Research on PHM technology application of ship maintenance program optimization

Xu, Xiaowei; Fan, Shidong; Huang, Haofei; Zhu, Hanhua; Quan, Wen

doi:10.1109/icphm.2013.6621438

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The core of this method is data fusion technology. Data fusion usually comes from multiple information sources, multiple sensors, and historical information in fault prediction [4]. This information is not entirely reliable, so it is necessary to conduct reliability analysis, remove the unreliable data and leave the reliable data.…”

Section: Fault Prediction Based On Data Fusionmentioning

confidence: 99%

Avionics fault prediction and health management technology based on SJ-BIST

Yang

Wu²,

Wen³

et al. 2022

International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

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This paper summarizes and analyzes the typical architecture and evolution of fault prediction and health management technology. We introduce and analyze fault detection, fault diagnosis, and fault prediction functions to explore the architecture of fault prediction and health management systems. This experiment studies the fault prediction method based on solder joint-built in self-test (SJ-BIST). According to the actual monitoring circuit experiments, we adjusted the temperature of the circuit board to 50℃, 100℃, and 150℃ for the circuit board heating treatment for 10 seconds, verified the effectiveness of the SJ-BIST method, and are in line with the law of linear change. The slope of the fitted curve is consistent with the analysis result.

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Section: Fault Prediction Based On Data Fusionmentioning

confidence: 99%

Avionics fault prediction and health management technology based on SJ-BIST

Yang

Wu²,

Wen³

et al. 2022

International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

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show abstract

“…It has enhanced potential to detect, isolate, and identify precursor and/or incipient faults of components and sub-components, monitors and predicts the progression of the fault, and provide decisionsupport or automation to develop maintenance schedules and asset management procedures [8]. Indeed, recent studies have confirmed that PHM is a positive alternative to traditional Condition Based Maintenance (CBM) and has therefore gained attention in both academia and the maritime industry [8]- [10]. However, DSSs with a high degree of decision automation have continue to fail frequently in industrial applications [11].…”

mentioning

confidence: 99%

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

et al. 2019

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The maritime industry widely expects to have autonomous and semi-autonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration towards PHM based on DL (PHMDL) in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology. Index Terms-Autonomous ships, deep learning, maritime industry, prognostics and health management.

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Design and Implementation of PHM System Framework for Unmanned Surface Vehicles

Zhou

Yang

2019

2019 Prognostics and System Health Management Conference (PHM-Qingdao)

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Research on PHM technology application of ship maintenance program optimization

Cited by 5 publications

References 8 publications

Avionics fault prediction and health management technology based on SJ-BIST

Avionics fault prediction and health management technology based on SJ-BIST

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

Design and Implementation of PHM System Framework for Unmanned Surface Vehicles

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