Reliability Analysis of Dragline Subsystem using Bayesian Network Approach

Kumar, Deepak; Jana, Debasis; Yadav, Pawan Kumar; Gupta, Suprakash

doi:10.18311/jmmf/2022/31958

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Kumar et al. (2020a, b) analyzed the reliability, availability and maintainability measures of soft water treatment plant and dragline. Kumar and Singh (2020) conducted cost optimization and RAM analysis of several industrial sectors.…”

Section: Introductionmentioning

confidence: 99%

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Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization

Saini

Sinwar

Swarith

et al. 2022

JQME

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PurposeReliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.Design/methodology/approachThe data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.FindingsThe reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.Practical implicationsThe present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.Originality/valueIn this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

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Section: Introductionmentioning

confidence: 99%

“…Kumar et al. (2020a, b) proposed operational performance improvement model for a soft water treatment and supply plant by basic concepts of reliability engineering. Dhiman and Kumar (2020) performed the RAM estimation of the repairable industrial systems using genuine human-mistake working conditions under generalized fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization

Saini

Sinwar

Swarith

et al. 2022

JQME

View full text Add to dashboard Cite

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Availability Optimization of a Dragline Subsystem using Bayesian Network

Jana

Kumar²,

Gupta³

et al. 2023

J. Inst. Eng. India Ser. D

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Machine learning approach for studying the influencing factors affecting the operational reliability and remaining useful life

Jana,

Gupta,

Kumar

et al. 2024

IJQRM

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PurposeReliability study plays a significant role in supporting the operation of any machinery working in a dynamic and harsh environment. This quality is inherently uncertain and a stochastic variable of any system. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.Design/methodology/approachThis study focused on the effects of operating conditions (OCs) on the operational reliability and remaining useful life (RUL) of machinery. A probabilistic graphical method called Bayesian network (BN) was used for studying the effect of OCs on the system performance. The developed methodology has been demonstrated by analyzing the operational reliability and predicting the RUL of electrical motors operated in a heavy mining machinery.FindingsThe failure probabilities estimated from the historical data of the motor system are failure likelihood, and OCs are the evidence in the developed BN model. It has been observed that the performance and RUL of the motor are significantly influenced by OCs and maintenance. A threshold value of reliability at which the motor system requires maintenance or replacement has been proposed to guide management in decision making.Originality/valueThe Bayesian approach for studying the covariate of motor reliability and RUL estimation is a novel approach. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.

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Reliability Analysis of Dragline Subsystem using Bayesian Network Approach

Cited by 3 publications

References 44 publications

Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization

Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization

Availability Optimization of a Dragline Subsystem using Bayesian Network

Machine learning approach for studying the influencing factors affecting the operational reliability and remaining useful life

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