Abstract
Recently, the use of machines working under time varying load and speed is increasing rapidly in modern industry. Fault diagnosis of these technical objects plays an important role but faces great challenges, since vibration signals are mostly non-stationary due to uncertainties affected by the change of speed and load during operation. This study aims to propose and verify a signal analysis procedure, namely, a combination of order tracking technique, cepstrum analysis and a classification algorithm using support vector machine to automatically detect severe faults in rotating machines working in non-stationary conditions. The experimental example in a test rig demonstrates that the proposed method is very quick and technically simple, and can be used for monitoring the condition of a helical gearbox during speed up.
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Dien, N.P., Du, N.T. (2020). Fault Detection for Rotating Machines in Non-stationary Operations Using Order Tracking and Cepstrum. In: Sattler, KU., Nguyen, D., Vu, N., Tien Long, B., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2019. Lecture Notes in Networks and Systems, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-030-37497-6_41
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DOI: https://doi.org/10.1007/978-3-030-37497-6_41
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