Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain

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DO, Van Tuan;CHONG, Ui-Pil .
Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 57, n.9, p. 655-666, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/>. Date accessed: 24 aug. 2019. 
doi:http://dx.doi.org/10.5545/sv-jme.2010.162.
Do, V., & Chong, U.
(2011).
Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain.
Strojniški vestnik - Journal of Mechanical Engineering, 57(9), 655-666.
doi:http://dx.doi.org/10.5545/sv-jme.2010.162
@article{sv-jmesv-jme.2010.162,
	author = {Van Tuan Do and Ui-Pil  Chong},
	title = {Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {9},
	year = {2011},
	keywords = {fault detection and diagnosis; SIFT; feature vector; texton dictionary; two-dimension domain; classification accuracy},
	abstract = {In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.},
	issn = {0039-2480},	pages = {655-666},	doi = {10.5545/sv-jme.2010.162},
	url = {https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/}
}
Do, V.,Chong, U.
2011 June 57. Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 57:9
%A Do, Van Tuan
%A Chong, Ui-Pil 
%D 2011
%T Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain
%B 2011
%9 fault detection and diagnosis; SIFT; feature vector; texton dictionary; two-dimension domain; classification accuracy
%! Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain
%K fault detection and diagnosis; SIFT; feature vector; texton dictionary; two-dimension domain; classification accuracy
%X In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.
%U https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/
%0 Journal Article
%R 10.5545/sv-jme.2010.162
%& 655
%P 12
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 57
%N 9
%@ 0039-2480
%8 2018-06-29
%7 2018-06-29
Do, Van, & Ui-Pil  Chong.
"Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain." Strojniški vestnik - Journal of Mechanical Engineering [Online], 57.9 (2011): 655-666. Web.  24 Aug. 2019
TY  - JOUR
AU  - Do, Van Tuan
AU  - Chong, Ui-Pil 
PY  - 2011
TI  - Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2010.162
KW  - fault detection and diagnosis; SIFT; feature vector; texton dictionary; two-dimension domain; classification accuracy
N2  - In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.
UR  - https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/
@article{{sv-jme}{sv-jme.2010.162},
	author = {Do, V., Chong, U.},
	title = {Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {9},
	year = {2011},
	doi = {10.5545/sv-jme.2010.162},
	url = {https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/}
}
TY  - JOUR
AU  - Do, Van Tuan
AU  - Chong, Ui-Pil 
PY  - 2018/06/29
TI  - Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 57, No 9 (2011): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2010.162
KW  - fault detection and diagnosis, SIFT, feature vector, texton dictionary, two-dimension domain, classification accuracy
N2  - In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.
UR  - https://www.sv-jme.eu/article/signal-model-based-fault-detection-and-diagnosis-for-induction-motors-using-features-of-vibration-signal-in-two-dimension-domain/
Do, Van, AND Chong, Ui-Pil.
"Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two- Dimension Domain" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 57 Number 9 (29 June 2018)

Authors

Affiliations

  • VTT Technical Research Centre of Finland, FI-02044, Espoo
  • University of Ulsan, Department of Computer Engineering and Information Technology

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)9, 655-666

10.5545/sv-jme.2010.162

In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.

fault detection and diagnosis; SIFT; feature vector; texton dictionary; two-dimension domain; classification accuracy