A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions

Qian, Weiwei; Li, Shunming; Wang, Jinrui

doi:10.1109/access.2018.2880770

Cited by 86 publications

(51 citation statements)

References 29 publications

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“…In addition, Ensemble TICNN adds ensemble learning to improve the stability of the algorithm. SF-SOF-HKL : Inspired by moment discrepancy and Kullback-Leibler (KL) divergence, Qian et al [ 26 ] proposed using high-order KL (HKL) divergence to align the high-order moments of the domain-specific distributions. Sparse filtering with HKL divergence (SF-HKL) can learn both discriminative and shared features between the source and target domains.…”

Section: Case Studymentioning

confidence: 99%

“…Zhang et al [ 25 ] proposed a Wasserstein distance guided Multi-Adversarial network-based method, in which the learning process is to minimize the Wasserstein distance between the source domain and the target domain by using an adversarial training strategy. Qian et al [ 26 ] built a fault diagnosis network that is robust with working condition variation based on high-order Kullback-Leibler (HKL) and transfer learning, wherein a sparse filter with HKL divergence is proposed for learning the domain-invariant features. In all the deep domain adaptation methods mentioned above, deep features are aligned for minimizing distribution discrepancy.…”

Section: Introductionmentioning

confidence: 99%

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One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Wang

Zhao

et al. 2020

Sensors

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Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Wang

Zhao

et al. 2020

Sensors

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“…So a diagnostic model adapted to multiple working conditions is highly desirable. TL [38][39][40] is a popular approach in machine learning. It can be applied between two related domains to reduce training time and save training samples.…”

Section: Tl and Fine-tuning Strategymentioning

confidence: 99%

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

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“…Transfer learning has attracted extensive attention [8,9].The feature extraction method of singular value decomposition and autocorrelation matrix is combined with the transfer learning TrAdaBoost algorithm to diagnose the motor fault [10][11][12][13][14]. A new fault identification method based on the combining long and short term memory network (LSTM) and transfer learning (TL) is proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

Zhang

et al. 2020

IEEE Access

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It is very difficult to obtain the label data of rolling bearings under the complicated and variable working conditions, which results in low diagnosis accuracy. Transfer sparse coding(TSC) is a new feature representation method, which can effectively extract features from data matrix. Joint geometric and statistical alignment (JGSA) is a domain adaptation method, which can reduce the distribution shift and geometric shift between domains. In order to make full use of the feature extraction ability of the TSC and the transfer classification ability of the JGSA, a new transfer learing fault diagnosis(TSC-JGSA) method based on combining the characteristics of the TSC and JGSA is proposed to realize the fault diagnosis of rolling bearings under variable working conditions in this paper. In the TSC-JGSA, the fast Fourier transform technology is used to transform the time-domain signals into frequency-domain amplitudes. Then the TSC is used to effectively extract the deep features from the obtained frequency-domain amplitudes in order to construct a sparse feature matrix, which is input into the JGSA in order to realize the fault diagnosis of rolling bearings. Finally, the vibration data of rolling bearings under variable working conditions is used to prove the effectiveness of the TSC-JGSA. The experiment results show that the TSC-JGSA can effecrively solve the problem of lacking label data in actual engineering by using label data in the laboratory, and obtan higher diagnosis accuracy than other compared methods. It provides a new diagnosis idea for rotating machinery.

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A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions

Cited by 86 publications

References 29 publications

One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

A New Transfer Learning Fault Diagnosis Method Using TSC and JGSA Under Variable Condition

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