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Automatic Fault Detection and Isolation Method for Roller Bearing Using Hybrid-GA and Sequential Fuzzy Inference

1
Railway Technical Research Institute, Materials Technology Division, Applied Superconductivity Laboratory, Tokyo 185-8540, Japan
2
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
3
Graduate School of Environmental Science and Technology, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in: Kobayashi, Y., Tomita; M.; Song, L.; Chen, P. Automatic Diagnosis Method for Rolling Bearing Using Measured Signal from Distant Points. In Proceedings of the 2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Tsu, Japan, 10–12 September 2018.
Sensors 2019, 19(16), 3553; https://doi.org/10.3390/s19163553
Received: 5 July 2019 / Revised: 2 August 2019 / Accepted: 9 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Abstract

Though accelerometers for condition diagnosis of a bearing is preferably placed at the nearest position of the bearing as possible, in some plant equipment, the accelerometer is difficult to set near the diagnosed bearing, and in many cases, sensors have to be placed at a location far from the diagnosed bearing to measure signals for diagnosing bearing faults. Since, in these cases, the measured signals contain stronger noise than the signal measured near the diagnosed bearing, bearing faults are more difficultly to be detected. In order to overcome the above difficulty, this paper proposes a new fault auto-detection method by which the signals measured by an accelerometer located at a far point from the diagnosed bearing can be used to simply and accurately detect the bearing faults automatically. Firstly, the hybrid GA (the combination of genetic algorithm and tabu search) is used to automatically search and determine the optimum cutoff frequency of the high-pass filter to extract the fault signal of the abnormal bearing. Secondly, the bearing faults are precisely diagnosed by possibility theory and fuzzy inference. Finally, in order to demonstrate the effectiveness of these proposed methods, these methods were applied to bearing diagnostics using vibration signals measured at the far point of the diagnostic bearing, and the efficiency of these methods was verified by the results of automatic bearing fault diagnosis. View Full-Text
Keywords: condition diagnosis; bearing faults; hybrid-GA; noise cancelling; fuzzy inference condition diagnosis; bearing faults; hybrid-GA; noise cancelling; fuzzy inference
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kobayashi, Y.; Song, L.; Tomita, M.; Chen, P. Automatic Fault Detection and Isolation Method for Roller Bearing Using Hybrid-GA and Sequential Fuzzy Inference. Sensors 2019, 19, 3553.

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