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A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion

1
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
2
Institut de Recherche Dupuy de Lôme UMR CNRS 6026 IRDL, University of Brest, Brest 29238, France
3
Group of Electrical Engineering Paris UMR CNRS 8507, CentraleSupelec, Univ. Paris Sud, Sorbonne Université, Gif-sur-Yvette 91192, France
*
Author to whom correspondence should be addressed.
Information 2019, 10(3), 116; https://doi.org/10.3390/info10030116
Received: 11 January 2019 / Revised: 12 March 2019 / Accepted: 13 March 2019 / Published: 17 March 2019
(This article belongs to the Special Issue Fault Diagnosis, Maintenance and Reliability)
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Abstract

This 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
Keywords: multi-model; self-learning; fusion and decision; fault diagnosis multi-model; self-learning; fusion and decision; fault diagnosis
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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.

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