Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques

Abstract

Rolling element bearings are one of the most common mechanical components used in a wide variety of rotating systems. The performance of these systems is closely associated with the health of bearings. In this study, a nonlinear time series analysis method, i.e., recurrence analysis is utilized to assess the health of bearings using time domain data. The recurrence analysis acquires the quantitative measures from the recurrence plots and provides an insight to the system under investigations. Experiments are performed to generate the vibration data from the healthy and faulty bearing. Eight recurrence quantitative analysis measures and five time-domain measures are used for the investigations. Three artificial intelligence techniques: rotation forest, artificial neural network, and support vector machine are employed to quantify the diagnosis performance. Results highlight the ability of recurrence analysis to identify the health state of the bearing at the early stage and superior diagnosis accuracy of the proposed methodology.