Application of maximum correlated Kurtosis deconvolution on bearing fault detection and degradation analysis
Year: 2014
Type: article
Abstract: As the key techniques of condition-based maintenance, fault detection, diagnosis, and prognosis become the hot research topic in recent decades. Earlier the faults detected, the lead time will be enough for maintenance actions. The correct maintenance actions at correct time are determined by the good prognosis ability, in other words, accurate remaining useful life (RUL) prediction ability. For bearing degradation, the faults always lead to impulse shock of the bearing. The vibration signals of fault bearing are more impulsive than the normal bearing. Based on this knowledge, maximum correlated Kurtosis deconvolution (MCKD) which has been used in gear fault detection is applied to bearing fault detection and degradation analysis. Compared to the minimum entropy deconvolution (MED), this method can enhance the impulsive signal of bearing fault more effective. This enables the bearing fault detection easier and can detect some incipient faults. For RUL prediction, it can find the degradation change point earlier. This is very useful for RUL prediction. Finally, an implemented bearing fault experiment and a run-to-failure bearing experiment are used to demonstrate the effectiveness of the MCKD in bearing fault detection and degradation analysis.
Source: Vibroengineering PROCEDIA
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W133299059