Bayesian Network Approach for Dragline Reliability Analysis: a Case Study

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

doi:10.1007/s42461-023-00729-x

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…BNs can also dynamically assess the system performance, considering updated information 19 , 20 . In recent decades, the BN has been widely used for the reliability analysis of complex mechanical and electrical equipment such as dragline system 21 complex electronic systems 22 , offshore wind turbines 23 , subsea blowout control systems 24 , wind turbines and diesel generators 25 , and complex railway systems 26 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian network approach for reliability analysis of mining trucks

Rahimdel

2024

Sci Rep

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Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck’s component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck’s engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system.

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Section: Introductionmentioning

confidence: 99%

Bayesian network approach for reliability analysis of mining trucks

Rahimdel

2024

Sci Rep

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“…Previous Reported reliability models incorporating the covariates studies mostly focus on identifying and simulating the impact of factors on the hazards function [12,13]. Recent research has shown that Bayesian Network models, when applied to the performance analysis of equipment and systems, are computationally precise enough to deal with the casual relations that exist between the components and subsystems [15][16][17][18]. In the context of performance studies of electrical equipment, Bayesian networks have not yet been properly investigated to their full potential.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Approach for Studying the Operational Reliability and Remaining Useful Life

Jana,

Kumar,

Gupta

et al. 2024

JRSS

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Reliability study plays a significant role in supporting the operation of any machinery working in a dynamic and harsh environment such as mining, and construction industries. This quality is inherently uncertain and a stochastic variable of any system. This 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 to study 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 heavy mining machinery. The 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. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime, and reducing the maintenance cost of electrical motors operated particularly in dynamic and harsh environmental industries.

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Bayesian Network Approach for Dragline Reliability Analysis: a Case Study

Cited by 2 publications

References 33 publications

Bayesian network approach for reliability analysis of mining trucks

Bayesian network approach for reliability analysis of mining trucks

Bayesian Network Approach for Studying the Operational Reliability and Remaining Useful Life

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