Automatic damage identification of roller bearings and effects of sifting stop criterion of IMFs

Damage identification of roller bearings has been deeply developed to detect faults using vibration-based signal processing. Empirical mode decomposition (EMD) is one of the recent techniques adapted to this purpose; it decomposes a multi-component signal into some elementary Intrinsic Mode Functions (IMFs). Although the EMD has been applied in various applications successfully, there are some drawbacks such as lack of a mathematical base, no robust stopping criterion for sifting process, mode mixing and border effect problem. One of the most relevant drawbacks in fault diagnosis is the sifting stop criterion. Although sifting as many times as possible is needed to decompose the signal, too many sifting steps will reduce the physical meaning of IMFs which are extremely important for fault diagnosis. Thus, a precise criterion is required to identify the appropriate stage to conclude the sifting process. The proposed criteria so far are: Cauchy-type convergence, Mean fluctuations thresholds, Energy difference tracking, Resolution factor, Bandwidths, and Orthogonality criterion. Effects of sifting stop criterion on damage identification performance has not studied thoroughly yet. In this study the influence of different stopping criteria on automatic fault diagnosis is investigated. Vibration signals were acquired using the test rig assembled by Dynamics & Identification Research Group (DIRG) at Department of Mechanical and Aerospace Engineering, Politecnico di Torino. Various operating conditions were considered to obtain reliable results. By extracting feature vectors for each decomposing algorithms, the accuracy of defect detection is examined by labeling the samples whether they are healthy or faulty using support vector machine (SVM).