An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

Wang, Fengtao; Dun, Bosen; Liu, Xiaofei; Xue, Yuhang; Li, Hongkun; Han, Qingkai

doi:10.1155/2018/6024874

Cited by 13 publications

(9 citation statements)

References 25 publications

(24 reference statements)

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“…Wang et al 2018 83 86 introduced heterogeneous transfer learning (HTL) based on stack sparse auto-encoder (SSAE). In order to solve the problem of small target domain data, they introduced a concept of distance to the center of the source and target domain to find the similarity of distribution using heterogeneous characteristics representation in HTL.…”

Section: Based Rotor-bearing System Fault Diagnosismentioning

confidence: 99%

“…Wang et al 2018 83 and Wang et al 2018, 84 employed Gaussian radial basis kernel function based autoencoder (KAEs) to enhance the feature extraction technique especially for non-linear component in bearing fault diagnosis of aircraft engine. A DNN was built with one kernel based autoencoder and multiple KAEs and result showed that the present methodology has higher accuracy because of its better feature clustering effect in feature extractions.…”

Section: Based Rotating Machines Fault Diagnosismentioning

confidence: 99%

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A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

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Section: Based Rotor-bearing System Fault Diagnosismentioning

confidence: 99%

Section: Based Rotating Machines Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

View full text Add to dashboard Cite

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“…In order to obtain good classification performance, the unsupervised model is often combined with the supervised model to form a semi-supervised model to achieve the purpose of adjusting the performance of the unsupervised model with the help of label information to obtain a reliable classification model. Wang FT et al [8] proposed an enhanced depth feature extraction method based on Gaussian radial basis kernel function and autoencoder. The advantage of this method is that it can obtain higher test accuracy based on fewer iterations, but the disadvantage is that it requires manual experience to adjust network parameters.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Zeng

et al. 2022

Applied Sciences

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Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.

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“…As an effective tool for dimension reduction, data mining, and machine learning, manifold learning seeks to cover the complex data processing mechanism behind the identification behavior, which can achieve the dimension reduction and feature extraction of massive fault data. erefore, it can be used as a technical means of mechanical fault diagnosis, and the fault category is better identified on the basis of maintaining the essential characteristics of fault data [6,7]. e feature extraction and fault diagnosis of mechanical equipment based on manifold learning have been a research hot spot in recent years.…”

Section: Introductionmentioning

confidence: 99%

Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis

Wang

et al. 2020

Shock and Vibration

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Fault diagnosis is essentially the identification of multiple fault modes. How to extract sensitive features and improve diagnostic accuracy is the key to fault diagnosis. In this paper, a new manifold learning method (Mass Laplacian Discriminant Analysis, MLDA) is proposed. Firstly, it is assumed that each data point in the space is a point with mass, and the mass is defined as the number of data points in a certain area. Then, the idea of universal gravitation is introduced to calculate the virtual universal gravitation between data points. Based on the Laplace eigenmaps algorithm, the gravitational Laplacian matrix between the same kind of data and the heterogeneous data is obtained, and the discriminant function is constructed by the ratio of the virtual gravitation between the heterogeneous data and the virtual gravitation between the similar data; the projection function with the largest discriminant function value is the optimal feature mapping function. Finally, based on the mapping function, the eigenvalues of the training data and the test data are calculated, and the softmax algorithm is used to classify the test data. Experiments on gear fault diagnosis show that this method has higher diagnostic accuracy than other manifold learning algorithms.

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An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

Cited by 13 publications

References 25 publications

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis

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