A Bayesian inference-based approach for performance prognostics towards uncertainty quantification and its applications on the marine diesel engine

Wang, Ruihan; Chen, Hui; Guan, Cong

doi:10.1016/j.isatra.2021.02.024

Cited by 33 publications

(11 citation statements)

References 31 publications

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“…To support decision-making on assessing the risk of СTS failures based on a priori and a posteriori data, as well as when searching for failed elements and system components in order to increase the efficiency of their operation, a method based on dynamic Bayesian trust networks (DBТN) is used [8,9]. The use of DBТN makes it possible to determine with great accuracy the elements and components of the СTS that are closest to the critical state and their failure.…”

Section: Main Partmentioning

confidence: 99%

Intelligent decision support system for assessing the risk of failures of complex technical systems

Vychuzhanin

2023

MCIT

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A decision support system has been developed that allows assessing the risk of failure of elements and components of complex technical systems using artificial intelligence. The proposed algorithm for the functioning of a decision support system implements the task of automating the process of assessing the technical condition of complex systems. The use of the proposed decision support system for assessing the technical condition of complex systems will improve the reliability of operating systems with insufficient information about their technical condition.

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Section: Main Partmentioning

confidence: 99%

Intelligent decision support system for assessing the risk of failures of complex technical systems

Vychuzhanin

2023

MCIT

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“…The reliability of marine vessel engine operation must be ensured by operative and qualified maintenance actions, while the type and frequency of actions to ensure the operability of mechanisms directly depends on the awareness of the technical condition of operating mechanism elements [11]. Decision-making is a complicated task, and, as a rule, it is a combination of past experience, method of expert evaluations, and timely diagnostics based on the results of the physical and chemical parameters of oil [12,13]. To avoid undesirable consequences due to incorrectly made or not made decisions on the maintenance of engine-oil system elements, we recommend precise forecasting and system approach to ensure specified operation levels, taking into account the residual life of the lubricant.…”

Section: Figure 1 Main Characteristics Of Engine Oilsmentioning

confidence: 99%

Research and evaluation of the operating characteristics of used ship engine oil using the process parameter matrix method

Mazur,

Shcherban,

Mazur

2023

FME Transactions

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The marine engine circulating oil change interval suggested by manufacturers is a guideline based on general scale statistics and laboratory testing. At the same time, the actual remaining oil life can significantly correct the time and money spent by the chief mechanic service on system maintenance. In the present work, a method has been developed that makes it possible to increase the service life of mechanisms and the reliability of ship equipment under operating conditions. The effect is achieved by identifying and analyzing the most significant and influential parameters of the lubricant used. An array of physical and chemical data on lubricants, taking into account the equipment's time to failure, is processed by a special computer program for monitoring the state of a marine engine in operating mode. The developed software package allows more accurate and timely maintenance of the SPP (ship power plant).

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“…Therefore, in the prediction method of MSAE, it is very necessary to explain and quantify the prediction uncertainty and improve the prediction results. To solve this problem, Wang et al proposed the fusion of two Bayesian models to provide a probability distribution for the uncertainty in the final prediction results, and the framework is shown in Figure 12 [143]. The Bayesian theorem can be expressed as…”

Section: Hybrid Failure Prognosticsmentioning

confidence: 99%

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

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Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

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A Bayesian inference-based approach for performance prognostics towards uncertainty quantification and its applications on the marine diesel engine

Cited by 33 publications

References 31 publications

Intelligent decision support system for assessing the risk of failures of complex technical systems

Intelligent decision support system for assessing the risk of failures of complex technical systems

Research and evaluation of the operating characteristics of used ship engine oil using the process parameter matrix method

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

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