Bearing fault diagnosis based on harmonic matching compensation and keyless phase order tracking
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摘要: 煤矿机械设备轴承在强冲击、大载荷工况下产生的振动信号表现出强烈的瞬态非平稳与局部非线性特性。经典的时域统计分析方法和全局域变换方法难以识别故障特征；传统阶次跟踪方法存在设备安装不便、难以获取瞬时频率的问题；传统的无键相阶次跟踪方法在转速波动剧烈的条件下估计出的瞬时频率精度低，导致故障识别效果差。针对上述问题，提出了一种基于谐波匹配补偿和无键相阶次跟踪的轴承故障诊断方法。首先，利用基于谐波匹配补偿的时频分析方法对轴承振动信号进行处理，准确估计瞬时频率；其次，通过Vold-Kalman滤波方法自适应提取谐波分量信号；再次，采用Hilbert变换计算谐波的瞬时相位，进而获得时间域与角度域的映射关系，完成原始时间域信号在角度域的重采样；最后，对重采样的信号进行快速傅里叶变换，通过分析包络阶次谱，实现轴承故障特征识别。仿真和试验结果表明，该方法估计的瞬时频率与实际值之间的最大相对误差不超过1%，表征轴承故障特征阶次准确且明显，可有效诊断轴承故障。Abstract: The vibration signals of the bearings of coal mine machanical equipment under the working conditions of strong impact and heavy load show strong transient non-stationary and local nonlinear features. It is difficult to identify the fault features by the classical time-domain statistical analysis method and the global domain transformation method. The traditional order tracking method has the problems of inconvenient equipment installation and difficulty in obtaining instantaneous frequency. The traditional keyless phase order tracking method estimates the instantaneous frequency with low precision under the condition of severe speed fluctuation. This leads to poor fault identification effect. To solve these problems, a new method of bearing fault diagnosis based on harmonic matching compensation and keyless phase order tracking is proposed. Firstly, the time-frequency analysis method based on harmonic matching compensation is used to process the bearing vibration signal and estimate the instantaneous frequency accurately. Secondly, the Vold-Kalman filtering method is used to adaptively extract the harmonic component signal. Thirdly, the Hilbert transform is used to calculate the instantaneous phase of the harmonic. The mapping relationship between the time domain and angle domain is obtained, so as to complete the resampling of the original time domain signal in the angle domain. Finally, the resampled signals are processed by fast Fourier transform (FFT). The fault features of the bearing are identified by analyzing the envelope order spectrum. The simulation and experimental results show that the maximum relative error between the estimated instantaneous frequency and the actual value is less than 1%. The feature order of bearing fault is accurate and obvious, which can effectively diagnose the bearing fault.
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图 1 基于HMC和无键相阶次跟踪的轴承故障诊断流程
Figure 1. Bearing fault diagnosis process based on harmonic matching compensation and keyless phase order tracking
图 5 仿真信号的共振解调序列和包络
Figure 5. Resonance demodulation sequence and envelope of simulated signal
图 6 不同方法下仿真信号时频谱
Figure 6. Time-frequency spectrum of simulated signal under different methods
图 7 仿真信号瞬时频率估计结果
Figure 7. Instantaneous frequency estimation result of simulated signal
图 8 Vold-Kalman滤波器提取的谐波分量
Figure 8. Harmonic components extracted by Vold-Kalman filter
图 15 试验信号的共振解调序列和包络
Figure 15. Resonance demodulation sequence and envelope of test signal
图 16 不同方法下试验信号时频谱
Figure 16. Time-frequency spectrum of test signal under different methods
图 21 轴承内圈故障振动信号的包络阶次谱
Figure 21. Envelope order spectrum of vibration signal of bearing inner ring fault
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