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Intelligent diagnosis of bearing knock faults in internal combustion engines using vibration simulation

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journal contribution
posted on 04.06.2018, 08:33 by Jian Chen, Robert Bond Randall
Big-end bearing knock faults in IC engines can be considered as a real industrial case of a slider-crank mechanism including a joint with clearance and lubrication. In this paper, an Artificial Neural Network (ANN) based system was used to solve the problem of intelligent big-end bearing knock fault diagnosis in Internal Combustion (IC) engine. But when the ANN is used in machine condition monitoring, it is either unlikely or uneconomical to experience all different real faults to generate sufficient training data. Therefore, model based method should be a viable way to generate adequate data to train the networks for the intelligent big-end bearing fault diagnosis in IC engines. In order to evaluate and update the simulation model, experiments with normal bearing clearance and with different oversize bearing clearances were first carried out on the engine test rig. It was found that the relevant diagnostic information lies in the squared envelope of the vibration signals. Therefore, we only need build a proper simulation model to simulate the correct envelope signals rather than the raw vibration signals. As the important inputs of the simulation model, the inertia properties of the simulated engine components were also measured and studied. Next, we built an ANN-based bearing knock diagnosis system which consists of three phases: fault detection phase, fault localization phase and fault severity identification phase. Particularly, a saturating linear function is selected as the transfer function of the fault severity identification stage, so the networks can linearly classify the fault levels and the output is more in agreement with the reality in industry. Following the feature extraction and selection from the processed squared envelope signals, the networks were purely trained by the simulated data with normal bearing clearance and with different oversize bearing clearances. Finally the networks was tested by the real experimental data and it was demonstrated that the networks can successfully detect different bearing knock faults in real tests, and also classify the faults' location and severity levels.

Funding

The authors would like to convey their special gratitude to the Australian Research Council and LMS International for sponsoring this research under Linkage Project LP0883486.

History

Citation

Mechanism and Machine Theory, 2016, 104, pp. 161-176

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

AM (Accepted Manuscript)

Published in

Mechanism and Machine Theory

Publisher

Elsevier, for International Federation for the Promotion of Mechanism and Machine Science

issn

0094-114X

eissn

1873-3999

Acceptance date

30/05/2016

Copyright date

2016

Available date

04/06/2018

Publisher version

https://www.sciencedirect.com/science/article/pii/S0094114X1630088X

Language

en