Weighted Evidential Fusion Method for Fault Diagnosis of Mechanical Transmission Based on Oil Analysis Data
Condition monitoring (CM) and fault diagnosis are critical for the stable and reliable operation of mechanical transmissions. Mechanical transmission wear, which leads to changes in the physicochemical properties of the lubrication oil and thus severe wear, is a slow degradation process that can be monitored by oil analysis, but the actual degradation degree is difficult to evaluate. To solve this problem, the authors propose a new weighted evidential data fusion method to better characterize the degradation degree of the mechanical transmission through the fusion of multiple CM datasets from oil analysis. This method includes weight allocation and data fusion steps that lead to a more accurate data-based fault diagnostic result for CM. First, the weight of each evidence is modeled with a weighted average function by measuring the relative scale of the permutation entropy from each CM dataset. Then, the multiple CM datasets are fused by the Dempster combination rule. Compared with other evidential data fusion methods, the proposed method using the new weight allocation function seems more reasonable. The rationality and superiority of the proposed method were evaluated through a case study involving an oilbased CM dataset from a power-shift steering transmission.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/12299138
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Supplemental Notes:
- Copyright © 2019, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.
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Authors:
- Yan, Shu-fa
- Ma, Biao
- Zheng, Chang-song
- Chen, Man
- Publication Date: 2019-10
Language
- English
Media Info
- Media Type: Digital/other
- Features: References;
- Pagination: pp 989-996
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Serial:
- International Journal of Automotive Technology
- Volume: 20
- Issue Number: 5
- Publisher: Korean Society of Automotive Engineers
- ISSN: 1229-9138
- EISSN: 1976-3832
- Serial URL: http://link.springer.com/journal/12239
Subject/Index Terms
- TRT Terms: Chemical analysis; Condition surveys; Data fusion; Deterioration; Fault location; Fault monitoring; Lubricating oils; Transmissions; Wear
- Identifier Terms: Dempster-Shafer theory
- Subject Areas: Data and Information Technology; Highways; Materials; Vehicles and Equipment;
Filing Info
- Accession Number: 01719944
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 22 2019 2:42PM