Performance Degradation Based on Importance Change and Application in Dissimilar Redundancy Actuation System

Zhang, Yadong; Zhang, Chao; Wang, Shaoping; Chen, Rentong; Tomovic, Mileta

doi:10.3390/math10050843

Cited by 7 publications

(5 citation statements)

References 33 publications

(38 reference statements)

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“…The Bir IM as highlighted earlier measures the contribution of the most critical component to the occurrence of the top event, hence helping to clearly identify what component need improvement. In this regard, researchers have adopted Bir IM to enable identification critical system failures to avoid catastrophic failures like crank case explosion in diesel engines [56,57]. DFTA has equally been combined with other tools to achieve additional research goals such as decision support or analysis requiring some level of subjective inputs [58,59].…”

Section: Reliability Importance Measuresmentioning

confidence: 99%

Component Criticality Analysis for Improved Ship Machinery Reliability

Daya¹,

Lazakis²

2023

Preprint

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Redundancy in ship systems is provided to ensure operational resilience through equipment backups which ensures system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems, hence is provided with good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired black outs are common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensure a more robust maintenance and support platform for efficient ship operations. In this regard hybrid reliability and fault detection analysis were conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of 4 marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate overall low system reliability of less than 70 per cent within the first 24 months of the 78 operational months. Component criticality using reliability importance measures was used to identify all components with more than 40 per cent contribution to sub systems failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Networks classification. The ANN has identified failure patten threshold at about 200kva which can be attributed to overheating, hence establishing link between component failure and generator performance.

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Section: Reliability Importance Measuresmentioning

confidence: 99%

Component Criticality Analysis for Improved Ship Machinery Reliability

Daya¹,

Lazakis²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The Bir IM, as highlighted earlier, measures the contribution of the most critical component to the occurrence of the top event, thereby helping to clearly identify what component needs improvement. In this regard, researchers have adopted Bir IM to enable the identification of critical system failures to avoid catastrophic failures like crankcase explosions in diesel engines [57,58]. DFTA has equally been combined with other tools to achieve additional research goals, such as decision support or analysis requiring some level of subjective input [59,60].…”

Section: Reliability Importance Measures (Ims)mentioning

confidence: 99%

Component Criticality Analysis for Improved Ship Machinery Reliability

Daya

Lazakis

2023

Machines

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Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance.

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“…Tan et al [17] used the gray correlation degree to measure the Euclidean distance and the coupling degree of the curve edge, which made the evaluation result of the importance degree more accurate. Zhang et al [18] considered the influence of the Birnbaum importance measure and integrated the importance measure with the reliability changes of key components in a dissimilar redundancy actuation system. Guo et al [19] used degree centrality, closeness centrality, betweenness centrality, toughness and cohesion as indicators to sort the importance of nodes.…”

Section: Relevant Workmentioning

confidence: 99%

Reliability Analysis and Redundancy Optimization of a Command Post Phased-Mission System

Dui

Zhang

2022

Mathematics

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This paper divides the execution process of the command post system into four stages: information acquisition, information processing, decision control and response execution. It combines multilayer complex networks with a phased-mission system. Most studies have only evaluated the reliability of phased-mission systems. This paper evaluates and optimizes the reliability of a phased-mission system. In order to improve the mission success rate and maximize the reliability of a command post system, this paper provides a multitasking node criticality index, and the index is used to identify the key nodes in the command post’s four-stage network Then, the hot backup system of the node is selected to determine the redundant structure of the key node. Under the constraints of the operation and maintenance costs of key nodes, with the goal of maximizing the reliability of the information processing network layer, the multitask redundancy optimization model of each stage is established. Finally, the reliability of the missions before and after redundancy optimization is compared, using the case analysis of the four-layer network to verify the effectiveness of the proposed model.

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Performance Degradation Based on Importance Change and Application in Dissimilar Redundancy Actuation System

Cited by 7 publications

References 33 publications

Component Criticality Analysis for Improved Ship Machinery Reliability

Component Criticality Analysis for Improved Ship Machinery Reliability

Component Criticality Analysis for Improved Ship Machinery Reliability

Reliability Analysis and Redundancy Optimization of a Command Post Phased-Mission System

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