关键词: 滚动轴承, 最小熵解卷积, 独立成分分析, 故障特征识别

Abstract: Aiming at the problem that multiple sensors collected multiple data synchronously for same vibration source,a combination denoising method based on Minimum-Entropy Deconvolution (MED) and Independent Components Analysis (ICA) was proposed and was used to extract cyclic impact feature of rolling bearing.MED was exploited to preprocess the vibrations from each sensor prior to eliminate the effect of vibration transfer path and enhance the cyclic shocks induced by bearing fault.The signals preprocessed by MED were fed to ICA for furthermore denoising where the resulted ICA component with biggest Kurtosis value was chosen as the finally filtered signal.From the envelop spectrum of the finally filtered signal obtained by a sequent use of MED and ICA,the health condition identification or fault localization could be achieved.The experiments on simulated and laboratorial data verified the conjunct application of MED and ICA within the context of multi-sensor condition monitoring of manufacturing equipment.

Key words: rolling bearing, minimum entropy deconvolution, independent components analysis, fault feature recognition