Automated and Adaptive Ridge Extraction for Rotating Machinery Fault Detection

Abstract

A ridge in a time-frequency graph (TFG) describes the relationship of a signal component's instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for assessing machine health conditions without rotational speed measurement. This article proposes a new automated and adaptive ridge extraction (AARE) method. The AARE develops an adaptive edge detection strategy to avoid excessive interferences when searching for a ridge. Besides, the AARE creates a balance between exploring peak amplitudes and guaranteeing a continuous curve through an adaptive core function, which is constructed entirely based on the instantaneous characteristics of the analyzed signal. The unique advantage of the proposed method is that it dispenses with the tuning of parameters and runs automatically. Thus, human intervention is minimized. Gear and bearing vibration signals collected under variable speed conditions are applied to investigate and demonstrate the performance of AARE. In addition, some challenging cases are analyzed and discussed. Results show that AARE has a superior performance in ridge extraction compared with the reported approaches.