Fault detection via recurrence time statistics and one-class classification

Predictive maintenance has emerged as a fundamental practice to preserve production assets in many industrial environments. Of a wide set of approaches, vibration analysis is one of the most used for highspeed rotating machinery, especially when fault detection is to be automatic. Traditionally, this task has been studied as a classification problem using data extracted from the frequency domain. This approach, however, has two main limitations: (a) manufacture and mounting procedures can vary the vibration spectra of a machine, even when these share the same design; and (b) incipient fault signatures may be concealed in the frequency domain by noise and vibration from other parts of the system. For these reasons, the application of a classifier obtained for one machine to another machine is pointless, making early fault detection difficult. In this paper, a bearing fault detection problem is tackled using one-class classifiers and features extracted from vibration capture in the time domain using recurrence time statistics. We also describe a study of the behavior of the proposed method in real conditions. Our method shows high detection accuracy accompanied by a reduced number of false positives and negatives. (C) 2016 Elsevier B.V. All rights reserved.