摘要
对齿轮的运行状态进行监测能够有效消除设备的潜在故障,避免大的事故发生.然而由于工作条件恶劣,很难采集到齿轮箱真实可靠的振动信号.由此,提出了一种基于独立分量分析(ICA)和支持向量机(SVM)相结合的齿轮故障诊断方法.首先利用ICA从被噪声污染的齿轮箱振动信号中分离出真实振动源信号,以提取可靠的齿轮状态高阶统计特征信息;而为了准确监测设备运行状态,采用支持向量机(SVM)对所提取的特征进行学习与智能分类,以检测齿轮早期故障.结果表明,通过齿轮箱故障实验分析,所提出的方法能够有效提取齿轮振动源信号,准确识别与诊断齿轮裂纹、点蚀以及断齿等故障状态,且精度比未进行源分离高10%,具有较好工业应用价值.
In this paper,a condition monitoring and faults identification technique for rotating machineries based on independent component analysis（ICA） and support vector machine（SVM） is described.In the diagnosis process,the ICA was initially employed to separate characteristic vibration signal and interference vibration signal from the parallel time series obtained from multi-channel accelerometers mounted on different positions of the gearbox.Then the ICA features could be obtained from the characteristic signal.Finally,the SVM was implemented in the pattern recognition process to identify the conditions of the gears of interest.The experimental results suggest that the sensitive fault features can be extracted efficiently after the ICA processing,and the proposed diagnostic system is effective for the gear multi-fault diagnosis,including the gear crack failure,pitting failure,gear tooth broken,etc.In addition,the proposed method can achieve better performance than that without ICA separation processing with regard to the classification rate.
出处
《湖北工业大学学报》
2011年第4期24-28,共5页
Journal of Hubei University of Technology
基金
国家自然科学基金资助项目(50975213)
高等学校学科创新引智计划(B08031)
