煤机设备轴承故障诊断方法  

Diagnosis method for bearing faults in coal mining equipment

作　　者：杨春才 李向磊 吕晓伟 YANG Chuncai;LI Xianglei;LYU Xiaowei(Wanli No.1 Mine,CHN Baotou Energy Co.,Ltd.,Ordos 017099,China)

机构地区：[1]国能包头能源有限责任公司万利一矿,内蒙古鄂尔多斯017099

出　　处：《工矿自动化》2023年第12期147-151,共5页Journal Of Mine Automation

基　　金：国家能源集团2020年第二批科技项目(GJNY-20-238)。

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。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.

关 键 词：煤机设备 轴承故障诊断 故障特征提取 局部特征尺度分解 奇异值分解 香农熵 

分 类 号：TD67[矿业工程—矿山机电]