Integrated fault evaluation through fusion algorithm supported by kalman filter

Sadra Mousavi; Duygu Bayram Kara; Sahin Serhat Seker

doi:10.18280/TS.370610

Integrated fault evaluation through fusion algorithm supported by kalman filter

Sadra Mousavi^*
, Duygu Bayram Kara
, Sahin Serhat Seker

^*Corresponding author for this work

Department of Electrical Engineering

Istanbul Technical University

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

Original language	English
Pages (from-to)	975-987
Number of pages	13
Journal	Traitement du Signal
Volume	37
Issue number	6
DOIs	https://doi.org/10.18280/TS.370610
Publication status	Published - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 Lavoisier. All rights reserved.

Funding

The authors would like to thank Prof. B. R. Upadhyaya from Nuclear Engineering Department at the University of Tennessee Knoxville, USA, for permission to the use of the experimental data. Also, this study is supported by the Scientific Research Unit of Istanbul Technical University with the project ID: MDK-2018-41044, entitled as “Sensor Validation and Fusion for System Monitoring”. Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

Funders	Funder number
Kalman Filter
Nuclear Engineering Department
University of Tennessee, Knoxville
Istanbul Teknik Üniversitesi	MDK-2018-41044

Keywords

Aging process
Data fusion
Fault detection
Health information
Kalman filter
Sensor validation
Vibration signal

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10.18280/TS.370610

Cite this

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title = "Integrated fault evaluation through fusion algorithm supported by kalman filter",

abstract = "An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.",

keywords = "Aging process, Data fusion, Fault detection, Health information, Kalman filter, Sensor validation, Vibration signal",

author = "Sadra Mousavi and Kara, \{Duygu Bayram\} and Seker, \{Sahin Serhat\}",

year = "2020",

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doi = "10.18280/TS.370610",

language = "English",

volume = "37",

pages = "975--987",

journal = "Traitement du Signal",

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AU - Mousavi, Sadra

AU - Kara, Duygu Bayram

AU - Seker, Sahin Serhat

PY - 2020/12

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N2 - An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

AB - An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

KW - Aging process

KW - Data fusion

KW - Fault detection

KW - Health information

KW - Kalman filter

KW - Sensor validation

KW - Vibration signal

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JO - Traitement du Signal

JF - Traitement du Signal

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Integrated fault evaluation through fusion algorithm supported by kalman filter

Abstract

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Keywords

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