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

Xie, Yiyang; Zhang, Tao

doi:10.1109/cac.2018.8623346

Cited by 30 publications

(15 citation statements)

References 7 publications

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“…Xie et al proposed using periodic consistency GAN to generate false samples for auxiliary diagnosis to diagnose bearing faults under different operating conditions [147]. Li et al used a GAN-based generative model to generate false fault samples for fault diagnosis of bearings under different operating conditions [148].…”

Section: Basic Eory Of Ganmentioning

confidence: 99%

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Cui¹,

Meng²,

Wang³

et al. 2021

Shock and Vibration

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With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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Section: Basic Eory Of Ganmentioning

confidence: 99%

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Cui¹,

Meng²,

Wang³

et al. 2021

Shock and Vibration

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show abstract

“…That is to say, in training stage, apart from massive labeled samples from the source domain, only normal samples of the target domain are available. Several research works discussed the crossdomain diagnosis problem under this setting [82], [89], [96], [114], [115].…”

Section: ) Different Problem Settingsmentioning

confidence: 99%

“…According to different data types, we divide the inputs of these approaches into two categories: time series data and image data. Among them, 1D time series were the most common input type, such as raw or preprocessed vibration signals [68], [73], [74], [84], [90], [92], [94], [97]- [99], [102], [109], [110], [120], [122], [127], [129] and frequency spectra [63]- [66], [76], [78], [82], [86], [88], [89], [93], [112], [114], [124], [126]. Other approaches used 2D images as inputs, and the images were mostly generated by signal-segment-stack [70], [87], [100], [105], [115], [121] and time-frequency representations (including short-time Fourier transform [116], wavelet transform [104], [106], S-transform [69], [117], [118]).…”

Section: ) Inputs Of Cross-domain Diagnosis Approachesmentioning

confidence: 99%

Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

et al. 2019

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Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis. INDEX TERMS Cross-domain, domain adaptation, fault diagnosis, review, transfer learning.

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“…Most recently, many interesting ADA approaches based on GAN have been proposed. For example, Xie et al [17] used a cycle-consistent GAN to solve the rotation machinery fault diagnosis problem. Liu et al [18] proposed a small-sample wind turbine fault detection method using the GAN.…”

Section: Introductionmentioning

confidence: 99%