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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qiufang Wang Zhong Zhang Ke Bao Shulin Liu Yuanyuan Zhao Yue Liu |
| Copyright Year | 2015 |
| Description | Author affiliation: Overall Tech. Dept., China North vehicle Res. Inst., Beijing, China (Qiufang Wang; Zhong Zhang; Ke Bao; Shulin Liu; Yuanyuan Zhao; Yue Liu) |
| Abstract | The widely use of the new technique and the new material make the vehicle's structure gradually complicated. For the complicated equipment, the wear state of the mechanism is hard to estimate. And it often makes the overly maintenance or insufficient maintenance. This paper discusses the failure diagnosis technology based on oil detection and studies the key technology of the oil detection, the construction method of the detection system, and the extraction method of the oil status wear parameters. Through the debris analysis and the performance testing for the collected oil sample, the technical condition of the vehicle can be judged and predicted by comparing and analyzing the test data with the boundary value of the oil detection parameters. The pollution control of the lubricant oil and the oil deterioration control can make the vehicle keep good lubrication state, and it's the efficiency measure to reduce or avoid the happening probability of the abnormal wear and tear for the vehicle. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 169495 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781467385541 |
| DOI | 10.1109/PHM.2015.7380091 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gears Friction PHM Maintenance engineering Oil Detection Timing Reliability Artificial intelligence Failure diagnosis Vehicles |
| Content Type | Text |
| Resource Type | Article |
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