基于经验模式分解和1(1/2)维谱的客车轴承故障诊断

Fault Diagnosis of 25Passenger Car Bearing Using Empirical Mode Decomposition and 1(1/2) Dimension Spectrum

为了保证高速客车的行车安全,开展了25型客车轴承故障检测诊断方法研究,使用振动信号对客车车轴轴承进行早期诊断和预警。分析了客车轴承振动信号特征,针对轴承故障特征频率包含的能量少且受到噪声干扰的特点,将经验模式分解和112维谱引入轴承振动信号分析,提出了基于经验模式分解和1 21维谱提取故障特征频率的客车轴承诊断方法。试验表明,该方法能有效识别客车轴承早期故障。

For running safety of high-speed passenger car, a novel fault diagnostic system for 25 passenger car bearing is developed. Using the vibration signal, the initial faults of bearings are diagnosed on car running test. The characteristics of passenger car bearing vibration signal are analyzed. The vibration signature of the damaged bearing consists of an exponentially decaying sinusoid containing the bearing defect characteristic frequency. Since energy of the defect characteristic frequency is distributed at a very low level, it can be 1 easily masked by noise. Consequently, the bearing signal is preprocessed using empirical mode decomposition, and the 1 1/2 dimension spectrum is employed to exact the quadratic phase couple caused by initial faults of bearings. The effectiveness of proposed method was shown by running test of 25 passenger car bearing.