset should remain sufficiently stable across varying wind speeds, loads, and operating regimes to avoid frequent feature reconfiguration during online deployment.

To this end, Han et al. proposed a multi-objective particle swarm optimization algorithm with adaptive strategies for feature selection^[131]. By jointly optimizing classification performance and feature-subset size, the method provides a relevant optimization basis for resource-constrained edge diagnosis. However, the effectiveness of edge-side feature engineering depends on reliable operating-state recognition and accurate estimation of device resource availability. Excessive dimensionality reduction may discard weak incipient-fault signatures, while frequent switching between feature subsets can add scheduling overhead and temporal inconsistency. Therefore, feature selection should be jointly optimized with lightweight model architectures to balance diagnostic accuracy, latency, communication cost, and energy consumption across the entire edge pipeline. Feature-level optimization alone, however, cannot fully satisfy the computational and memory constraints of embedded edge platforms, making model-level lightweight design and compression equally important.

5.2.2. Efficient architectures and model compression

Beyond data-level optimizations, the intrinsic lightweight design of deep learning models themselves is paramount for actualizing edge deployment. To rigorously evaluate the applicability of diverse models in resource-constrained environments, the academic community primarily focuses on the trade-off between model complexity and diagnostic performance.

As depicted in Figure 8, HRC-NASNet-b and WTBMobileNet occupy the low-complexity region and provide favorable accuracy-efficiency trade-offs. Their reported accuracies are lower than those of VGG-16 and MCWT-WCFormer in the figure; therefore, they should not be described as surpassing the heavyweight baselines. MCWT-WCFormer achieves the highest accuracy among the compared models but at substantially greater computational cost than the compact architectures, illustrating the performance-efficiency trade-off relevant to edge deployment. The technical pathways for lightweight wind turbine fault diagnosis can be broadly classified into two categories: (1) the design of inherently lightweight and domain-specific network architectures; and (2) the compression and hardware-aware optimization of existing high-performance models through structured pruning, knowledge distillation, and neural architecture search (NAS).

Figure 8. Accuracy–complexity trade-off of lightweight models and baselines. GFLOPs: Giga floating-point operations.

These lightweight strategies improve deployment efficiency through fundamentally different mechanisms. Purpose-built lightweight networks reduce computational complexity through compact architectural design and therefore provide relatively predictable inference latency, but their limited representational capacity may limit recognition of weak or compound faults. Structured pruning exploits redundant channels, filters, or network blocks in over-parameterized models, although excessive pruning may remove fault-sensitive pathways and degrade diagnostic accuracy. Knowledge distillation transfers the soft decision relationships learned by a high-capacity teacher model to a compact student model and may preserve more diagnostic information than direct pruning, but its effectiveness depends strongly on teacher quality and teacher–student compatibility. Hardware-aware NAS explicitly incorporates latency, memory, and energy constraints into the optimization objective, enabling device-specific accuracy–efficiency trade-offs, although its high search cost and limited cross-platform portability constrain large-scale reuse.

For the first category, researchers have developed various domain-specific lightweight network architectures tailored to the distinct characteristics of wind turbine vibration signals. For instance, a customized lightweight one-dimensional CNN designed exclusively for real-time gearbox monitoring introduces asymmetric convolution kernels to reduce redundant computations. This architecture significantly reduces inference latency while maintaining a high recognition accuracy of 94.04%^[132].

Furthermore, in visual inspection, the WTBMobileNet architecture has been deployed for wind turbine blade surface defect recognition. Compared to the baseline network, this model achieves a 9.4-fold reduction in parameter count and a 2.7-fold decrease in computational overhead, accompanied by a marginal accuracy degradation of merely 1.68%. Such exemplary performance unequivocally demonstrates its exceptional engineering practicability^[133].

The second category focuses on compressing and optimizing existing high-performance yet over-parameterized models. Structured pruning systematically eliminates redundant convolutional channels, filters, or network blocks, thereby reducing computational overhead and facilitating efficient deployment on hardware acceleration platforms. For instance, in the Single Shot MultiBox Detector framework, replacing the VGG-16 backbone with ShuffleNetV2, coupled with a rigorous channel-pruning strategy, significantly increases the inference frame rate on embedded devices^[134].

Concurrently, knowledge distillation paradigms compel lightweight edge models to mimic the output distributions of cumbersome cloud-based models. This mechanism ensures compact networks inherit the robust generalization of complex models while maintaining a remarkably low parameter count, further optimizing the performance-efficiency trade-off shown in Figure 8.

To further dismantle the barrier between algorithmic design and hardware deployment, cutting-edge research has actively pivoted toward hardware-aware NAS. Departing from the conventional paradigm that treats model design as an isolated algorithmic endeavor, this technology explicitly integrates the latency, energy consumption, and memory footprint of the target hardware as strict constraints within the neural network’s search space^[135].

As a distinct architecture-design approach, MCWT-WCFormer combines wavelet-based multi-sensor processing with a CNN–Transformer network and reports approximately 4.2 ms for single-sample diagnosis under its experimental configuration^[136]. This result should not be attributed to NAS or treated as evidence of equivalent latency on an untested edge device. To complement the accuracy–complexity relationship illustrated in Figure 8, Table 6 provides a quantitative comparison of lightweight fault diagnosis models in terms of diagnostic performance and computational complexity.

These results indicate that lightweight performance should be evaluated jointly by accuracy, parameter count, computational operations, and execution time. Input compression and compact CNN design provide substantial efficiency gains, whereas feature-fusion and transfer-learning models generally introduce additional computation in exchange for improved robustness and cross-condition generalization.

Overall, lightweight edge deployment is achieved primarily through two complementary pathways: designing inherently efficient network architectures and compressing or hardware-aware optimizing existing high-performance models. In practice, selecting these strategies should consider diagnostic accuracy, inference latency, memory footprint, energy consumption, and compatibility with the target hardware platform^[140].

5.3.1. Post-hoc interpretability in black-box diagnostics

To bridge the critical chasm between high-performance black-box models and actionable human decisions in wind power O&M, post-hoc explainability methods have emerged as indispensable tools. Their fundamental objective is to deconstruct the model’s decision-making logic by rigorously quantifying feature contributions.

Within the complex, multi-variable coupled environment of wind turbine monitoring, model outputs typically depend on a vast array of interconnected operational parameters. Consequently, merely predicting an impending fault is insufficient to build trust among on-site engineers or formulate concrete intervention strategies^[141]. Methods such as Shapley additive explanations (SHAP) and the sensitivity analysis-based differential importance measure (DIM) address this challenge by assigning quantifiable impact scores to individual input features. In doing so, they translate inherently opaque predictive outputs into auditable, evidence-based diagnostic rationales^[142].

SHAP analysis is utilized not only to identify novel, highly interpretable variable sets associated with rotor and pitch control for anomaly detection^[143], but also to dissect the deep correlations between inputs and hidden layer outputs within prognostic models. This facilitates localizing and eliminating low-contribution features, thereby optimizing sensor configuration and deployment strategies^[144]. Concurrently, the DIM quantifies the relative contribution of individual parameters or parameter groups to changes in a risk metric, thereby supporting sensitivity-based prioritization in risk-informed decision-making^[145].

The main advantage of these methodologies is twofold: they can precisely pinpoint critical fault indicators^[146] and, on the other hand, facilitate root cause localization and false alarm suppression by distinguishing anomalies caused by component degradation from deviations induced by external operating conditions.

Furthermore, such quantifiable interpretations can be directly integrated into decision support systems. By generating diagnostic rules based on a minimal set of relevant features, this approach provides acceptable explanations for anomalies and achieves detection accuracies up to 80%, thereby accelerating and standardizing O&M responses. Additionally, the underlying logical relationships unveiled by Shapley value analysis can be distilled into intuitive, comprehensible operational state indicators. This establishes a direct bridge between the model’s inferential logic and empirical judgments made by site engineers on system behavior^[147].

Consequently, by systematically attributing model decisions to specific, measurable input variables, post-hoc explainability methods provide the requisite foundation of trust for the verification, comprehension, and execution of AI-driven diagnostics in high-risk maintenance scenarios.

5.3.2. Inherent interpretability and uncertainty estimation

Intrinsic interpretability depends on whether a model has an understandable structure and whether its explanations faithfully represent the prediction process. Attention mechanisms can expose feature-weighting patterns for inspection, but attention weights alone do not establish a transparent or causally valid diagnostic pathway.

For instance, a cosine-enhanced channel attention mechanism has been used to learn and accentuate effective features with explanatory value while suppressing noise interference^[148]. Meanwhile, parameter-free attention modules can integrate formalized prior knowledge of outlier types into CNNs. This achieves a seamless fusion of domain knowledge and data-driven learning, supporting feature selection and diagnostic interpretation^[149]. Pushing architectural innovation further, recent approaches constrain standard dot-product attention using learnable adaptive Gaussian mixture models. By explicitly targeting the extraction of local sparse features and mitigating sensitivity to irrelevant information, these methods provide additional structure for inspecting feature extraction, without by themselves guaranteeing faithful explanations^[150].

Beyond architectural innovations, feature visualization techniques translate internal model representations into human-comprehensible formats, serving as a visual bridge between complex data manifolds and engineering intuition.

A prominent approach employs a supervised VAE to project high-dimensional wind turbine states into a lower-dimensional latent space. This generates 2D trajectory maps that visualize health degradation processes and facilitate early anomaly detection, with studies demonstrating the capability to identify main bearing over-temperature signatures up to 11 days prior to control system alarms^[151]. Similarly, dimensionality reduction and visualization techniques, such as t-distributed stochastic neighbor embedding, have been integrated into advanced diagnostic frameworks like the long-term feature memory network and deformable spatio-temporal fusion network (DSTF-Net). These tools intuitively illustrate the distribution of fault features learned from complex insulated bearing data. These visualizations can illustrate separation within the learned feature space, but do not establish physical validity, causal interpretation, or calibrated confidence. Corresponding studies report accuracy enhancements ranging from 4.2% to 29.7%^[152,153].

Attention-assisted inspection and feature visualization can help analysts examine learned representations. Their explanatory value should nevertheless be assessed separately from diagnostic accuracy, using appropriate checks of explanation fidelity, stability, and physical plausibility.

5.3.3. Bayesian and probabilistic deep learning frameworks

Beyond achieving decision explainability, establishing genuine engineering trust necessitates a transition from qualitative comprehension to a quantitative foundation-specifically, the explicit quantification of predictive uncertainty inherent in data-driven models to transform heuristic forecasts into calibrated risk metrics. Central to this pivotal transition is the deployment of probabilistic and Bayesian deep learning frameworks. Rather than yielding singular point estimates, these architectures output a probability distribution of potential outcomes, thereby formally articulating the confidence of the model in its internal inferences.

For instance, uncertainty-aware Bayesian optimization applied to extreme recurrent networks explicitly integrates predictive uncertainty into the objective function to be minimized. This approach yields superior generalization compared to deterministic counterparts, with the coefficient of determination improving by 5.14% ± 2.13%^[154]. Furthermore, the synergy of parallel gated recurrent units, kernel density estimation, and Monte Carlo dropout facilitates the generation of probabilistic RUL estimates. This ensemble preserves high diagnostic precision while simultaneously delineating reliable uncertainty bounds^[155]. The selection of probabilistic models also profoundly impacts performance; in early-stage fault detection, Gaussian mixture copula models have demonstrated superiority over alternative probabilistic power curve paradigms, accelerating detection speed by a factor of 10-30^[156].

Jing and Zhao developed an adjustable piecewise multivariable regression strategy that groups operating-data bins with similar nonlinear characteristics and fits Gaussian process regression submodels within the resulting clusters. The associated probabilistic monitoring strategy uses the predictive distribution to quantify condition uncertainty and reports a wind-turbine abnormal-condition detection accuracy of 95.35%^[157].

Furthermore, incorporating temporal dependencies and prior knowledge can substantially enrich such point-wise uncertainty estimation. For instance, a Bayesian recurrent state estimator, when integrated with historical degradation trends for robust RUL prediction, can reduce prediction errors by over 16%^[158]. Similarly, variational deep Gaussian processes strengthen uncertainty representation within multi-layer architectures while concurrently identifying low-contribution features to optimize sensor deployment strategies.

Providing robust theoretical underpinning, Bayesian networks and related probabilistic graphical models have been systematically reviewed as powerful tools for characterizing uncertainty and complex dependencies, directly addressing both operational and failure uncertainties within wind energy systems^[159]. Advancing this paradigm, sophisticated hybrid models, such as D-vine copula Bayesian networks, model fault correlations across multi-channel condition monitoring data. This enables the rigorous quantification of component risk probabilities and maintenance priorities, successfully overcoming the simplistic assumption of mutually independent inputs^[160].

Ultimately, upon completing the uncertainty modeling via Bayesian or probabilistic deep learning, the practical viability of the output of the model is inherently contingent upon whether the confidence levels can be adequately calibrated, and whether the decision-making layer can leverage this probabilistic information to execute risk-aware maintenance scheduling.

Ultimately, whether by embedding Gaussian processes into network output layers to formulate health-aware distributions^[161], leveraging non-Gaussian deep state-space models equipped with planar flows to relax rigid distributional assumptions^[162], or adopting the broader spectrum of probabilistic methodologies prevalent in statistical fault detection-such as probabilistic principal component analysis and probabilistic partial least squares-uncertainty quantification unequivocally establishes itself as the core quantitative pillar^[163]. It underpins trustworthy, risk-aware decision-making in the field, thereby propelling diagnostic systems beyond mere interpretability toward intrinsic credibility.

5.3.4. Confidence calibration and decision fusion

Building on uncertainty quantification, the engineering reliability of predictive outputs hinges not merely on generating probabilistic estimates, but on whether these probabilities are well calibrated and translated into actionable decisions through strategic fusion. This is paramount for managing missed detections and false alarms in dynamic environments. In practical windfarm deployment, miscalibrated model confidence often leads to two harmful practical outcomes: overconfident alarms for normal operational fluctuations and missed alerts for incipient, lowamplitude component degradation. Furthermore, the inherent volatility of wind turbine operations, particularly across diverse operating conditions, often induces discrepancies between model confidence and actual accuracy, thereby necessitating specialized calibration techniques for realignment.

For instance, to address fluctuating reconstruction errors under varying operating conditions, Xiao et al. proposed the SME index for multi-condition fault detection, making anomaly evaluation more consistent and comparable than evaluation based on raw reconstruction loss^[164]. Furthermore, to prevent regression models-such as power curves-from being contaminated by anomalous data, Zhang et al. introduced a risk-regularized optimization method formulated as a chance-constrained problem. This approach effectively filters outliers while retaining normal data with high probability, eliminating the need for external reference datasets or extensive hyperparameter tuning^[165].

Beyond single-model calibration, uncertainty-aware models can further elevate diagnostic credibility. Amin et al. developed a Bayesian CNN for wind turbine gearbox diagnosis that flags unfamiliar inputs through elevated predictive uncertainty, reducing the risk of overconfident misclassification^[166]. Complementarily, Pandit and Infield constructed Gaussian-process operational curves that provide both mean estimates and 95% confidence intervals for wind turbine condition monitoring^[167]. For early fault diagnosis, Zhao proposed a threshold-free strategy that leverages the probability density function of estimation errors alongside self-organizing neural networks to cluster fault features, yielding a diagnostic accuracy exceeding 90% for early pitch system faults^[168]. Taken together, these approaches indicate that reliable wind-turbine diagnosis requires not only accurate predictions, but also calibrated confidence estimates and uncertainty-aware decision thresholds.

To further delineate the application boundaries and performance trade-offs of the aforementioned trustworthy diagnostic technologies in engineering practice, Table 7 provides a systematic taxonomy. It categorizes these approaches across key dimensions: representative techniques, core advantages, inherent limitations, and typical application scenarios within the wind power sector. A comparative analysis reveals a pronounced trade-off between the depth of interpretation and computational overhead across different methodologies. Consequently, this necessitates that O&M decision-making systems flexibly select or fuse appropriate technological pathways contingent upon specific monitoring targets and real-time requirements.

In summary, through error metric calibration, risk-regularized learning, and UQ-driven decision fusion, these methodologies translate raw probabilistic outputs into robust, actionable O&M insights, thereby closing the critical loop between predictive uncertainty and reliable maintenance actions.

5.3.5. Integrating physics-based models with counterfactual analysis

The ultimate objective of trustworthy condition monitoring extends beyond merely identifying statistical correlations; it lies in unraveling the underlying causal mechanisms of faults. This is achieved by coupling data-driven pattern discovery with physical laws to provide interpretations with profound intrinsic insights. Although advanced data-driven models excel at detecting anomalies and associating them with specific signal patterns, their diagnostics frequently remain at the correlational level, making them susceptible to confounding by variations in external operating conditions. To overcome the limitation that statistical associations in purely data-driven methods lack physical causal constraints, a vital research trajectory involves incorporating explicit physical models into the diagnostic framework. For instance, addressing the ambiguity of vibration signatures in planetary gearboxes, research has formulated a novel transfer path effect model based on modulation phenomena. This physical model can generate interpretable phenomenological signals for both healthy and faulty states, thereby furnishing a priori guidance and causal-mechanistic explanatory bases for monitoring decisions, transcending the diagnostic logic of pure data correlation^[174].

In parallel, a complementary class of data-driven strategies attempts to distinguish influential associations within learned models by analyzing sensitivity to input perturbations. Model-agnostic explanation methods can quantify how changes in input variables influence a learned prediction^[175]. Such perturbation-based explanations can identify influential variables, but they should not be interpreted as causal effects without additional causal assumptions or interventions^[176].

Physics-based interpretation requires substantial domain knowledge and may not encompass unmodeled dynamics. In contrast, perturbation-based explanations remain dependent on the accuracy and completeness of the underlying detection model and do not provide the mechanistic depth of first-principles models. Overall, these two pathways-one embedding physical causality into the model and the other extracting explanatory signals from model behavior-are complementary. Together, they support more credible O&M decisions while clarifying the limits of causal interpretation.

5.4.1. Integrated management spanning the full lifecycle and multi-component systems

A well-established standardized O&M framework must transcend reactive response modes, instituting a full-lifecycle management paradigm-from design through decommissioning-to enhance system sustainability and efficiency^[177]. This macroscopic philosophy must be concretely implemented at the microscopic component level. For instance, addressing bearing lubrication failures requires constructing a comprehensive scheme that integrates failure analysis, tribological mechanisms, and intelligent lubrication^[178-180]. For generators, AI technologies have already permeated the entire process spanning their design and maintenance^[181]. Meanwhile, for offshore wind support structures, harsh operating conditions impose more stringent demands on monitoring robustness^[182]. However, this architecture, fusing temporal dimensions with component depth, significantly amplifies system complexity, necessitating the capability to seamlessly integrate heterogeneous domain knowledge and multi-source data. Ultimately, only by achieving a deep synergy between full-lifecycle tracking and critical component management can O&M frameworks truly fulfill the dual demands for reliability and economic viability in modern wind power systems.

5.4.2. Construction and advanced optimization of O&M decision support systems

The core of a standardized O&M framework lies in its decision support system. This system is no longer confined to simplistic condition alarms; rather, it aims to integrate front-end equipment health prognostics, mid-end maintenance strategy optimization, and back-end resource scheduling into a unified decision engine. The paramount challenge for the decision support system (DSS) resides in outputting O&M strategies that are optimal in terms of both cost and risk, amidst a multitude of compounding uncertainties-including model prediction errors, economic parameter fluctuations, logistical constraints, and the inherent stochasticity of component degradation. Architecturally, a DSS typically encompasses two tightly coupled modules: health prognostics and maintenance decision-making^[183]. Its transition from theoretical constructs to practical engineering applications fundamentally represents an algorithmic evolution from deterministic planning toward explicit uncertainty modeling.

Different O&M optimization methods exhibit distinct advantages because they represent uncertainty, conflicting objectives, and sequential decisions in fundamentally different ways. Evolutionary multi-objective optimization can balance production losses and maintenance costs, robust optimization emphasizes protection against uncertain degradation predictions, and reinforcement-learning-based methods are more appropriate for high-dimensional dynamic scheduling but depend strongly on simulator fidelity and reward design. Table 8 systematically summarizes the key algorithms embedded within the DSS and the specific O&M pain points they target.

The engineering value of integrating these algorithms has been corroborated by multiple empirical studies. Simulation results indicate that advanced DSS frameworks incorporating semi-Markov models and hierarchical failure analysis offer substantial economic advantages over traditional strategies^[189,190]. For example, a reference model fusing preventive and predictive maintenance demonstrates a potential reduction in overall maintenance costs by up to 32%^[191]. Concurrently, quantitative evaluation methods tailored for short-term cost-effectiveness further validate the economic rationality of such decision outputs. Ultimately, by standardizing the encapsulation of advanced algorithms-such as evolutionary optimization, robust optimization, and deep RL-within the decision kernel, modern DSS effectively transform the inherent uncertainties of wind farm operations into quantifiable and controllable economic risks. This constitutes the critical nexus bridging data and value within the intelligent O&M architecture.

5.5.1. Cloud-edge-physical-layer collaborative architecture and IoT communication foundations

Realizing fleet-level collaborative monitoring fundamentally relies on constructing a highly reliable Internet of Things (IoT) framework and a flexible computational scheduling system. The paradigm shift from single-turbine monitoring to networked synergy depends heavily on a communication infrastructure capable of supporting the seamless convergence of heterogeneous data streams. For instance, in a real-world deployment involving 75 urban turbines, an IoT network comprising 2,300 sensors maintained a high mean time between failures while facilitating deep learning-based wind condition forecasting and system optimization. This implementation yielded substantial benefits, notably a 41% reduction in maintenance costs and a 23.8% increase in annual energy production^[192]. Figure 10 illustrates a typical tri-tier cloud-edge-physical-layer smart O&M architecture along with its internal data flow dynamics. Through hierarchical decoupling, this architecture achieves a structured integration of massive data streams derived from physical sensing with the control streams originating from cloud-based decision-making.

Figure 10. Cloud-edge-physical-layer collaborative O&M architecture for wind turbines. O&M: Operation and maintenance; SCADA: supervisory control and data acquisition.

As illustrated, the core advantage of this architecture lies in the differentiated deployment of functions and closed-loop synergy: the foundational layer focuses on the holographic sensing of multi-source heterogeneous data; acting as the first line of defense for real-time operations, the edge layer undertakes data cleaning and lightweight inference tasks to alleviate uplink bandwidth pressure; meanwhile, the cloud leverages its computational superiority to handle heavy-duty model training and dispatches the generated strategies down to the edge. Based on this hierarchical division of labor, the cloud-edge-physical-layer collaborative architecture resolves edge resource constraints through dynamic task offloading. For offshore wind scenarios, a task-offloading strategy based on multi-agent deep RL can reduce system overhead by approximately 61.8%^[193]. At the same time, this architecture enables a shift from anomaly detection to comprehensive decision support. For instance, a fuzzy inference system utilizing multispectral imagery can output maintenance priority scores highly correlated with expert assessments^[194]. Furthermore, for time-sensitive tasks such as low-voltage ride-through, neural predictive control at the edge achieves millisecond-level responses^[195]. Here, millisecond-level response refers to control-loop inference and actuation latency for time-critical electrical control tasks, rather than the prognostic lead time of mechanical fault warnings, which is generally measured over substantially longer horizons. Theoretical research also underscores that situational awareness, integrating state-space models and AI, is crucial for realizing system-level adaptive decision-making^[196].

5.5.2. Multi-scale spatio-temporal modeling and adaptive diagnostic algorithms

The high fidelity of digital twins relies heavily on algorithms striking an optimal balance between real-time responsiveness and accuracy. For abrupt electrical faults, short-time signal amplitude ripple analysis and the three-stage temporal window mechanism have proven highly effective in extracting fault features^[197,198]. Furthermore, when addressing complex spatio-temporal coupling characteristics, deep learning models demonstrate outstanding performance: a residual LSTM integrated with an attention mechanism achieves exceptionally high precision in critical variable prediction^[199], while a self-attention LSTM attains a 98.67% accuracy rate in yaw system diagnostics^[200].

To address non-stationary operational data, spatio-temporal graph neural networks and interactive spatio-temporal networks enhance anomaly recognition capabilities through topological correlations. Meanwhile, wavelet LSTM networks and the differentiated frequency-spatial-temporal feature extraction with differential entropy method effectively mitigate the interference of signal non-stationarity on early detection. Furthermore, integrating the exponentially weighted moving average and adjacent-state adaptive threshold mechanisms can significantly reduce false alarm rates^[201-205], ensuring the robustness of decision-making. However, regardless of the algorithms’ sophistication, their practical deployment efficacy at the edge is ultimately constrained by input data quality and physical sensing stability^[206-208]. This compels us to shift our focus downward from the algorithmic layer to the physical layer, re-examining sensing-system maintenance and data governance as the foundation of reliable intelligent O&M.

5.5.3. Physical sensing maintenance and data governance foundation

The sustainable operation of edge intelligence hinges on the dual guarantees of physical power supply and data quality. Addressing the energy supply bottlenecks of hard-to-maintain components, electromagnetic and pendulum-based energy harvesting technologies have emerged as critical breakthroughs. Specifically, electromagnetic harvesters inside the blades can output 6 mW of power to drive vibration monitoring^[209], whereas pendulum structures mounted on rotating shafts can achieve an output of 104.5 mW^[210], laying the physical foundation for self-powered wireless sensor networks. At the data level, semi-supervised multivariate models can exploit abundant unlabeled SCADA records together with limited labeled anomalies to improve wind-turbine anomaly detection^[211]. Meanwhile, sequence-driven automatic label calibration methods effectively resolve mislabeling issues^[212]. Simultaneously, to accommodate constrained edge computing power, Autoregressive compression techniques reduce dimensionality while heightening sensitivity to fault features^[213]. Coupled with covariate adjustment strategies^[214], these methods ensure that the data streams fed into the models possess high integrity and adaptability to varying operational conditions. Ultimately, these preprocessing technologies constitute the indispensable data bedrock for the digital twin ecosystem.