An SDP Characteristic Information Fusion‐Based CNN Vibration Fault Diagnosis Method

Xiao-hong, Zhu; Zhao, Jianhong; Hou, Dibo; Han, Zhonghe

doi:10.1155/2019/3926963

Cited by 25 publications

(9 citation statements)

References 15 publications

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“…The raw vibration signal has also been transformed to an imagery form for diagnosis purposes using techniques like Continuous Interleaved Sampling (CIS) [108]- [112], Omnidirectional Regeneration Technique (ORT) [113] and Symmetrized Dot Pattern (SDP) [114], [115]. Armed with these transformation techniques and the transfer learning strategy, several pretrained CNNs, originally trained on natural images, were transferred to fault diagnosis applications using vibration data; examples include LeNet-5 [107], [109], [110], VGG-16 [106], AlexNet [95] and ResNet-50 [108].…”

Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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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.

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Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…e results show that the proposed model has better generalization and convergence speed than the original AE network. [79], which has been widely used in computer vision [80,81], speech recognition [82], and other fields [83]. e typical structure of CNN is shown in Figure 13.…”

Section: Output Layermentioning

confidence: 99%

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Cui¹,

Meng²,

Wang³

et al. 2021

Shock and Vibration

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With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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“…Another kind of method is to obtain the feature image by processing the signal and recognizing the feature image. For example, the spectrum images [ 13 ], time–frequency images [ 14 ], axis orbit images [ 15 ], symmetrized dot pattern (SDP) images [ 16 ], etc. However, the essence of these two methods is to extract fault features by using signal processing technology, then use CNN to learn and recognize two-dimensional images or one-dimensional vectors containing fault feature signals.…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment

Xiao-hong

Liu

et al. 2022

Sensors

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CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%.

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An SDP Characteristic Information Fusion‐Based CNN Vibration Fault Diagnosis Method

Cited by 25 publications

References 15 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment

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