Next Article in Journal
A Photoluminescent Colorimetric Probe of Bovine Serum Albumin-Stabilized Gold Nanoclusters for New Psychoactive Substances: Cathinone Drugs in Seized Street Samples
Previous Article in Journal
Research on Filtering Algorithm of MEMS Gyroscope Based on Information Fusion
Previous Article in Special Issue
A Novel Fault Feature Recognition Method for Time-Varying Signals and Its Application to Planetary Gearbox Fault Diagnosis under Variable Speed Conditions
Article Menu

Export Article

Open AccessArticle

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

Railway Technical Research Institute, Materials Technology Division, Applied Superconductivity Laboratory, Tokyo 185-8540, Japan
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
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;
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)
PDF [5092 KB, uploaded 15 August 2019]
  |     |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top