A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition

At present, the multiscale fuzzy entropy has been verified to be an excellent measure of the complexity for dynamic time series. However, when using to short-time time series collected in practical application, the conventional multiscale fuzzy entropy may result in undefined or unreliable value. In this work, improved multiscale fuzzy entropy, named moving-average based multiscale fuzzy entropy (MA_MFE), is presented at first to potentially characterize the complexity of short-term time series. The MA_MFE algorithm can successfully produce more template vectors to overcome the problem of shortening the samples in the procedure of the existing approaches. The analysis experiments for both white noise signal and 1/f noise signal are made and the results show MA_MFE method is more effective for the short-term datasets. Then, a novel fault detection scheme has been developed. After using non-local mean approach to reduce background noise, the non-stationary vibration signals are decomposed into several intrinsic scale components (ISCs) by a newly developed time-frequency signal analysis method- partly ensemble local characteristic-scale decomposition (PELCD); The ISCs with higher correlation coefficients are used to reconstruct into a new signal and the inherent MA_MFEs are extracted to quantify the complexity of the collected vibration signal. At last, the multiSVM and improved variable predictive model based class discrimination (VPMCD) are employed as small-sample classifiers to achieve fault detection. Two experiments have been conducted, which include both rolling bearing as vital component in rotating machinery and a piston pump as typical reciprocation machinery in hydraulic system. The comparison results show that the proposed fault detection scheme is more effective and reliable and suitable for real-time online fault detection.