Application of Factor Analysis Optimization Error Back Propagation Algorithm FA-BP Neural Network Equipment Fault Diagnosis Model
To enhance the precision of equipment fault diagnosis, a novel device fault diagnosis method based on factor analysis (FA) and an improved error back propagation (BP) algorithm has been proposed. An FA-BP neural network diagnosis model has been developed to enable intelligent diagnosis of equipment...
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| Veröffentlicht in: | 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA) S. 32 - 37 |
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11.08.2023
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| Abstract | To enhance the precision of equipment fault diagnosis, a novel device fault diagnosis method based on factor analysis (FA) and an improved error back propagation (BP) algorithm has been proposed. An FA-BP neural network diagnosis model has been developed to enable intelligent diagnosis of equipment faults. Firstly, three common factors influencing motor faults were extracted using FA, which served as effective input data for the BP neural network. Subsequently, the data that was optimized by factor analysis was utilized as the input layer of the BP neural network, and the data were trained and tested by employing Matlab software. Eventually, the results of fault diagnosis prediction obtained from the FA-BP neural network model were compared with those attained using the traditional BP neural network. The experimental findings affirm that the improved model achieves significantly elevated accuracy of equipment fault prediction and swifter training speed compared to the traditional error back propagation algorithm. |
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| AbstractList | To enhance the precision of equipment fault diagnosis, a novel device fault diagnosis method based on factor analysis (FA) and an improved error back propagation (BP) algorithm has been proposed. An FA-BP neural network diagnosis model has been developed to enable intelligent diagnosis of equipment faults. Firstly, three common factors influencing motor faults were extracted using FA, which served as effective input data for the BP neural network. Subsequently, the data that was optimized by factor analysis was utilized as the input layer of the BP neural network, and the data were trained and tested by employing Matlab software. Eventually, the results of fault diagnosis prediction obtained from the FA-BP neural network model were compared with those attained using the traditional BP neural network. The experimental findings affirm that the improved model achieves significantly elevated accuracy of equipment fault prediction and swifter training speed compared to the traditional error back propagation algorithm. |
| Author | Liu, Yue |
| Author_xml | – sequence: 1 givenname: Yue surname: Liu fullname: Liu, Yue email: 827219834@qq.com organization: Tianjin University of Technology,Tianjin,China |
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| Snippet | To enhance the precision of equipment fault diagnosis, a novel device fault diagnosis method based on factor analysis (FA) and an improved error back... |
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| StartPage | 32 |
| SubjectTerms | Analytical models Backpropagation BP neural network Computational modeling equipment fault diagnosis error back propagation factor analysis Fault diagnosis Neural networks Training Vibrations |
| Title | Application of Factor Analysis Optimization Error Back Propagation Algorithm FA-BP Neural Network Equipment Fault Diagnosis Model |
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