An improved local characteristic-scale decomposition to restrict end effects, mode mixing and its application to extract incipient bearing fault signal

Empirical mode decomposition (EMD) adaptively decomposes a signal into a linear combination of waveforms embedded in the signal and has been successfully applied to many engineering areas including mechanical fault diagnosis. Local characteristic-scale decomposition (LCD) defines a new baseline signal and then conducts the sifting process to achieve decomposition. LCD utilizes the inherent instantaneous amplitude/frequency/ phase information and morphological features of signals leading to a set of powerful adaptive signal filters. However, LCD suffers from the problems of end effects and mode mixing inheriting from the sifting process. This paper proposes an improved LCD (ILCD) method to reduce the two problems. First, the equivalent filter and equivalent impulse response of LCD are analyzed and compared with EMD and intrinsic time-scale decomposition (ITD). Furthermore, the ILCD deals with the problem of end effects based on support vector regression (SVR) boundary condition processing method and resolves the problem of mode mixing using complementary partial noise assisted method (CPNAM). Simulation case studies are used to verify the improvements of the proposed method. Finally, the ILCD is applied to extract bearing fault signals for an experimental bearing under variable speed condition and a practice case of wind power bearing. The analysis results from the applications demonstrate that the proposed algorithm is effective and robust in extracting incipient bearing fault signals. (c) 2021 Elsevier Ltd. All rights reserved.