基于噪声利用HVD的矿井提升机轴承故障诊断研究

Study on Bearing Fault Diagnosis of Mine Hoist Based on Noise Utilization HVD

为了高效、准确地监测强噪声背景下矿井提升机轴承的运行状态,提出了一种基于噪声利用的希尔伯特振动分解(HVD)方法,在提升机轴承故障诊断中对振动信号进行分析。首先根据随机共振(SR)的小参数要求,对振动信号进行频移变换,利用四阶龙格库塔求得模型输出,实现噪声的利用。然后经过HVD处理,选择有用的分解信号进行故障诊断。与直接采用HVD方法相比,模拟和实验结果表明,该方法能有效、准确地识别出强噪声背景下提升机轴承的微弱故障。

In order to effectively and accurately monitor the running state of mine hoist bearing under the background of strong noise, proposed a Hilbert vibration decomposition(HVD) method based on noise utilization to analyze the vibration signal in the fault diagnosis of mine hoist bearing. Firstly according to the small parameter requirements of stochastic resonance(SR), the vibration signal is frequency shifted, and the model output is obtained by using fourth-order Runge Kutta to realize the utilization of noise. Then after HVD processing, the useful decomposition signal is selected for fault diagnosis. Compared with the direct HVD method, the simulation and experimental results show that this method can effectively and accurately identify the weak fault of hoist bearing under strong noise background.