Improving the availability and lengthening the life of power unit elements through the use of risk-based maintenance planning

Rusin, Andrzej; Wojaczek, A.

doi:10.1016/j.energy.2019.05.079

Cited by 12 publications

(2 citation statements)

References 26 publications

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“…Several previous studies have presented risk-based maintenance (RBM) methods [15][16][17][18][19][20][21][22][23][24][25][26], and they have not considered the risk consequences such as human health loss, asset loss, production loss, and maintenance costs. Some studies have suggested risk-based shutdown interval models, such as those proposed by Khan and Hameed [27] and Hameed et al [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

et al. 2023

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In this paper, a risk-assessment model for minimizing human-machine error consequences during the implementation of preventive maintenance tasks has developed. The developed model aimed to find the optimal preventive maintenance interval (PMI) and corresponding minimum risk consequences costs per unit of time. Based on expert judgment, a human error probability (HEP) model was developed using the success likelihood index methodology (SLIM). In addition, we developed a system failure probability (SFP) model based on the failure and repair data of the system. The HEP model was integrated with an SFP model to develop a human-machine error model. Then, the risk-assessment model was developed based on the consequences of system failure and human error. The effectiveness of the developed human-machine error model was shown by applying a numerical example for a multi-component series system. The optimization toolbox from MATLAB R2022b was applied to solve the developed human-machine model. The results showed that the optimal PMI can be implemented after 24 working hours to 105 hours under the acceptable limit of risk in the system which is 100 SR/h. Finally, the developed model is an effective method that can be applied to a wide range of manufacturing systems to minimize financial risks related to system inspection and maintenance while meeting safety and availability requirements.

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Section: Literature Reviewmentioning

confidence: 99%

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

et al. 2023

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“…Hence, for preserving social and economic issues related to human health and climate change sustainable operation of ash handling plant is of supreme importance in thermal power Industry. For the multipart repairable system, like, ash handling plant, failure prediction is a hard‐hitting assignment for the maintenance personnel, which leads to partial or complete failure in the plant operation, like, thermal power plant 4–6 and paper mill industry 7 . These minor or sudden failures can be overcome by planning and employing a scheduled preventive maintenance procedure for which failure behavior study of the plant is a must.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy‐based structured framework for sustainable operation and environmental friendly production in coal‐fired power industry

Panchal

Chatterjee

Pamučar

et al. 2021

Int J of Intelligent Sys

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This study proposes a novel structured framework for analyzing sustainable operational performance‐related issues of ash handling unit (AHU) under vague/uncertain information and its impact on ecofriendly production. The structured framework is developed using an integrated fuzzy lambda–tau and fuzzy multicriteria decision‐making methods. Reliability parameters are formulated for assessing the failure behavior of AHU using the fuzzy lambda–tau approach. Repair time parameter for the AHU system shows an increasing trend with a 0.6840% increase as the spread increases from ±15% to ±25% and 0.5981% when spread increases from ±25% to ±60%. Furthermore, the availability of the system decreases by 0.0335% with increase in spread from ±15% to ±25% and further decreases to 0.0315% with spread increases from ±25% to ±60%. Under risk analysis, possible failure causes with their fuzzy rating are listed under the failure mode and effect analysis (FMEA) approach. Evaluation based on distance from average solution (EDAS) and combinative distance‐based assessment (CODAS) methods are further applied within the FMEA approach for identifying the most critical failure causes. Improper design (BT2), excessive gas volume (FH3), chocking/blockage (WA1), and scanty lubrication/greasing (SP1) associated with bottom ash handling, the fly ash handling system, wetting unit and ash disposal pipeline, and slurry pump with EDAS outputs (0.1464, 0.1090, 0.0918, 0.000) and CODAS outputs (3.3962, 3.5208, 2.6562, 2.0877) are found responsible for sudden failure of the considered unit. Sensitivity analysis has been done for evaluating the robustness of the proposed framework.

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Multiresponse maintenance modeling using desirability function and Taguchi methods

Rane

Pai²,

Pai³

et al. 2021

Safety and Reliability Modeling and Its Applications

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Improving the availability and lengthening the life of power unit elements through the use of risk-based maintenance planning

Cited by 12 publications

References 26 publications

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

A novel fuzzy‐based structured framework for sustainable operation and environmental friendly production in coal‐fired power industry

Multiresponse maintenance modeling using desirability function and Taguchi methods

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