The Study on Rotating Machinery Fault Diagnosis Based on Deep Neural Networks

2016 ◽  
Author(s):  
Lang Bo ◽  
Jin Ying ◽  
Chen Yu Ping ◽  
Fan Xiaolong
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rotating Machinery ◽  
Machinery Fault Diagnosis

scholarly journals Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Author(s):  
Wei Cui ◽  
Guoying Meng ◽  
Aiming Wang ◽  
Xinge Zhang ◽  
Jun Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Safe Production ◽  
Continuous Progress ◽  
Machinery Fault Diagnosis

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.

Research on Rotating Machinery Fault Diagnosis System

2011 ◽  
Vol 55-57 ◽  
pp. 1310-1314
Author(s):  
Zheng Yao ◽  
Zhao Hua Wang
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Hybrid Method ◽  
Rotating Machinery ◽  
The Other ◽  
Bp Neural Networks ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Machinery Fault Diagnosis ◽  
Modern Industry

Fault diagnosis has been the research hotspot in the industry fields, but, with the gradual complication in modern industry equipments and systems, it is more hard to quickly diagnose complicated or exceptional faults. For overcoming the diagnosis weakness of traditional fault diagnosis methods in the rotating machinery, this paper presents a hybrid method that combines the wavelet with neural networks theory. Both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations were solved and this method was used in rotating machinery fault diagnosis. The research shows that this method is feasible and effective and can be applied to the other rotating machinery fault diagnosis.

scholarly journals A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 671
Author(s):  
Daoguang Yang ◽  
Hamid Reza Karimi ◽  
Len Gelman
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Deep Convolutional Neural Networks ◽  
Signal Features ◽  
Fuzzy Fusion ◽  
Machinery Fault Diagnosis

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.

Digital Twin for rotating machinery fault diagnosis in smart manufacturing

2018 ◽  
Vol 57 (12) ◽  
pp. 3920-3934 ◽  
Author(s):  
Jinjiang Wang ◽  
Lunkuan Ye ◽  
Robert X. Gao ◽  
Chen Li ◽  
Laibin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Machinery Fault Diagnosis
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