Fault feature extracting by wavelet transform for control system fault detection and diagnosis

Zhang Ren; Jie Chen; Xiaojing Tang; Weisheng Yan

doi:10.1109/CCA.2000.897471

Fault feature extracting by wavelet transform for control system fault detection and diagnosis

Zhang Ren, Jie Chen, Xiaojing Tang, Weisheng Yan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this paper, to overcome the difficulty in fault feature extracting from the residual in model-based method for control system fault detection and diagnosis, based on the fact that the wavelet transform of a signal will take its modulus maximum at its singular point in transform domain, and the fault error has positive singularity exponent but noise has negative singularity exponent at the corresponding singular points, the fault error and noise mixed in the residual can be separated from each other by multi-scale wavelet transform, and the modulus maximum can be taken as the fault feature, so that the fault feature becomes clearer and more recognizable and a correct decision whether the system fault toke place or not can be correctly made in transform domain. This makes it easy to detect and diagnose the fault in control system. © 2000 IEEE

Original language	English
Title of host publication	Proceedings of the 2000 IEEE International Conference on Control Applications
Subtitle of host publication	Conference Proceedings
Publisher	IEEE
Pages	485-489
ISBN (Print)	0-7803-6562-3
DOIs	https://doi.org/10.1109/CCA.2000.897471
Publication status	Published - Sept 2000
Externally published	Yes
Event	2000 IEEE International Conference on Control Applications - Anchorage, Alaska, United States Duration: 25 Sept 2000 → 27 Sept 2000

Conference

Conference	2000 IEEE International Conference on Control Applications
Place	United States
City	Anchorage, Alaska
Period	25/09/00 → 27/09/00

Research Keywords

Wavelet Transform
fault feature extracting
Fault detection and Diagnosis

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10.1109/CCA.2000.897471

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@inproceedings{bfb08c04e95c4e88805bd81db1069949,

title = "Fault feature extracting by wavelet transform for control system fault detection and diagnosis",

abstract = "In this paper, to overcome the difficulty in fault feature extracting from the residual in model-based method for control system fault detection and diagnosis, based on the fact that the wavelet transform of a signal will take its modulus maximum at its singular point in transform domain, and the fault error has positive singularity exponent but noise has negative singularity exponent at the corresponding singular points, the fault error and noise mixed in the residual can be separated from each other by multi-scale wavelet transform, and the modulus maximum can be taken as the fault feature, so that the fault feature becomes clearer and more recognizable and a correct decision whether the system fault toke place or not can be correctly made in transform domain. This makes it easy to detect and diagnose the fault in control system. {\textcopyright} 2000 IEEE",

keywords = "Wavelet Transform, fault feature extracting, Fault detection and Diagnosis",

author = "Zhang Ren and Jie Chen and Xiaojing Tang and Weisheng Yan",

year = "2000",

month = sep,

doi = "10.1109/CCA.2000.897471",

language = "English",

isbn = "0-7803-6562-3",

pages = "485--489",

booktitle = "Proceedings of the 2000 IEEE International Conference on Control Applications",

publisher = "IEEE",

address = "United States",

note = "2000 IEEE International Conference on Control Applications ; Conference date: 25-09-2000 Through 27-09-2000",

}

Ren, Z, Chen, J, Tang, X & Yan, W 2000, Fault feature extracting by wavelet transform for control system fault detection and diagnosis. in Proceedings of the 2000 IEEE International Conference on Control Applications: Conference Proceedings. IEEE, pp. 485-489, 2000 IEEE International Conference on Control Applications, Anchorage, Alaska, United States, 25/09/00. https://doi.org/10.1109/CCA.2000.897471

Fault feature extracting by wavelet transform for control system fault detection and diagnosis. / Ren, Zhang; Chen, Jie; Tang, Xiaojing et al.
Proceedings of the 2000 IEEE International Conference on Control Applications: Conference Proceedings. IEEE, 2000. p. 485-489.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Fault feature extracting by wavelet transform for control system fault detection and diagnosis

AU - Ren, Zhang

AU - Chen, Jie

AU - Tang, Xiaojing

AU - Yan, Weisheng

PY - 2000/9

Y1 - 2000/9

N2 - In this paper, to overcome the difficulty in fault feature extracting from the residual in model-based method for control system fault detection and diagnosis, based on the fact that the wavelet transform of a signal will take its modulus maximum at its singular point in transform domain, and the fault error has positive singularity exponent but noise has negative singularity exponent at the corresponding singular points, the fault error and noise mixed in the residual can be separated from each other by multi-scale wavelet transform, and the modulus maximum can be taken as the fault feature, so that the fault feature becomes clearer and more recognizable and a correct decision whether the system fault toke place or not can be correctly made in transform domain. This makes it easy to detect and diagnose the fault in control system. © 2000 IEEE

AB - In this paper, to overcome the difficulty in fault feature extracting from the residual in model-based method for control system fault detection and diagnosis, based on the fact that the wavelet transform of a signal will take its modulus maximum at its singular point in transform domain, and the fault error has positive singularity exponent but noise has negative singularity exponent at the corresponding singular points, the fault error and noise mixed in the residual can be separated from each other by multi-scale wavelet transform, and the modulus maximum can be taken as the fault feature, so that the fault feature becomes clearer and more recognizable and a correct decision whether the system fault toke place or not can be correctly made in transform domain. This makes it easy to detect and diagnose the fault in control system. © 2000 IEEE

KW - Wavelet Transform

KW - fault feature extracting

KW - Fault detection and Diagnosis

UR - http://www.scopus.com/inward/record.url?scp=0034474797&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0034474797&origin=recordpage

U2 - 10.1109/CCA.2000.897471

DO - 10.1109/CCA.2000.897471

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0-7803-6562-3

SP - 485

EP - 489

BT - Proceedings of the 2000 IEEE International Conference on Control Applications

PB - IEEE

T2 - 2000 IEEE International Conference on Control Applications

Y2 - 25 September 2000 through 27 September 2000

ER -

Fault feature extracting by wavelet transform for control system fault detection and diagnosis

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Research Keywords

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