Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/42363
Title: Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals
Authors: Chen, Jian
Randall, Robert Bond
Peeters, Bart
First Published: 16-Jan-2016
Publisher: Elsevier
Citation: Mechanical Systems and Signal Processing, 2016, 75, pp. 434-454
Abstract: Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.
DOI Link: 10.1016/j.ymssp.2015.12.023
ISSN: 0888-3270
eISSN: 1096-1216
Links: https://www.sciencedirect.com/science/article/pii/S0888327015005889
http://hdl.handle.net/2381/42363
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier, 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/), 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

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