Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis

Abstract

The sparse decomposition based on basic pursuit is an adaptive sparse expression of the signals. An adaptive basic pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. In the first, a new dictionary model is constructed according to the characteristics and mechanism of rolling bearing faults. Rotational speed of bearing, dimensions of bearing and bearing fault situation, among other parameters incorporates the new mode which could simulate the real fault impulse in different fault levels. Secondly a new method of dictionary establishment called adaptive impulse dictionary combine with basic pursuit introduced in this paper for bearing fault diagnosis. Adaptive impulse dictionary is created by changing characteristic parameters progressively, which make the dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The basic pursuit algorithm of an adaptive impulse dictionary is adopted to analyze experiment signal of the early-stage fault signals and composite fault signals of the bearing. The results demonstrate the effectiveness of the basic pursuit algorithm that uses the adaptive impulse dictionary.