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...

Full description

Saved in:
Bibliographic Details
Published in:International journal of automotive technology 2019, 20(5), 110, pp.989-996
Main Authors: Yan, Shu-fa, Ma, Biao, Zheng, Chang-song, Chen, Man
Format: Article
Language:English
Subjects:
Automotive Engineering
Condition monitoring
Data integration
Datasets
Degradation
Diagnostic systems
Engineering
Evaluation
Fault diagnosis
Mechanical transmissions
Permutations
Steering
Wear
Weight
자동차공학
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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, we 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.
ISSN:1229-9138
1976-3832
DOI:10.1007/s12239-019-0093-9