Replacement and maintenance analysis of longwall shearer using fault tree technique

Gupta, Suprakash; Ramkrishna, Naik Kranti; Bhattacharya, Jayanta

doi:10.1179/174328606x111433

Cited by 16 publications

(12 citation statements)

References 2 publications

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“…The FTA technique has been extensively used within numerous industrial systems aimed at diverse applications; for instance, the assessment of system safety in a nuclear reactor and gas distribution systems [33,40]; the diagnosis and prognosis of faults/failures in electric power, electronic components, pipelines, and aerospace [41,42]; the implication of maintenance tasks in medical devices, mining, chemical processes, and mechanical systems [36,37,43,44]; and the analysis of FP values in automotive, chemical, and petrochemical processes [39,45,46]. Despite many advantages and the successful feedback of the FTA technique to estimate the FP values, the technique includes some limitations, such as the simplification of the model due to the system's complexity and the lack of knowledge about the system's behavior; an assumption of the independence of events; and the possibility of human error while specifying the logical structure of fault trees and the presence of the redundant or common cause failures [28,29].…”

Section: Literature Review Of Ftamentioning

confidence: 99%

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

et al. 2021

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Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time.

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Section: Literature Review Of Ftamentioning

confidence: 99%

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

et al. 2021

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“…According to the Bernbaum factor method and based on the shearer reliability of the shearer components, Gupta established a two-parameter Weibull distribution model based on the fault data and combined it with the fault tree to determine the failure rate function of the equipment. The degree of contribution was prioritized for the repair of various components of the shearer, and the maintenance strategy of the shearer was updated according to the sequencing situation [9]. Hoseinie established a reliability model based on the fault time for shearer failure and introduced the cost rate function to determine the best maintenance time of the shearer [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mine Down-Hole Equipment Maintenance Strategy based on Fault Data

Jin¹,

Yuan²,

Yingqian³

et al. 2019

Int J Performability Eng

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Aiming at the problems of poor operating conditions, low operating efficiency, and improper maintenance of mine down-hole equipment, a new maintenance strategy model for cycle prevention is proposed. The model comprehensively considers the effect of down-hole environment on the equipment failure rate and the effect of preventive maintenance and minor repairs to the cost. Based on the historical fault data, the equipment failure rate function parameters are estimated by the least square method and correlation coefficient method. Considering the down-hole environment, the fuzzy rate theory is used to correct the failure rate function, and the cycle prevention maintenance strategy model aiming at optimizing the equipment maintenance cost is established. Finally, an example is given. The research shows that the maintenance period is shortened to 23.64 h after optimization, and the total cost of maintenance is reduced from 574,621 yuan to 378,942.44 yuan, saving maintenance costs of 195,678.56 yuan. The maintenance strategy is in line with the actual situation of down-hole equipment in mining enterprises, and it can provide effective maintenance solutions for down-hole equipment in mine enterprises.

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“…The problem was formulated in an underground mine system using five basic operations: drilling, blasting, loading, hauling and ventilation and this was solved by using genetic algorithms. Gupta et al (2006) developed a method using maintenance information, such as, time to failure (TTF) for pinpointing the weak links in the shearer machine. In this study a fault tree (FT) technique was used to understand the failure logic of a longwall shearer and its components were ranked by the Birnbaum factor.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…The reliability of the shearer machine was reported by Mandal and Banik (1996) and Bing‐Yuan et al (2009). Nevertheless, in only one study, presented by Gupta et al (2006), the reliability and especially maintenance of shearer was analyzed as a distinct system. They studied the shearer's reliability using the FT method and assumed that all units and components fell under the two‐parameter Weibull failure density function.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Reliability analysis of drum shearer machine at mechanized longwall mines

Hoseinie

Ataei

Khalokakaie

et al. 2012

Journal of Quality in Maintenance Engineering

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PurposeLongwall mining is a special mining method with high productivity and smooth operation and the drum shearer is known as the most important component in longwall mines due to its direct role in the coal cutting and production process. Therefore, its reliability is important in keeping the mine production at a desired level. Hence, reliability analysis is essential in identifying and removing existing problems of this machine in order to achieve a better production condition. This paper seeks to learn about the reliability of the shearer machine in order to locate critical subsystems. The improvement of the reliability of the critical subsystems, to enhance the optimum operation of the shearer machine, is the main objective of this research.Design/methodology/approachA basic methodology was used in this paper for the reliability modeling of the shearer machine. First, failure and performance data from a two‐year period at the Tabas Coal Mine‐Iran was classified and sorted. The tests for validating the assumption of independent and identical distribution (iid) of TBF data are done and the best modeling method for each subsystem was selected among the renewal process, homogeneous Poisson process and non‐homogeneous Poisson process. Finally, the reliability of subsystems and the machine were assessed.FindingsThe study revealed that six important subsystems of the shearer machine are; water system, haulage, electrical system, hydraulic system, cutting arms, and cable system. Pareto analysis shows that the 30 percent of failures and stoppages of the shearer were related to the water system and this system is the most critical subsystem of the machine. The failure rate analysis shows that the failure rates of the hydraulic, haulage and electrical systems were decreasing, meanwhile, the failure rates of the water system, cutting arms and cable system were increasing. The reliability of drum shearer reaches the zero value after 100 hours.Originality/valueThis paper, for the first time, defines a practical set of subsystems for the coal shearer based on field data and machine design.

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Replacement and maintenance analysis of longwall shearer using fault tree technique

Cited by 16 publications

References 2 publications

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Optimization of Mine Down-Hole Equipment Maintenance Strategy based on Fault Data

Reliability analysis of drum shearer machine at mechanized longwall mines

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