Du Wenliao 1, Myeongsu Kang 2, and Michael Pecht 2
1 School of Mechanical and Electronic Engineering, Zhengzhou University of Light Industry, 117776 Zhengzhou China 450002
2 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
Multifractal detrended fluctuation analysis (MF-DFA) has been used for vibration-based fault diagnosis because it is able to uncover multifractality buried in nonlinear and nonstationary vibration signals and thus offers an opportunity to explore a new set of multifractal features for fault diagnosis. However, the choice of detrending polynomial orders is one of the major concerns in MF-DFA because improper polynomials can cause the underfitted or overfitted scale-dependent trend of the signals. To address this issue, adaptive MF-DFA (AMF-DFA) was developed. More specifically, the developed AMF-DFA uncovers multifractality of the signals by adaptively extracting a variable number of scale-dependent fluctuations present in the signals and automatically eliminating irrelevant trend components to the fundamental structure of the signals based on correlation analysis. Accordingly, the developed AMF-DFA does not require a priori knowledge (i.e., detrending polynomial order). The effectiveness of the developed AMF-DFA was verified for fault diagnosis applications.