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: 19 dec. 2024. 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. 19 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 57(2011)9, 655-666
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.