Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest

While there have been extensive studies in the health assessment of the bearings using the vibration signal, most have focused on the constant operating conditions. The field, however, operates often under time-varying conditions as is the case of the automotive wheel bearing. In this study, a novel health indicator (HI) is proposed to address this problem based on the Isolation Forest algorithm, which was originally developed for anomaly detection. The method is advantageous in two aspects: the HI is not influenced by the type of operating conditions whether it is constant or time-varying. Only the data under normal condition are used to construct the HI without the need of run-to-failure data. The method is demonstrated by the three cases with different types of bearing and operating conditions ranging from constant to the highly variable conditions. As a result, monotonic trends are obtained for the HI in all cases, which may be useful for the prognostic monitoring. Furthermore, in comparison with the HI constructed by the run-to-failure data in the previous literature, it is found that the trends of HI agree reasonably well with each other, which supports the validity of the method.