Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions

Zhang, Ran; Tao, Hongyang; Wu, Lifeng; Guan, Yong

doi:10.1109/access.2017.2720965

Cited by 296 publications

(155 citation statements)

References 22 publications

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“…In [14], a method based on neural network by using transferring parameters is proposed and success for diagnosing two datasets including 6 kinds of health conditions sampled from different fault diameters (BF IF OF with fault size being 0.007 in and 0.021 in) with the same motor load and speed (L0), and it focuses on fault diagnosis between two kinds of fault diameters under the same working conditions. In addition, unlike our method, it should be noted that a small amount of labeled data in test domain is needed when training modified neural networks, while our method does not need labeled test data during the training.…”

Section: Discussionmentioning

confidence: 99%

“…Although the fault diameter and categories are not changed, the distribution differences between training data (training domain) and test data (test domain) changes with working condition vary. As a direct result, the classifier can achieve high accuracy on training domain while performing poorly on test domain [14]. This is caused by distribution differences between two domains, since features extracted from one domain can not represent for another domain.…”

Section: Introductionmentioning

confidence: 99%

“…However, in operation of rotating machinery, because of complicated working conditions and complex sensor signals, the distribution of fault data is not consistent. Vibration signals sampled under different working conditions violate above assumption and show large distribution differences between domains [9,14], which leads to drop dramatically of performance. More specifically, taking the roller bearing fault diagnosis problem as an example, classifier was trained under a very concrete type of data sampled under a certain motor speed and load; however, the actual application in fault diagnosis is to recognize test data collected under another motor speed and load.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Tong

Zhang

et al. 2018

Shock and Vibration

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Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Tong

Zhang

et al. 2018

Shock and Vibration

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“…To enhance the accuracy of fault detection, statistics methods should be based on the frequency spectrum to reduce false and missing alarms [12]. Alternatively, machine learning methods, namely support vector machine (SVM) [13,14], decision tree (DT) [15], and various neural network architectures [16,17] combined with advanced signal processing can be used to find the complex relations on the feature space by using predefined time-frequency features, being based on fault characteristic frequencies. However, without the characteristic frequencies, the mentioned methods have great difficulty in classifying bearing faults [18].…”

Section: Introductionmentioning

confidence: 99%

“…Using transfer learning (TL) allows us to reduce the time and complexity of generating features for fault classification. Further, TL is very helpful for a bearing fault diagnosis if the available data for training and validation are limited in industry [16]. Within this work, a pretrained version of the well-known AlexNet convolutional neural network (CNN) architecture [19] is applied to CWT spectrograms of vibration and acoustic emission signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

Hemmer

Khang

Robbersmyr

et al. 2018

Designs

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Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data.

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APHMRoadmap for Electronics‐Rich Systems

Pecht

Kang²

2018

Prognostics and Health Management of Electronics

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Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions

Cited by 296 publications

References 22 publications

Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

APHMRoadmap for Electronics‐Rich Systems

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