Please use this identifier to cite or link to this item:
Title: Intelligent diagnosis of bearing knock faults in internal combustion engines using vibration simulation
Authors: Chen, Jian
Randall, Robert Bond
First Published: 8-Jun-2016
Publisher: Elsevier, for International Federation for the Promotion of Mechanism and Machine Science
Citation: Mechanism and Machine Theory, 2016, 104, pp. 161-176
Abstract: 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.
DOI Link: 10.1016/j.mechmachtheory.2016.05.022
ISSN: 0094-114X
eISSN: 1873-3999
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier, for International Federation for the Promotion of Mechanism and Machine Science 2016. This version of the paper is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Published Articles, Dept. of Engineering

Files in This Item:
File Description SizeFormat 
MMT full paper2016 rev_final.pdfPost-review (final submitted author manuscript)606.08 kBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.