ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis

Chen, Yuanhang; Peng, Gaoliang; Xie, Chaohao; Zhang, Wei; Li, Chuanhao; Liu, Shaohui

doi:10.1016/j.neucom.2018.03.014

Cited by 100 publications

(49 citation statements)

References 16 publications

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“…In addition, the Paderborn dataset also incorporates both artificially induced bearing fault and realistic damages caused by accelerated lifetime tests. In [116], the Paderborn dataset is used to train a deep inception net with atrous convolution, which improves the average accuracy from 75% (best result of conventional data-driven methods) to 95% for diagnosing the real bearing faults when trained only with the data generated from artificial bearing damages. The "PRONOSTIA bearings accelerated lifetime test dataset" [27], as introduced in Section II, is applied in [117] with a deep convolution structure consisting of 8 layers: 2 convolutional, 2 pooling, 1 flat, and 3 nonlinear transformation layers.…”

Section: ) Experimental Setup and Data Descriptionmentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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Section: ) Experimental Setup and Data Descriptionmentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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“…Defects of bearing can lead to serious damage for the entire mechanical system [1][2][3][4], so it is very important to study the reliable fault diagnosis method to prevent the bearing from malfunction [5][6][7]. e features of the bearing vibration signal (BVS) are key to the fault diagnosis results of bearing.…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Fei

2019

Shock and Vibration

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Fault diagnosis of bearing based on variational mode decomposition (VMD)-phase space reconstruction (PSR)-singular value decomposition (SVD) and improved binary particle swarm optimization (IBPSO)-K-nearest neighbor (KNN) which is abbreviated as VPS-IBPSOKNN is presented in this study, among which VMD-PSR-SVD (VPS) is presented to obtain the features of the bearing vibration signal (BVS), and IBPSO is presented to select the parameter K of KNN. In IBPSO, the calculation of the next position of each particle is improved to fit the evolution of the particles. The traditional KNN with different parameter K and trained by the training samples with the features based on VMD-SVD (VS-KNN) can be used to compare with the proposed VPS-IBPSOKNN method. The experimental result demonstrates that fault diagnosis ability of bearing of VPS-IBPSOKNN is better than that of VS-KNN, and it can be concluded that fault diagnosis of bearing based on VPS-IBPSOKNN is effective.

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“…Qu et al [28] investigated the influence of deformation-band damage zone on reservoir performance in the presence of different fault core transmissibility multipliers. Chen et al [29] proposed a novel model-deep inception nets with atrous convolution-to extract common features shared by both kinds of data, because the differences between the artificial one and the natural one baffle the learning machine. Li et al [30] proposed a three-dimensional lumped-parameter nonlinear dynamic model for compound planetary gear set, which takes into consideration time-varying mesh stiffness (TVMS), mesh phase relations, and gear chipping defects.…”

Section: Introductionmentioning

confidence: 99%

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy

Zhao

Yao

et al. 2018

Entropy

101

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A damage degree identification method based on high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) is proposed in this paper to solve the problem that fault signal of rolling bearings are weak and difficult to be quantitatively measured. In the HMGSEDI method, on the basis of mathematical morphology gradient spectrum and spectrum entropy, the changing scale influence of structure elements to damage degree identification is thoroughly analyzed to determine its optimal scale range. The high-order difference mathematical morphology gradient spectrum entropy is then defined in order to quantitatively describe the fault damage degree of bearing. The discrimination concept of fault damage degree is defined to quantitatively describe the difference between the high-order differential mathematical entropy and the general mathematical morphology entropy in order to propose a fault damage degree identification method. The vibration signal of motors under no-load and load states are used to testify the effectiveness of the proposed HMGSEDI method. The experiment shows that high-order differential mathematical morphology entropy can more effectively identify the fault damage degree of bearings and the identification accuracy of fault damage degree can be greatly improved. Therefore, the HMGSEDI method is an effective quantitative fault damage degree identification method, and provides a new way to identify fault damage degree and fault prediction of rotating machinery.

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ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis

Cited by 100 publications

References 16 publications

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy

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