Fault detection via recurrence time statistics and one class classification
A methodology for fault detection in rotating machinery is presented.An original one class classifier based on extreme statistics (EVOC) is employed.One advantage of the method is a reduced number of hyperparameters to be adjusted.Another advantage is the use of only normal data of the machinery being monitored.The method shows higher classification accuracy than other state of the art methods. 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 high speed 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.
Pattern Recognit. Lett.