Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems

The raw vibration signal of a faulty mechanical component carries a large amount of information reflecting its health condition, and the faulty information is typically carried by the high-frequency term in the vibration signal. However, the high-frequency term can easily by overwhelmed by the interference from the low-frequency term and noise. Considering the elimination of interference of the low-frequency term a novel preprocessing technique is presented, one-level kernel regression residual decomposition (KRRD), which can be used to extract the high-frequency term from the raw vibration signal to track the fault information. Combined with the synchroextracting transform (SET) technique, a one-level KRRD-based SET method is proposed. First, the high-frequency term in the raw vibration signal, which contains the faulty information, is extracted using one-level KRRD. Then, the high-frequency term is purified using SET, and the signal-to-noise ratio (SNR) is increased. Finally, a Hilbert envelope analysis is applied to the purified signal to demodulate the faulty feature frequency. To validate the performance and necessity of the proposed method, numerical simulations and experimental investigations are conducted. By introducing two commonly used methods, i.e., empirical mode decomposition (EMD) and variational mode decomposition (VMD), four comparisons (KRRD & EMD, KRRD & VMD, EMD, VMD) are conducted, and the superiority of the proposed method is verified. The analysis results show the effectiveness of the one-level KRRD-based SET method for the detection of mechanical component faults (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.