Unsupervised Condition Change Detection In Large Diesel Engines

Niels Henrik Pontoppidan; Jan Larsen; C. Molina et al.

Unsupervised Condition Change Detection In Large Diesel Engines

Niels Henrik Pontoppidan, Jan Larsen, C. Molina et al. (Editor)

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Abstract

This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.

Original language	English
Title of host publication	IEEE Workshop on Neural Networks for Signal Processing
Publisher	IEEE Press
Publication date	2003
Pages	565-574
ISBN (Print)	0-7803-8177-7
Publication status	Published - 2003
Event	2003 IEEE XIII Workshop on Neural Networks for Signal Processing - Toulouse, France Duration: 17 Sept 2003 → 19 Sept 2003 Conference number: 13

Conference

Conference	2003 IEEE XIII Workshop on Neural Networks for Signal Processing
Number	13
Country/Territory	France
City	Toulouse
Period	17/09/2003 → 19/09/2003

Bibliographical note

Copyright: 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Keywords

change detection
Independent component analysis
large diesel engines

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@inproceedings{9195cda340684b94b70b55c6cbfb34a7,

title = "Unsupervised Condition Change Detection In Large Diesel Engines",

abstract = "This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.",

keywords = "change detection, Independent component analysis, large diesel engines",

author = "Pontoppidan, {Niels Henrik} and Jan Larsen and {Molina et al.}, C.",

note = "Copyright: 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 2003 IEEE XIII Workshop on Neural Networks for Signal Processing, NNSP'03 ; Conference date: 17-09-2003 Through 19-09-2003",

year = "2003",

language = "English",

isbn = "0-7803-8177-7",

pages = "565--574",

booktitle = "IEEE Workshop on Neural Networks for Signal Processing",

publisher = "IEEE Press",

}

TY - GEN

T1 - Unsupervised Condition Change Detection In Large Diesel Engines

AU - Pontoppidan, Niels Henrik

AU - Larsen, Jan

A2 - Molina et al., C.

N1 - Conference code: 13

PY - 2003

Y1 - 2003

N2 - This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.

AB - This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.

KW - change detection

KW - Independent component analysis

KW - large diesel engines

M3 - Article in proceedings

SN - 0-7803-8177-7

SP - 565

EP - 574

BT - IEEE Workshop on Neural Networks for Signal Processing

PB - IEEE Press

T2 - 2003 IEEE XIII Workshop on Neural Networks for Signal Processing

Y2 - 17 September 2003 through 19 September 2003

ER -

Unsupervised Condition Change Detection In Large Diesel Engines

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Keywords

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