Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Wang, Huaqing; Xu, Zhitao; Tong, Xingwei; Song, Liuyang

doi:10.3390/s23042137

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…The authors demonstrate the effectiveness of their approach through experiments on several mechanical fault datasets: Laboratory Gearbox dataset, the Case Western Reserve University (CWRU) dataset, the Intelligent Maintenance Systems (IMS), the centrifugal pump dataset. They compare their method with other domain adaptation methods and show that it outperforms them in terms of accuracy and robustness [64] for tomato disease recognition. The approach provides a set of guidelines for analyzing the recognition of novel data to make the system more adaptable to real-world environments.…”

Section: ) Open Set Domain Adaptation Literature Reviewmentioning

confidence: 99%

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Akshay,

Gopalakrishnan,

Sowmya

et al. 2024

IEEE Access

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Nonlinear fluid dynamical systems, such as thermoacoustic systems, aeroelastic systems are archetypical complex systems involving state transitions upon a change in bifurcation parameter. These state transitions in any certain direction are always undesirable and can radically alter the operational paradigms associated with these systems. Hence, predicting the impending dynamical state is paramount for avoiding such undesirable transitions. The hitherto research so far focused largely on metric-based and modelbased indicators to foretell an impending transition and is often fraught with difficulties when deployed in practicable scenarios. In this study, we assuage this end of concern by proposing a model-agnostic datadriven method for automated classification of the dynamical states of nonlinear fluid dynamical systems. By using recurrence plots we transform the time series pertaining to the dynamical states into images and subsequently employ a convolution neural network (CNN) to classify the generated images. This study also proceeds to present cross-domain classifications via a trained deep learning (DL) model and successfully classify the dynamical states of one fluid dynamical system (say, thermoacoustic) with the dynamical states of another fluid dynamical system (say, aeroelastic). The underlying methodology for the above is based on open set (OS) domain adaptation -inherent to transfer learning schemes. Towards enhancing the confidence levels of our proposed methodology, we carry out four cross-domain numerical experiments, wherein we consistently get about 94 -98% accuracy.

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Section: ) Open Set Domain Adaptation Literature Reviewmentioning

confidence: 99%

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Akshay,

Gopalakrishnan,

Sowmya

et al. 2024

IEEE Access

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An adaptive thresholding approach for open set fault diagnosis

Wang,

Cheng,

Deng

2024

Meas. Sci. Technol.

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Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-Nearest Neighbors based Adaptive Thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.

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A lightweight improved residual neural network for bearing fault diagnosis

Wang,

Fu,

Lin

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Timely and accurate fault diagnosis is essential for modern industrial system reliability and safety. However, traditional bearing fault diagnosis using the residual neural network (ResNet) is limited by high parameters, computational costs, and low accuracy. To address this, a lightweight improved residual network (LIResNet) is proposed to enhance the ResNet structure. LIResNet introduces a parallel layer (PL) merging the discrete cosine transform convolutional layer (DCTConv) and the standard convolutional layer (Conv). This reduces trainable parameters, aiding feature extraction from input signals, with spectrograms preprocessed by CWT fed into the PL. Furthermore, integrating a self-attention mechanism enhances fault feature comprehension. The residual structure is refined using low-rank decomposition, reducing model complexity. This refinement, combined with a max-pooling layer and single convolutional layer, forms the downsampling residual module (DAM). The proposed batch-free normalization (BFN) and improved hard-swish activation function constitute the normalized activation module (NAM), ensuring model stability and nonlinear mapping in low-batch data situations. Experiments across bearing datasets reveal that the proposed method achieves a diagnostic accuracy of 99.8%, surpassing ResNet18′s 98.7%. Moreover, while ResNet comprises 11.18M parameters and 753.07M Flops, the proposed method uses 0.06M parameters and 49.49M Flops. These findings underscore the improved lightweight performance and diagnostic capability of our approach.

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Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Cited by 4 publications

References 41 publications

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

An adaptive thresholding approach for open set fault diagnosis

A lightweight improved residual neural network for bearing fault diagnosis

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