Integrated fault evaluation through fusion algorithm supported by kalman filter

Sadra Mousavi*, Duygu Bayram Kara, Sahin Serhat Seker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (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 languageEnglish
Pages (from-to)975-987
Number of pages13
JournalTraitement du Signal
Volume37
Issue number6
DOIs
Publication statusPublished - 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

FundersFunder number
Kalman Filter
Nuclear Engineering Department
University of Tennessee, Knoxville
Istanbul Teknik ÜniversitesiMDK-2018-41044

    Keywords

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

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