Imperial College London

DrAmirKadiric

Faculty of EngineeringDepartment of Mechanical Engineering

Reader in Mechanical Engineering
 
 
 
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Contact

 

a.kadiric Website

 
 
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Assistant

 

Mrs Chrissy Stevens +44 (0)20 7594 7064

 
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Location

 

672City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gouda:2019:10.1177/1350650119838896,
author = {Gouda, K and Rycerz, P and Kadiric, A and Morales-Espejel, G},
doi = {10.1177/1350650119838896},
journal = {Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology},
pages = {1523--1540},
title = {Assessing the effectiveness of data-driven time-domain condition indicators in predicting the progression of surface distress under rolling contact},
url = {http://dx.doi.org/10.1177/1350650119838896},
volume = {233},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Condition monitoring of machine health via analysis of vibration, acoustic and other signals offers an important tool for reducing the machine downtime and maintenance costs. The key aspect in this process is the ability to relate features derived from the recorded sensor signals to the physical condition of the monitored asset in real time. This paper uses simple machine learning techniques to examine the ability of specific time-domain features obtained from vibration signals to predict the progression of surface distress in lubricated, rolling-sliding contacts, such as those found in rolling bearings and gears. Controlled experiments were performed on a triple-disc rolling contact fatigue rig using seeded-fault roller specimens where micropitting damage was generated and its progression directly observed over millions of contact cycles. Vibration signals were recorded throughout the experiments. Features known as condition indicators were then extracted from the recorded time-domain signals and their evolution related to the observed physical state of the associated specimens using simple machine learning techniques. Five time-domain condition indicators were examined, peak-to-peak, root-mean-square, kurtosis, crest factor and skewness, three of which were found not to be redundant. First, a classification model using KNN nearest neighbor was built with the three informative condition indicators as training data. The cross-validation results indicated that this classifier was able to predict the presence of micropitting damage with a relatively high precision and a low rate of false positives. Secondly, a k-means clustering analysis was performed to measure the significance of each condition indicator by leveraging patterns. The peak-to-peak condition indicator was found to be a good predictor for progression of micropitting damage. In addition, this indicator was able to distinguish between micropitting and pitting failure modes with a high success rate. Finally
AU - Gouda,K
AU - Rycerz,P
AU - Kadiric,A
AU - Morales-Espejel,G
DO - 10.1177/1350650119838896
EP - 1540
PY - 2019///
SN - 1350-6501
SP - 1523
TI - Assessing the effectiveness of data-driven time-domain condition indicators in predicting the progression of surface distress under rolling contact
T2 - Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
UR - http://dx.doi.org/10.1177/1350650119838896
UR - http://hdl.handle.net/10044/1/69866
VL - 233
ER -