Fault severity assessment of rolling bearing based on optimized multi-dictionaries matching pursuit and Lempel-Ziv complexity
| Autor: | Peng-Fei Dang, Baogang Wen, Ming-Gang Wang, Qingkai Han, Zheng-Xin Yang |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Bearing (mechanical) Computer science Applied Mathematics 020208 electrical & electronic engineering 02 engineering and technology Fault severity Fault (power engineering) Matching pursuit Signal Computer Science Applications law.invention Vibration 020901 industrial engineering & automation Control and Systems Engineering law 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) Lempel-Ziv complexity Electrical and Electronic Engineering Instrumentation Algorithm |
| Zdroj: | ISA transactions. 116 |
| ISSN: | 1879-2022 |
| Popis: | For the safe working of rolling bearing, this paper presents a fault severity assessment method through optimized multi-dictionaries matching pursuit (OMMP) and Lempel-Ziv (LZ) complexity. To solve the redundancy problem of over-complete dictionary, the OMMP is proposed by introducing the quantum particle swarm optimization into matching pursuit for best representing the original vibration signal. And then, LZ complexity is calculated as an index of fault severity assessment by reconstructed signal. The performance of assessment method is verified through the measured signals of three bearing tests, and the comparisons with various methods are specifically described. The results indicate that the OMMP method averagely takes the shortest running time for the vibration signal decomposition. The assessment method is able to effectively evaluate different fault sizes of rolling bearing, and has a great applicability to in the condition-based maintenance of rotating machineries. |
| Databáze: | OpenAIRE |
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