Intelligent Fault Diagnosis of Engine Based on PCA-SOM. (January 2020)

• Record Type: Journal Article
• Authors: Zhang, Yangyang; Jia, Yunxian; Guo, Chiming; Wu, Weiyi
• Is Part Of: Journal of physics. Volume 1453(2020)
• Publication Date: 2020-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1453/1/012022
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Self-Organizing Feature Map (SOM) is a kind of self-organizing and self-learning network without teacher. It is mainly used for pattern recognition and region classification of input vectors. A fault diagnosis method of engine fuel supply system based on SOM neural network is proposed. The sensor is used to monitor the fuel pressure waveform of a certain engine fuel supply system, time domain analysis and feature extraction are carried out on the waveform, and the feature dimension reduction is realized by PCA to form the input vector of SOM neural network. The SOM neural network is used to establish the diagnosis model and then recognize fault patterns for test samples. The results of pattern recognition show that SOM neural network can identify and classify faults accurately, and it has certain engineering application value.