Human reliability analysis and optimization of manufacturing systems through Bayesian networks and human factors experiments: A case study in a flexible intermediate bulk container manufacturing plant

Wang, Yaliang; Ding, Yangke; Chen, Guoda; Jin, S.

doi:10.1016/j.ergon.2019.05.001

Cited by 20 publications

(15 citation statements)

References 22 publications

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“…We showed that when a worker was in a poor state, the most vulnerable unsafe behaviour was violation, followed by decision-making errors. The conclusion is consistent with [18], which determined the weights of the accident-causing factors in China's coal mine accidents based on the HFACS model and AHP method. Furthermore, in this work, insufficient experience is the most significant contributory factor to unsafe behaviour.…”

Section: Discussionsupporting

confidence: 74%

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Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

2019

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Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.

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Section: Discussionsupporting

confidence: 74%

“…The network has, in recent years, been implemented in diverse research areas related to safety. For instance, Wang [18] examined human reliability through a BN model and human factors experiments. Garcia-Herrero [19] used BN to determine the relationships between organisational culture and safety culture in a nuclear power plant.…”

Section: Introductionmentioning

confidence: 99%

Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

2019

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“…Each GEP aims to provide a general description of a task and an estimated HEP rate for such a general task. 25–30,32,33,35 Table 2 shows the general task types and GEP values. These task types and values are values calculated by the HEART method.…”

Section: Methodsmentioning

confidence: 99%

“…The findings reveal that the most important causes of human factor should be determined correctly. 32 In this respect, Wang et al 33 applied their study an optimization through a Bayesian network (BN) model and human factors experiments (HFEs) for a flexible intermediate bulk container manufacturing plant. They trained the workers according to the fault diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

A Human Reliability Analysis to Crankshaft Overhauling in Dry-Docking of a General Cargo Ship

Bicen

Kandemir

Celik

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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This study conducts a practical application of shipboard operation human reliability analysis (SOHRA) to a crankshaft overhauling operation of a general cargo ship at dry-docking period. The SOHRA approach includes error producing condition (EPC) and general task type (GTT) parameters to consistently calculate the human error probability (HEP) values of operation steps. In this case, a comprehensive overhauling of main engine was planned at shipyard since the ship has experienced a catastrophic failure. An onboard survey to ship engine room is conducted to monitor the operational conditions. The targeted operation, involves disassembly, maintenance, and reassembly stages, is monitored based on 39 sub-tasks. According to the initial findings, immediate recovery actions are suggested to eliminate critical safety issues in a timely manner. Moreover, an extended discussion through long-term safety recommendations are also provided. The results revealed from case study illustrates that HEP values in maintenance operations are sensitive to ship operating conditions. The proposed approach is found very useful by company executives to support ship technical superintendents in critical operation monitoring. The further study is considered to develop mobile application of SOHRA specific to maintenance operations onboard ships.

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“…Salehi et al presented a manufacturing line balancing problem that takes into account the skill of the operator in a fuzzy environment [28]. Wang et al attempted to reduce errors in a manufacturing process by applying ergonomic elements and Bayesian networks [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Jung

Park²,

Lee³

et al. 2020

Applied Sciences

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The measurement of sewing work in the labor-intensive garment industry depends considerably on the person performing the measurements, making it difficult to quantitatively define the level of skill (LS) of the sewing machine operator and the level of difficulty (LD) of the unit process. In this study, a power monitoring system attached to the sewing machine was used to remotely collect power consumption data, which were then analyzed to extract the working times for a series of sewing tasks. LS of each operator was then classified and LD of each process was analyzed in terms of working time and quality. Finally, the resulting LS and LD weight factors considered to optimize the subject garment production line were compared against those proposed by experts. The LS weight factor proposed by the experts was ~15% less than that indicated by the experimental results, whereas the LD weight factor proposed by the experts was ~15%–40% greater than that indicated by the experimental results. The results of this study suggest that the proposed method could be applied in real time to inform the arrangement of line workers to increase the productivity of a garment production line.

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Human reliability analysis and optimization of manufacturing systems through Bayesian networks and human factors experiments: A case study in a flexible intermediate bulk container manufacturing plant

Cited by 20 publications

References 22 publications

Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

A Human Reliability Analysis to Crankshaft Overhauling in Dry-Docking of a General Cargo Ship

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

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