Fault detection via recurrence time statistics and one-class classification.
Pattern Recognition Letters(2016)
摘要
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.
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关键词
Vibration analysis,Recurrence time statistics,Machinery fault detection,One-class classifiers
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