Diagnosis method for bearing faults in coal mining equipment

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.

Keywords

• coal mining equipment
• bearing fault diagnosis
• fault characteristic extraction
• local characteristic scale decomposition
• singular value decomposition
• shannon entropy