Fault diagnosis of rotating machinery based on recurrent neural networks

Zhang, Yahui; Zhou, Taotao; Huang, Xufeng; Cao, Longchao; Zhou, Qi

doi:10.1016/j.measurement.2020.108774

Cited by 174 publications

(59 citation statements)

References 22 publications

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“…Therefore, many researchers have introduced neural networks into the field of fault diagnosis [ 2 , 3 ] and achieved automatic fault diagnosis based on deep learning. Deep learning methods, such as Convolutional Neural Networks [ 4 , 5 ], DBNs (Deep Belief Networks) [ 6 , 7 ], Generative Adversarial Networks [ 8 , 9 ], Recurrent Neural Networks [ 10 , 11 ], and Deep Autoencoder [ 12 , 13 ], by automatically and efficiently extracting feature information, overcome several limitations of traditional diagnostic methods and significantly improve the diagnostic accuracy. Among them, the typical BP (Back Propagation) neural network is widely used in the field of fault diagnosis, such as automobile transmission systems, engines, and hydraulic power steering systems [ 14 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

Zhou

Cheng

Chang

et al. 2021

Computational Intelligence and Neuroscience

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Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

Zhou

Cheng

Chang

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…It also includes triaxial vibration data of three phase induction motor in healthy condition. The acquired data can be used for evaluation of new methods proposed for bearing fault detection and identification such as methods presented in the research [1] , [2] , [3] , [4] . The collected datasets are stored in comma separated values (CSV) files.…”

Section: Data Descriptionmentioning

confidence: 99%

Triaxial bearing vibration dataset of induction motor under varying load conditions

et al. 2022

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“…As the depth of the network increases, neural networks easily suffer from vanishing or exploding gradient problems, which causes the training process difficult to converge. To alleviate this issue, Zhang et al [115] employed RNN with residual connection to learn representative features. They compared the performance of the RNN with and without residual connection.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…However, when the number of training sample is small but the number of features is huge, Fig. 8 Effect of residual connection on classification accuracy [115] Fig. 9 Classification accuracy (a) and computation times (b) of LSPTSVM, PSVM, and SVM [120] then it is not necessary that all available features are of equal importance in the classification context.…”

Section: Support Vector Machinementioning

confidence: 99%

Technology development and commercial applications of industrial fault diagnosis system: a review

Liu

Cichoń

Królczyk

et al. 2021

Int J Adv Manuf Technol

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Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.

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Fault diagnosis of rotating machinery based on recurrent neural networks

Cited by 174 publications

References 22 publications

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

Triaxial bearing vibration dataset of induction motor under varying load conditions

Technology development and commercial applications of industrial fault diagnosis system: a review

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