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A Self-diagnostic Flame Monitoring System Incorporating Acoustic, Optical, and Electrostatic Sensors

Zhang, Yanchao, Yan, Yong, Bai, Xiaojing, Wu, Jiali (2022) A Self-diagnostic Flame Monitoring System Incorporating Acoustic, Optical, and Electrostatic Sensors. In: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2022) Proceedings. . IEEE ISBN 978-1-66548-360-5. (doi:10.1109/I2MTC48687.2022.9806634) (KAR id:95649)

Abstract

Reliable flame monitoring is essential to enhance the safety of industrial boilers. This paper presents a new self-diagnostic system to measure the oscillation frequency of a burner flame. The system incorporates three sensors including a microphone, a photodiode and an electrostatic electrode and simultaneously acquires three signals. The oscillation frequencies from the three sensors are determined through power spectral analysis, and a fused result of the three frequencies is obtained as the oscillation frequency of the burner flame. Moreover, detection and location of the system faults are realized using a self-diagnostic algorithm through the cross-correlation signal processing. Experimental tests were performed on a laboratory-scale combustion test rig with methane as the test fuel. The results demonstrate that the method is capable of measuring the oscillation frequency of a burner flame. In addition, the results are helpful for the comprehensive analysis of the oscillatory behaviors of burner flames. The self-diagnostic algorithm is able to detect the fault of the monitoring system and no additional self-diagnostic hardware is required.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/I2MTC48687.2022.9806634
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Yong Yan
Date Deposited: 02 Jul 2022 07:33 UTC
Last Modified: 04 Jul 2022 09:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/95649 (The current URI for this page, for reference purposes)

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