Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Tang, Shengnan; Yuan, Shouqi; Zhu, Yong

doi:10.1109/access.2020.3012182

Cited by 97 publications

(51 citation statements)

References 113 publications

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“…In common fault diagnosis methods, data preprocessing technologies are usually used to achieve feature extraction by complex steps [ 46 ]. Combining signal acquisition, feature extraction and fault classification, intelligent techniques could be considered as a potent direction in developing novel fault classification methods [ 47 ].…”

Section: Proposed Intelligent Fault Diagnosis Methodsmentioning

confidence: 99%

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Tang

Yuan

Zhu

et al. 2020

Sensors

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A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

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Section: Proposed Intelligent Fault Diagnosis Methodsmentioning

confidence: 99%

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Tang

Yuan

Zhu

et al. 2020

Sensors

Self Cite

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“…With this advantage, it is applied to various fields and has achieved great success [36]. CNN conducts data training through the convolution layer and pooling layer.…”

Section: Shock and Vibrationmentioning

confidence: 99%

Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

Jiang

Zhang

et al. 2021

Shock and Vibration

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An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.

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“…Localization can be accomplished via STFT and WT in both time and frequency domains. Compared with STFT, the superiority of CWT is that it carries out analysis in a variable time-frequency window instead of only the fixed window [ 47 , 48 ].…”

Section: Basic Theorymentioning

confidence: 99%

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Tang

Zhu

Yuan

et al. 2020

Sensors

Self Cite

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As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.

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Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Cited by 97 publications

References 113 publications

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

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