Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Neupane, Dhiraj; Seok, Jongwon

doi:10.1109/access.2020.2990528

Cited by 293 publications

(155 citation statements)

References 125 publications

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“…In this paper, the WDTL performance model is verified using the CWRU bearing failure laboratory experimental data [30]. The experimental platform is shown in Fig.…”

Section: A Datasets Descriptionmentioning

confidence: 99%

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

2021

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“…In this paper, the WDTL performance model is verified using the CWRU bearing failure laboratory experimental data [30]. The experimental platform is shown in Fig.…”

Section: A Datasets Descriptionmentioning

confidence: 99%

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

2021

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“…In the literature, DL methods have been used increasingly in fault detection and diagnosis due to their high efficiency and accuracy. Specifically, several DL methods have been proposed to diagnose bearing faults based on the CWRU data set [36]. The most common methods include auto-encoders, convolutional neural networks (CNN), deep belief networks (DBN), and generative adversarial networks (GAN).…”

Section: Fractal Characteristics Of Vibration Signalsmentioning

confidence: 99%

Bearing Fault Diagnosis Approach under Data Quality Issues

AlShalalfeh

Shalalfeh

2021

Applied Sciences

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In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α1) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.

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“…Most of the research in recent years has focused on unsupervised homogeneous transfer learning [ 38 ], which is also the direction of our work. Domain adaptation is a common method to solve unsupervised homogeneous transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Zheng

et al. 2021

Sensors

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Cited by 293 publications

References 125 publications

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

Bearing Fault Diagnosis Approach under Data Quality Issues

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

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