Improved ALIF and its application to rolling bearing fault diagnosis

Abstract

Abstract Adaptive Local Iterative Filtering (ALIF) is a commonly used signal processing method. However, when applied to rolling bearing fault diagnosis, ALIF has been found to have problems such as abnormal interruption, serious mode aliasing, and inconvenient parameter setting. To address these issues, this study proposed an improved Adaptive Local Iterative Filtering (IALIF) method. Based on another assumption, IALIF not only effectively guarantees the stability of the decomposition and the reliability of the results by adaptively dividing the distance and de-oscillation by extreme envelope form, but also provides more convenient parameter setting and effectively suppresses the problem of modal aliasing. In addition, IALIF adds a stop criterion, which greatly improves the efficiency of the algorithm. Finally, the analysis results of simulation signals and experimental signals show that IALIF has good decomposition performance and is practical for rolling bearing fault diagnosis.