| 最优品质因子信号共振稀疏分解的往复压缩机故障诊断 |
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| 引用本文： | 王金东,卜庆超,赵海洋,张宏斌.最优品质因子信号共振稀疏分解的往复压缩机故障诊断[J].机械强度,2019,41(3):557-561. |
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| 作者姓名： | 王金东 卜庆超 赵海洋 张宏斌 |
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| 作者单位： | 东北石油大学机械科学与工程学院,大庆,163318;庆油田有限责任公司第四采油厂,大庆,163511 |
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| 基金项目： | 黑龙江省自然科学基金;黑龙江省自然科学基金 |
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| 摘 要： | 针对往复压缩机振动信息干扰耦合,振动信号非平稳、非线性的特性,提出了最优品质因子信号共振稀疏分解的往复压缩机故障诊断方法。该方法以信号共振稀疏分解得到的低共振分量峭度最大为目标,利用遗传算法与粒子群算法结合的混合算法对品质因子进行优化,得到最优品质因子;然后利用最优品质因子对往复压缩机振动信号进行信号共振稀疏分解,提取故障信息。实验及结果表明,该方法在往复压缩机轴承故障诊断方面效果显著。
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| 关 键 词： | 故障诊断 往复压缩机 共振稀疏分解 品质因子 轴承 |
FAULT DIAGNOSIS OF RECIPROCATING COMPRESSOR ON THE RESONANCE-BASED SPARSE SIGNAL DECOMPOSITION WITH OPTIMAL Q-FACTOR |
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| WANG JinDong,BU QingChao,ZHAO HaiYang,ZHANG HongBin.FAULT DIAGNOSIS OF RECIPROCATING COMPRESSOR ON THE RESONANCE-BASED SPARSE SIGNAL DECOMPOSITION WITH OPTIMAL Q-FACTOR[J].Journal of Mechanical Strength,2019,41(3):557-561. |
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| Authors: | WANG JinDong BU QingChao ZHAO HaiYang ZHANG HongBin |
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| Affiliation: | (Mechanical Science and Engineering Institute,Northeast Petroleum. University,Daqing 163318,China;No. 4 Oil Production Company of Daqing Oil Field Company Ltd,Daqing 163511,China) |
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| Abstract: | Reciprocating compressor vibration signal is typical nonlinear and non-stationary,and the vibration information interference coupling,owing to this problem,a fault diagnosis method of reciprocating compressor on the resonance-based sparse signal decomposition with optimal Q-factor was proposed.The method use resonance sparse decomposition to find the low resonance component which its kurtosis is maximum,optimize Q-factor with genetic algorithm and particle swarm optimization to get the optimal Q-factor;then use resonance sparse decomposition to decompose reciprocating compressor vibration signal by the optimal Q-factor;the result shows that this method can diagnose the oversized bearing clearance fault effectively. |
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| Keywords: | Fault diagnosis Reciprocating Compressor Resonance-based sparse signal decomposition Quality factor Bearing |
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