Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

Yuhua Li; MJ Pont; CR Parikh; NB Jones

Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

Yuhua Li, MJ Pont, CR Parikh, NB Jones

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.

Language	English
Title of host publication	Unknown Host Publication
Pages	46-51
Number of pages	6
Publication status	Published - 2000
Event	SOFT COMPUTING TECHNIQUES AND APPLICATIONS - Duration: 1 Jan 2000 → …

Publication series

Name	ADVANCES IN SOFT COMPUTING

Conference

Conference	SOFT COMPUTING TECHNIQUES AND APPLICATIONS
Period	1/01/00 → …

Fingerprint

Radial basis function networks

Multilayer neural networks

Classifiers

Engines

Keywords

engine misfire detection
neural networks
multi-layer Perceptron
radial basis function
condition monitoring
fault classification

Cite this

Li, Y., Pont, MJ., Parikh, CR., & Jones, NB. (2000). Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. In Unknown Host Publication (pp. 46-51). (ADVANCES IN SOFT COMPUTING).

Li, Yuhua ; Pont, MJ ; Parikh, CR ; Jones, NB. / Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. Unknown Host Publication. 2000. pp. 46-51 (ADVANCES IN SOFT COMPUTING).

@inproceedings{bb76dd0aaa424207b1bc5ca46ec42baa,

title = "Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection",

abstract = "In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10{\%}.",

keywords = "engine misfire detection, neural networks, multi-layer Perceptron, radial basis function, condition monitoring, fault classification",

author = "Yuhua Li and MJ Pont and CR Parikh and NB Jones",

note = "Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999",

year = "2000",

language = "English",

series = "ADVANCES IN SOFT COMPUTING",

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Li, Y, Pont, MJ, Parikh, CR & Jones, NB 2000, Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. in Unknown Host Publication. ADVANCES IN SOFT COMPUTING, pp. 46-51, SOFT COMPUTING TECHNIQUES AND APPLICATIONS, 1/01/00.

Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. / Li, Yuhua; Pont, MJ; Parikh, CR; Jones, NB.

Unknown Host Publication. 2000. p. 46-51 (ADVANCES IN SOFT COMPUTING).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

AU - Li, Yuhua

AU - Pont, MJ

AU - Parikh, CR

AU - Jones, NB

N1 - Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999

PY - 2000

Y1 - 2000

N2 - In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.

AB - In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.

KW - engine misfire detection

KW - neural networks

KW - multi-layer Perceptron

KW - radial basis function

KW - condition monitoring

KW - fault classification

M3 - Conference contribution

T3 - ADVANCES IN SOFT COMPUTING

SP - 46

EP - 51

BT - Unknown Host Publication

ER -

Li Y, Pont MJ, Parikh CR, Jones NB. Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. In Unknown Host Publication. 2000. p. 46-51. (ADVANCES IN SOFT COMPUTING).