Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis

Zhang, Zhongwei; Chen, Huaihai; Li, Shunming; An, Zenghui

doi:10.1016/j.measurement.2020.108071

Cited by 48 publications

(16 citation statements)

References 29 publications

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“…It is based on the same principle as MMD but focuses on second-order statistics instead of first-order statistics. PHM approaches were presented by Zhang et al [228] and Zhang et al [229] for the condition diagnosis of bearings and gearboxes. Other measures used in PHM to compare the similarity between datasets are the cosine distance [137], [144], the log-Euclidean metric of second-order statistics [230], and the a-distance [192].…”

Section: B Feature Alignment By Other Similarity Measuresmentioning

confidence: 99%

“…In the latter, the source samples are weighted by their relevance for the target domain. Zhang et al [228] enhanced TJM by the additional use of MVD and applied the approach to bearing condition diagnosis. Zhang et al [229] also assigned weights to the source samples to reduce the influence of irrelevant samples and used MVD as a similarity measure.…”

Section: E Combined Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: B Feature Alignment By Other Similarity Measuresmentioning

confidence: 99%

Section: E Combined Approachesmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…DA has been intensively studied in recent years in the context of PHM applications. Proposed approaches include discrepancy-based [13][14][15], adversarial [7,16,17] and reconstruction based methods [18,19]. Most of the proposed approaches have been exclusively developed for ClosedSet DA where the source and target domain cover the same class set -see left column in Fig.…”

Section: Related Workmentioning

confidence: 99%

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

Rombach¹,

Michau²,

Fink³

2022

Preprint

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New operating conditions can result in a performance drop of fault diagnostics models due to the domain gap between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the label spaces of the two domains are not congruent. To improve the transferability of the trained models, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework based on a Wasserstein GAN for Partial and OpenSet&Partial domain adaptation. The main contribution is the controlled fault data generation that enables to generate unobserved fault types and severity levels in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. To evaluate the ability of the proposed method to bridge domain gaps in different domain adaption settings, we conduct Partial as well as OpenSet&Partial domain adaptation experiments on two bearing fault diagnostics case studies. The results show the versatility of the framework and that the synthetically generated fault data helps bridging the domain gaps, especially in instances where the domain gap is large.

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“…With the growth of the social economy, there are more stringent performance criteria for industrial machinery [1]. Rolling bearings, as a vital component of industrial equipment, will reduce the performance of industrial equipment when they fail, and may even cause the machine to shut down [2][3][4]. The effectiveness of industrial equipment depends on the bearing state, which operates in a complex environment and under variable loading, so there are several factors that can trigger faults in bearings [5].…”

Section: Introductionmentioning

confidence: 99%

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

Liu

Sun

et al. 2022

Energies

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The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

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Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis

Cited by 48 publications

References 29 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

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