Adaptive threshold method for intelligent ship power system equipment

GAO Zeyu; ZHANG Peng; ZHANG Boshen; ZHANG Yuewen; SUN Peiting

doi:10.19693/j.issn.1673-3185.01951

Volume 16 Issue 1

Feb. 2021

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Chinese Journal of Ship Research > 2021 > 16(1): 167-173. > DOI: 10.19693/j.issn.1673-3185.01951 CSTR: 32390.14.j.issn.1673-3185.01951

GAO Z Y, ZHANG P, ZHANG B S, et al. Adaptive threshold method for intelligent ship power system equipment[J]. Chinese Journal of Ship Research, 2021, 16(1): 167–173. DOI: 10.19693/j.issn.1673-3185.01951

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Adaptive threshold method for intelligent ship power system equipment

Marine Engineering College, Dalian Maritime University, Dalian 116026, China

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Received Date: May 05, 2020
Revised Date: August 12, 2020
Available Online: December 24, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Abstract

Objective In light of problems such as the untimely condition monitoring and alarm, excessively large threshold bandwidth and inaccurate condition evaluation parameters of intelligent ship power system equipment, an adaptive threshold method is proposed to monitor, alarm and evaluate the conditions of such equipment.
Method First, a simulated annealing algorithm is used to optimize the support vector regression (SVR) machine prediction model to simulate the general state characteristic parameters of the power system equipment. Then, after the normal transformation of the modeling residual, combined with the sliding time window, the adaptive threshold model is constructed. Finally, the exhaust gas temperature of the ship's main propulsion diesel engine is selected as the research object for example verification.
Results The results show that compared with the traditional fixed threshold, the adaptive threshold model has more compact bandwidth and good adaptability, and can identify abnormal phenomena in power system equipment in advance.
Conclusion This method improves the efficiency and threshold accuracy of monitoring and alarm systems, and provides an effective means of early fault diagnosis and a more accurate basis for system status evaluation.
- intelligent ships,
- adaptive threshold,
- support vector regression (SVR),
- simulated annealing,
- state characteristic parameters

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Adaptive threshold method for intelligent ship power system equipment