Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

Wu, Chunzhi; Jiang, Pengcheng; Ding, Chen; Feng, Fuzhou; Tang, Chen

doi:10.1016/j.compind.2018.12.001

Cited by 254 publications

(104 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking raw vibration signals as input, researchers built a standard RNN [119], [120] and 1D CNN [121], [122] to automatically learn representative features and output the desired targets. Some merits of the end-to-end learning paradigm are the following: it lets the data speak; feature engineering is automated, without the need for hand-crafted features; parameters of the whole network can be jointly optimized, leading to better accuracy; the network is generic and can be easily transferred or adapted to a different but similar scenario.…”

Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

View full text Add to dashboard Cite

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

show abstract

Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Wu et al developed a CNN structure for gearbox fault diagnosis, effectually coping with the existing challenge on endto-end fault diagnosis. Prognostics and Health Management 2009 gearbox challenge data and a planetary gearbox test rig were used to verify the effectiveness of the method [99]. The maximum accuracy of 99% was acquired, which outperformed the other three approaches for comparison.…”

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

View full text Add to dashboard Cite

Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

show abstract

“…In order to deal with one-dimensional raw sensor signals, one-dimensional CNN (1D-CNN) has been adopted. Wu et al [34] used 1D-CNN for diagnosis of gearbox systems and achieved better diagnosis accuracy, as compared to other methods that are based on traditional signal demodulation methods. Jiang et al [35] proposed a 1D-CNN based fault diagnosis method for a wind turbine gearbox that can consider the multiscale characteristics inherent in vibration signals.…”

Section: B Cnn-based Fault Diagnosismentioning

confidence: 99%

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Kim

Jung

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Fault diagnosis of rotor systems is important to prevent unexpected failures. Recently, deep learning (DL) methods, such as a convolutional neural network (CNN), have been utilized in many research areas, including fault diagnosis. DL has gained significant attention thanks to its ability to efficiently learn proper features from input data. It is possible to learn enriched hierarchical features by making the DL architectures deeper; therefore, many studies have been conducted to stack the neural networks, which are the basic building blocks of DL, deeper. However, it becomes difficult to comprehensively train neural network architectures as they become deeper, due to problems in the flow of gradient information during the training phase. In this paper, a direct connection based CNN (DC-CNN) method is proposed to significantly improve training efficiency and diagnosis performance. DC-CNN connects feature maps of different layers within a CNN to improve the gradient information flow over the layers. These additional connections, however, can increase the number of trainable parameters within the network. To prevent problems that might be caused by an increased number of parameters, dimension reduction modules are also developed. Moreover, to consider the anisotropic characteristics inherent in rotor systems, the vibration images containing both spatial and temporal information are generated and utilized. The effectiveness of DC-CNN is validated using experimental data from a rotor testbed. The experimental results indicate that the proposed method outperforms other conventional approaches with a smaller number of parameters. Also, visualizations of the learned features indicate that the proposed method can learn much more effective and significant features. Furthermore, the proposed method outperforms other approaches under conditions of insufficient or noisy data.

show abstract

Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

Cited by 254 publications

References 17 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Contact Info

Product

Resources

About