A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion
AbstractThis paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%. View Full-Text
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Wang, T.; Dong, J.; Xie, T.; Diallo, D.; Benbouzid, M. A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion. Information 2019, 10, 116.
Wang T, Dong J, Xie T, Diallo D, Benbouzid M. A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion. Information. 2019; 10(3):116.Chicago/Turabian Style
Wang, Tianzhen; Dong, Jingjing; Xie, Tao; Diallo, Demba; Benbouzid, Mohamed. 2019. "A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion." Information 10, no. 3: 116.
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