Condition monitoring of ball bearings using estimated AR models as logistic regression features

Bearings are one of the most common components in automatic machines. Diagnosis and prognosis of their working condition is crucial for minimization of downtime and maintenance costs. Different approaches may be adopted to either solve or mitigate the problem of identifying incipient faults during machinery operations. In this paper, we propose a simple and efficient yet effective method to solve this problem by exploiting the edge-computing capabilities of PLCs. Accelerometer signals are modeled as AutoRegressive (AR) processes whose coefficient are used as features for machine learning, based on logistic regression algorithm (LR), to perform Fault Detection and Isolation (FDI). Estimation and prediction are both implementable on-board the PLC, while machine learning can be carried out remotely, in a cloud computing perspective. The exploitation of AR modelling gives a simple and inherent methodology for feature selection. We apply the procedure to the Case Western Reserve University database, a widely known and used benchmark, to highlight its performance with respect to similar fault recognition techniques.