Diagnosis method for bearing faults in coal mining equipment
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摘要:
煤机设备滚动轴承早期故障特征微弱，且易受载荷、工况等因素的影响而被噪声淹没，导致轴承故障诊断困难。现有研究大多采用单一算法处理轴承故障信号，故障特征提取精度和故障诊断准确性有待进一步提高。提出了一种融合局部特征尺度分解（LCD）和奇异值分解（SVD）的煤机设备轴承故障诊断方法：采用LCD方法将煤机设备轴承振动信号分解为若干个内凛尺度分量（ISC），实现信号初步降噪；计算各ISC的香农熵，选择香农熵最小的ISC进行SVD，并构建SVD信号的奇异值差分谱，针对最大突变分量进行信号重构，实现信号增强去噪；对重构信号进行Hilbert包络解调，得到轴承故障特征频率，进而判断轴承故障。采用现场实测数据对基于LCD−SVD的煤机设备轴承故障诊断方法进行验证，结果表明，该方法可准确提取出轴承故障特征频率，从而实现煤机设备轴承早期故障诊断。
Abstract:The early fault characteristics of rolling bearings in coal mining equipment are weak, and they are easily affected by factors such as load and working conditions. The characteristics can be submerged by noise, making bearing fault diagnosis difficult. Most existing research uses a single algorithm to process bearing fault signals, and the accuracy of fault characteristic extraction and fault diagnosis needs to be further improved. A fault diagnosis method for coal mining equipment bearings is proposed, which combines local characteristic-scale decomposition (LCD) and singular value decomposition (SVD). The LCD method is used to decompose the vibration signal of coal mining equipment bearings into several intrinsic scale components (ISC), achieving preliminary signal denoising. The method calculates the Shannon entropy of each ISC, selects the ISC with the smallest Shannon entropy for SVD. The method constructs the singular value difference spectrum of the SVD signal. The method reconstructs the signal for the maximum abrupt component to achieve signal enhancement and denoising. The method performs Hilbert envelope demodulation on the reconstructed signal to obtain the characteristic frequency of bearing faults, and then determine the bearing faults. The on-site measured data is used to validate the bearing fault diagnosis method of coal mining equipment based on LCD-SVD. The results show that this method can accurately extract the characteristic frequency of bearing faults, thereby achieving early fault diagnosis of coal mining equipment bearings.
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图 1 煤机设备轴承振动信号LCD原理
Figure 1. Local characteristic-scale decomposition（LCD） principle of vibration signal of coal machine bearing
图 2 基于LCD–SVD的煤机设备轴承故障诊断流程
Figure 2. Fault diagnosis flow ofcoal machine bearing based on LCD and singular value decompostion（SVD）
图 4 减速机轴承振动信号时域波形及其幅值谱
Figure 4. Temporal waveform and amplitude spectrum of vibration signal of reducer bearing
图 6 ISC1的SVD信号奇异值及其差分谱（前100个点）
Figure 6. Singular value and its difference spectrum of SVD signal of ISC1 （former 100 points）
表 1 ISC1—ISC6香农熵
Table 1. The Shannon entropy of ISC1-ISC6
ISC ISC1 ISC2 ISC3 ISC5 ISC6 香农熵 4.386 4 5.708 3 5.480 8 5.652 9 5.504 0 
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