Deep transfer learning with limited data for machinery fault diagnosis

Data-driven machinery fault diagnosis has gained much attention from academic research and industry to guarantee the machinery reliability. Traditional fault diagnosis frameworks are commonly under a default assumption: the training and test samples share the similar distribution. However, it is nearly impossible in real industrial applications, where the operating condition always changes over time and the quantity of the same-distribution samples is often not sufficient to build a qualified diagnostic model. Therefore, transfer learning, which possesses the capacity to leverage the knowledge learnt from the massive source data to establish a diagnosis model for the similar but small target data, has shown potential value in machine fault diagnosis with small sample size. In this paper, we propose a novel fault diagnosis framework for the small amount of target data based on transfer learning, using a modified TrAdaBoost algorithm and convolutional neural networks. First, the massive source data with different distributions is added to the target data as the training data. Then, a convolutional neural network is selected as the base learner and the modified TrAdaBoost algorithm is employed for the weight update of each training sample to form a stronger diagnostic model. The whole proposition is experimentally demonstrated and discussed by carrying out the tests of six three-phase induction motors under different operating conditions and fault types. Results show that compared with other methods, the proposed framework can achieve the highest fault diagnostic accuracy with inadequate target data.

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A Transfer Learning Strategy for Rotation Machinery Fault Diagnosis based on Cycle-Consistent Generative Adversarial Networks

2018 Chinese Automation Congress (CAC) ◽

10.1109/cac.2018.8623346 ◽

2018 ◽

Cited By ~ 3

Author(s):

Yuan Xie ◽

Tao Zhang

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Learning Strategy ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Machinery Fault Diagnosis

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Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

Advances in Mechanical Engineering ◽

10.1177/16878140211040226 ◽

2021 ◽

Vol 13 (8) ◽

pp. 168781402110402

Author(s):

Jiajie Shao ◽

Zhiwen Huang ◽

Yidan Zhu ◽

Jianmin Zhu ◽

Dianjun Fang

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Working Conditions ◽

Diagnostic Performance ◽

Domain Adaptation ◽

Rotating Machinery ◽

Learning Method ◽

Migration Ability ◽

Adversarial Learning ◽

Machinery Fault Diagnosis

Rotating machinery fault diagnosis is very important for industrial production. Many intelligent fault diagnosis technologies are successfully applied and achieved good results. Due to the fact that machine damages usually happen under different working conditions, and manual scale labeled data are too expensive, domain adaptation has been developed for fault diagnosis. However, the current methods mostly focus on global domain adaptation, the application of subdomain adaptation for fault diagnosis is still limited. A deep transfer learning method is proposed for rotating machinery fault diagnosis in this study, where subdomain adaptation and adversarial learning are introduced to align local feature distribution and global feature distribution separately. Experiments are performed on two rotating machinery datasets to verify the effectiveness of this method. The results reveal that this method has outstanding mutual migration ability and can improve the diagnostic performance.

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A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis

Computers in Industry ◽

10.1016/j.compind.2021.103399 ◽

2021 ◽

Vol 127 ◽

pp. 103399

Author(s):

Yafei Deng ◽

Delin Huang ◽

Shichang Du ◽

Guilong Li ◽

Chen Zhao ◽

...

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Double Layer ◽

Adversarial Network ◽

Partial Transfer ◽

Machinery Fault Diagnosis

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Alignment subdomain based deep convolutional transfer learning for machinery fault diagnosis under different working conditions

Measurement Science and Technology ◽

10.1088/1361-6501/ac40a7 ◽

2021 ◽

Author(s):

Yibing Li ◽

Hu Wan ◽

Li Jiang

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Working Conditions ◽

Local Maximum ◽

Mean Difference ◽

Global Distribution ◽

Target Domain ◽

Learning Methods ◽

Time Frequency ◽

Machinery Fault Diagnosis

Abstract In recent years, transfer learning methods have been extensively used in machinery fault diagnosis under different working conditions. However, most of these transfer learning methods perform poorly in the actual industrial applications, due to the fact that they mainly focus on the global distribution of different domains without considering the distribution of subdomains belonging to the same category in different domains. Therefore, we propose an alignment subdomain-based deep convolutional transfer learning (AS-DCTL) network for machinery fault diagnosis. First, continuous wavelet transform is used to transform the original vibration signal into a two-dimensional time-frequency image. Then, AS-DCTL uses convolutional neural network as the feature extractor to extract the features of the source and target domain samples and introduces maximum mean difference to align the global distribution of the extracted features. Simultaneously, we use local maximum mean difference as a metric criterion to align the distribution of related subdomains, by adding weights to similar samples in the source domain and target domain. The experimental results of the two case studies show that the proposed AS-DCTL network can achieve higher recognition accuracy and classification effect, in comparison with the current mainstream transfer learning methods.

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