改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。

Fault Detection Method Based on Improved Laplacian of Gaussian Operator

The weak fault features of wheel bearing are difficult to accurately detect because of the interference of strong background noise. Aiming at the problem, this paper presents a novel method named self adaptive improved Laplacian of Gaussian (ILoG) operator to detect the weak fault features of wheel bearing. The ILoG operator filter has excellent ability to detect the sudden change of signals, which is applied to detect the fault impulse characteristics in bearing fault signals. In addition, water cycle algorithm (WCA) with good optimization characteristic is used to search for the influencing parameters of ILoG operator in order to achieve the best filtering results. The envelope demodulation method is further used to analyze the best filtering signals of the optimized ILoG operator and extract weak fault features. The proposed method is applied to analyze wheel bearings with outer race and inner race fault, and the results show that this method can detect the weak fault characteristic frequencies of bearings effectively. The filtering effect is better than the wavelet threshold denoising and multi-scale morphological difference filter methods.