Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks

Liu, Yong Zhi; Shi, Ke; Li, Zhi Xuan; Ding, Guo Fu; Zou, Yi

doi:10.1016/j.measurement.2021.109553

Cited by 36 publications

(10 citation statements)

References 20 publications

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“…Liu et al proposed a transfer learning fault diagnosis model based on the deep full convolution conditional Wasserstein adversarial network. This model strengthens the supervision of the learning process and the effect of class field alignment [16]. To solve the problem much more effectively, many scholars combined the ensemble empirical mode decomposition method with deep learning to achieve the classification of bearing faults.…”

Section: Introductionmentioning

confidence: 93%

Fault diagnosis of rolling bearing under time-varying speed conditions based on EfficientNetv2

Qu¹,

Yang²,

Shen³

et al. 2022

Meas. Sci. Technol.

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Rolling bearing is a widely used component in engineering. Fault diagnosis of rolling bearing is the key issue to ensure the normal operation of equipment. At present, the research on fault diagnosis of rolling bearing mainly focuses on the analysis of vibration data under constant working conditions. Nevertheless, when dealing with practical engineering problems, the operation of equipment is frequent in the case of variable speed. To analyze the vibration data in the case of frequency conversion and accurately extract the fault characteristic frequency is a challenge especially when the fault characteristics are weak. In addition, the traditional vibration characteristic analysis requires professional technicians to supervise the operation of the equipment, which requires certain professional ability of the staff. Based on the above two problems, this paper proposes a rolling bearing fault diagnosis model under time-varying working conditions based on EfficientNetv2 network. This method uses short-time Fourier transform to convert one-dimensional vibration signal into two-dimensional image signal, and uses the advantages of image recognition network to realize the fault diagnosis under time-varying speed conditions. After training the network based on transfer learning, the experimental data verify that the accuracy of the results reach 99.9 ± 0.1% even though in the case of weak fault characteristics, and there is no need for professional technicians to supervise and diagnose after model training, which is conducive to practical application.

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Section: Introductionmentioning

confidence: 93%

Fault diagnosis of rolling bearing under time-varying speed conditions based on EfficientNetv2

Qu¹,

Yang²,

Shen³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Other condition diagnosis approaches that integrate multiple classifiers into the DANN structure but do not perform adversarial training between them and the feature extractor were proposed in [263] and [264] (B2.3). Liu et al [263] integrated two classifiers in a DANN structure. The classifiers were trained with different features to obtain two classification boundaries.…”

Section: Adversarial 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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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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“…Li et al 19 proposed a rolling bearing status monitoring method, which is based on the domain adaptation to minimize the multi-kernel maximum mean discrepancy (MK-MMD) between two bearing datasets. Liu et al 20 used a deep full convolution-based Wasserstein adversarial network to strengthen the supervision and control of the learning process. They verified the effect of class alignment and the superiority of their proposed method via transfer experiments.…”

Section: Introductionmentioning

confidence: 99%

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Kang

Zhang

et al. 2023

Structural Health Monitoring

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The brake pads of high-speed trains operate under complex and variable conditions, and the collected brake signals are easily affected by noise, making monitoring the health status of brake pads more difficult. A multi-representation adaptation network for online monitoring the health status of high-speed train brake pads, which are affected by noise under variable working conditions, is proposed in this study. First, a parameter-sharing deep residual network is used to extract the friction block features of the source and target domain data. Then, the features are mapped to different low-dimensional feature spaces through the inception adaptation module, and multiple representations are obtained. The network applies conditional maximum mean discrepancy to align representations of the source and target domains, thus learning multiple domain-invariant representations. Hence, the network acquires more knowledge of the friction block status and attenuates the interference of noise signals on the status monitoring. The friction block vibration data were collected from various brake disc speeds, and variable working condition-transfer experiments under the influence of noise were performed on the brake friction and bearing datasets. The results show that the proposed network outperforms other transfer methods, which can better extract and identify the status features of the friction block under the noise interference.

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Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks

Cited by 36 publications

References 20 publications

Fault diagnosis of rolling bearing under time-varying speed conditions based on EfficientNetv2

Fault diagnosis of rolling bearing under time-varying speed conditions based on EfficientNetv2

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

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

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