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Bearing fault diagnosis suffers from unsatisfied accuracy under variant working conditions, due to class-imbalance and distribution discrepancy of faulty data. Class-imbalance will cause classification bias that makes the diagnostic model tend to misclassify faulty classes, while the distribution discrepancy caused by variant working conditions will further reduce the model's generalization. Thus, this paper is mainly focusing on solving the two challenges simultaneously, and an Iterative Resampling Deep Decoupling Domain Adaptation (IRDDDA) model is proposed. The model consists of a feature extractor, domain classifier, label classifier and feature resampler modules, and it adopts a decoupling two-stage training framework. In the first stage, the model learns domain invariant features from the source domain and target domain based on original class-imbalance datasets. In the second stage, the feature extractor is partially fixed, and the feature-based resampling method is used to adjust the biased classifier caused by the class-imbalance problem. An iterative training strategy is adopted to simultaneously update the upper-layers of the feature extractor and adjust the classifier till convergence. The method is verified on the CWRU open dataset and the dataset collected from our bearing test rig. The verification results show that our method can achieve a better fault diagnostic performance overall, especially in minority faulty classes. • Class-imbalance and distribution discrepancy problem are simultaneously considered. • Deep decoupling domain adaptation framework is proposed. • An iterative feature-based resampling training strategy is adopted. • The method learns transferable invariant features and the calibrated classifier. [ABSTRACT FROM AUTHOR]

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks. • An innovative hierarchical CMFD strategy for wind turbines is proposed which consists of variable-level and turbine-level methods. • At the variable-level, the monitoring and isolated methods are proposed based on SCA and ICA. • At the turbine-level, Bayesian inference is used to integrate variable-level monitoring results, and the root faulty subsystem is deduced based on Gaussian process regression. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis suffers from class imbalances and distributional discrepancies of fault data under different working conditions. The class imbalance of the fault class increases the difficulty of learning the classification boundary of the diagnostic model for the minority class. Furthermore, the diversities of feature distributions decrease the diagnostic model's generalizability under various working conditions. Thus, in this paper, a deep adversarial transfer learning model for imbalanced bearing fault diagnosis (deep Imba-DA) is proposed to overcome these problems. In the proposed method, a cost-sensitive deep classifier is used to solve the class imbalance problem, and the domain adversarial subnet with the intraclass maximum mean discrepancy (MMD) is used to minimize the marginal and conditional distributional discrepancy between the source (data under one working condition) and target domain (data under another working condition) simultaneously. The performance of deep Imba-DA is evaluated and analyzed on the Case Western Reserve University (CWRU) and Paderborn datasets. The results show that deep Imba-DA outperforms other baseline methods on bearing diagnostic tasks. • An imbalanced bearing fault diagnostic model under variant conditions is proposed. • A joint transfer network with marginal and conditional distribution is developed. • Pseudo label strategy guarantees a good accuracy and fast convergence. [ABSTRACT FROM AUTHOR]

• Generalization of diagnosis model is improved on new hard disks without faults. • New faulty samples of hard disks could be generated from healthy samples. • Distribution discrepancy between different types of hard disks could be decreased. • End-end EM-based training strategy guarantees a good accuracy and convergence. Intelligent fault diagnosis of hard disks becomes significantly important to guarantee reliability of current cloud-based industrial systems. Most intelligent diagnostic methods are commonly based on assumptions that data from different disks are subject to the same distribution and there are sufficient faulty samples for training the models. However, in reality, there are types of hard disks from different manufacturers and their SMART encoding varies widely across manufacturers. It results in distribution discrepancy among disks and influences the generalization of machine learning methods. Moreover, hard disks usually work in healthy state that faulty events rarely happen on most of them, or especially never occur on new ones. Thus, this paper proposes a deep generative transfer learning network (DGTL-Net) for intelligent fault diagnostics on new hard disks. The DGTL-Net combines the deep generative network that generates fake faulty samples and the deep transfer network that solves the problem of distribution discrepancy between hard disks. An iterative end-end training strategy is also proposed for DGTL-Net to get the most optimal parameters of generative and transfer network simultaneously. Experiments have been conducted to prove that our method achieves better performance. [ABSTRACT FROM AUTHOR]