A comprehensive review on convolutional neural network in machine fault diagnosis

Jiao, Jinyang; Zhao, Ming; Lin, Jing; Liang, Kaixuan

doi:10.1016/j.neucom.2020.07.088

Cited by 386 publications

(141 citation statements)

References 216 publications

(141 reference statements)

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“…While FFCNN is effectively applied in domain adaptation for fault diagnosis, we still face the following challenges regarding transfer learning and fault diagnosis: While FFCNN can improve the effect of domain adaptation, if the source domain and target domain are too different, FFCNN will also fail. How to further enhance the effect of domain adaptation still needs to be further studied [ 47 ]. We explained the FFCNN from the perspective of frequency domain.…”

Section: Discussionmentioning

confidence: 99%

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Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Zheng

et al. 2021

Sensors

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

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Section: Discussionmentioning

confidence: 99%

“…While FFCNN can improve the effect of domain adaptation, if the source domain and target domain are too different, FFCNN will also fail. How to further enhance the effect of domain adaptation still needs to be further studied [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Zheng

et al. 2021

Sensors

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“…As the core layer of CNN, most calculations are performed in the convolutional layer. It contains different feature information extracted by multiple convolution kernels [ 49 ]. Rich feature data can be extracted with deep convolutional layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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“…CNNs are particularly advantageous in the context of fault diagnosis since they implement the feature extraction and classification tasks in an end-to-end fashion. Moreover, they can be applied to several data structures, including both time-series and images ( Jiao et al, 2020 ). A common strategy to employ 2D-CNNs 6 in PHM applications is to feed these models with image-like data.…”

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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A comprehensive review on convolutional neural network in machine fault diagnosis

Cited by 386 publications

References 216 publications

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

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