Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

作者：Wang, JiaQing; Xiao, Han^*; Lv, Yong; Wang, Tao; Xu, Zengbing

来源：Shock and Vibration, 2016, 2016: 3409897.

DOI：10.1155/2016/3409897

• RESCALED RANGE ANALYSIS
• FAULT-DIAGNOSIS
• CROSSOVER CHARACTERISTICS

This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear's fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed.

• 出版日期2016
• 单位武汉科技大学