Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor

Sadra Mousavi; Duygu Bayram; Serhat Seker

doi:10.1109/ACEMP-OPTIM44294.2019.9007202

Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor

Sadra Mousavi, Duygu Bayram, Serhat Seker

Department of Electrical Engineering

Istanbul Technical University

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

3 Citations (Scopus)

Abstract

In this paper, a data fusion method through Kalman filtering for condition monitoring (CM) and fault detection (FD) of electrical motors (EM) is proposed. Moreover, sensor validation (SV) and tracking the fault source (either the sensor or the process) are possible through this approach. A current signal, obtained from different sensors, is used for the case study. A fused current information is calculated through Kalman filtering. Afterwards, the effects of the measurement and process noises on the fused signal, are discussed, respectively. Then, it is noticed distinctive features by the comparison of the fused and original signal in terms of spectral and statistical properties. In addition, Kalman gain is monitored to investigate the impact of the process noise and measurement noise to perform SV. The proposed method is developed on the artificial data and then tested on the real data collected from an Induction Motor (IM).

Original language	English
Title of host publication	Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	155-160
Number of pages	6
ISBN (Electronic)	9781538676875
DOIs	https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007202
Publication status	Published - Aug 2019
Event	2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019 - Istanbul, Turkey Duration: 27 Aug 2019 → 29 Aug 2019

Publication series

Name	Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019

Conference

Conference	2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019
Country/Territory	Turkey
City	Istanbul
Period	27/08/19 → 29/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

condition monitoring
current signal
data fusion
electrical motors
fault detection
Kalman filtering
Kalman gain
sensor validation

Access to Document

10.1109/ACEMP-OPTIM44294.2019.9007202

Cite this

Mousavi, S., Bayram, D., & Seker, S. (2019). Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor. In Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019 (pp. 155-160). Article 9007202 (Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007202

Mousavi, Sadra ; Bayram, Duygu ; Seker, Serhat. / Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor. Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 155-160 (Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019).

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title = "Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor",

abstract = "In this paper, a data fusion method through Kalman filtering for condition monitoring (CM) and fault detection (FD) of electrical motors (EM) is proposed. Moreover, sensor validation (SV) and tracking the fault source (either the sensor or the process) are possible through this approach. A current signal, obtained from different sensors, is used for the case study. A fused current information is calculated through Kalman filtering. Afterwards, the effects of the measurement and process noises on the fused signal, are discussed, respectively. Then, it is noticed distinctive features by the comparison of the fused and original signal in terms of spectral and statistical properties. In addition, Kalman gain is monitored to investigate the impact of the process noise and measurement noise to perform SV. The proposed method is developed on the artificial data and then tested on the real data collected from an Induction Motor (IM).",

keywords = "condition monitoring, current signal, data fusion, electrical motors, fault detection, Kalman filtering, Kalman gain, sensor validation",

author = "Sadra Mousavi and Duygu Bayram and Serhat Seker",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019 ; Conference date: 27-08-2019 Through 29-08-2019",

year = "2019",

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doi = "10.1109/ACEMP-OPTIM44294.2019.9007202",

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series = "Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Mousavi, S, Bayram, D & Seker, S 2019, Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor. in Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019., 9007202, Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 155-160, 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019, Istanbul, Turkey, 27/08/19. https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007202

Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor. / Mousavi, Sadra; Bayram, Duygu ; Seker, Serhat.
Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 155-160 9007202 (Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019).

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

TY - GEN

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AU - Bayram, Duygu

AU - Seker, Serhat

PY - 2019/8

Y1 - 2019/8

N2 - In this paper, a data fusion method through Kalman filtering for condition monitoring (CM) and fault detection (FD) of electrical motors (EM) is proposed. Moreover, sensor validation (SV) and tracking the fault source (either the sensor or the process) are possible through this approach. A current signal, obtained from different sensors, is used for the case study. A fused current information is calculated through Kalman filtering. Afterwards, the effects of the measurement and process noises on the fused signal, are discussed, respectively. Then, it is noticed distinctive features by the comparison of the fused and original signal in terms of spectral and statistical properties. In addition, Kalman gain is monitored to investigate the impact of the process noise and measurement noise to perform SV. The proposed method is developed on the artificial data and then tested on the real data collected from an Induction Motor (IM).

AB - In this paper, a data fusion method through Kalman filtering for condition monitoring (CM) and fault detection (FD) of electrical motors (EM) is proposed. Moreover, sensor validation (SV) and tracking the fault source (either the sensor or the process) are possible through this approach. A current signal, obtained from different sensors, is used for the case study. A fused current information is calculated through Kalman filtering. Afterwards, the effects of the measurement and process noises on the fused signal, are discussed, respectively. Then, it is noticed distinctive features by the comparison of the fused and original signal in terms of spectral and statistical properties. In addition, Kalman gain is monitored to investigate the impact of the process noise and measurement noise to perform SV. The proposed method is developed on the artificial data and then tested on the real data collected from an Induction Motor (IM).

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 27 August 2019 through 29 August 2019

ER -

Mousavi S, Bayram D , Seker S. Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor. In Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 155-160. 9007202. (Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019). doi: 10.1109/ACEMP-OPTIM44294.2019.9007202

Current Data Fusion through Kalman Filtering for Fault Detection and Sensor Validation of an Electric Motor

Abstract

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Bibliographical note

Keywords

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