| 基于小波分形技术提取发动机曲轴轴承故障特征 |
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| 引用本文： | 耿纪洲,程广涛,王成恺.基于小波分形技术提取发动机曲轴轴承故障特征[J].工业仪表与自动化装置,2013(4):116-120. |
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| 作者姓名： | 耿纪洲 程广涛 王成恺 |
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| 作者单位： | [1]山东省公安厅,济南250001 [2]济南军区汽车技工训练队,济南250023 |
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| 摘 要： | 利用小波技术对发动机曲轴轴承振动信号进行分解,对特定层的信号进行重构,并计算重构信号的分形维数,来实现发动机曲轴不同技术状态下特征提取。实验结果表明,特定频率带振动信号的分形维数更能敏感反应发动机曲轴轴承技术状态,它可以作为诊断发动机曲轴轴承故障的一个重要特征量。
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| 关 键 词： | 小波分析 分形维数 曲轴轴承 故障诊断 |
Extraction of crankshaft bearing fault characters based on wavelet fractal technology |
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| GENG JizhouI,CHENG Guangtao,WANG Chengkai.Extraction of crankshaft bearing fault characters based on wavelet fractal technology[J].Industrial Instrumentation & Automation,2013(4):116-120. |
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| Authors: | GENG JizhouI CHENG Guangtao WANG Chengkai |
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| Affiliation: | 1. Department of Public Security of Shandong Province, Ji＇nan 250001, China ; 2. Ji＇nan Military Automechanic Training Team, Ji＇nan 250023, China) |
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| Abstract: | In this paper, the wavelet transform technology was applied to analyze the crankshaft bear- ing vibration signal, then is reconstructed the specifically vibration series of signal, and fractal dimension of them is computed to pick up the fault characteristic in different technology state of the crankshaft bear- ing. The experiment result shows that the fractal dimension of the vibration signal of specifically frequency bands can reflect the technology state of the crankshaft bearing, and can be as an important characteristic parameter to diagnose the crankshaft bearing fault. |
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| Keywords: | wavelet analysis fractal dimension crankshaft bearing fault diagnosis |
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