Semi‐Markov reliability model of manufacturing systems considering machine degradation, human errors, and their mutual facilitation effects

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The occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism.

Section: 24mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

Li,

Guo,

Zeng

et al. 2024

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“…Time-on-task fatigue indicates that as time on task increases, attentional lapses could be increased that adversely affect perception and cognitive action. 34,47 Without any consideration about the type of tasks that have been performed, it is completely obvious that individual abilities in the first time period are somewhat different from the last one.…”

Section: Select the Influential Parameters On Fault Rate And Workloadmentioning

confidence: 99%

Reliability optimization model in man‐machine systems considering human factors in uncertain situations

Saidi‐Mehrabad,

Atashfeshan,

Razavi

2023

Over the last four decades, researchers have recognized that traditional automation has many negative consequences stemming from human out‐of‐the‐loop problems. However, there is a paucity of research to optimize function allocation problems under uncertainty by considering both human and machine influential factors. This work attempts to address this standing paucity of research by designing an optimization model with the objective function of maximizing system reliability besides balancing human workload over a mission time and keeping him/her in the control loop. To maximize system reliability, the prediction of human and machine fault rates is inspired by the human‐in‐the‐loop fault tree analysis (FTA). Furthermore, the failure probabilities of human‐related events in the proposed fault tree are estimated via fuzzy logic inference systems. A typical supervisory control task is selected to demonstrate the application and feasibility of the proposed method. In conclusion, this mathematical model as a decision support method provides guidance for automation designers to improve an automated system via systematic prediction of failures in the early stage of the design phase.

“…The basic events of FT can involve HEs, MFs, and AE, while they are usually assumed to be statistically independent. 28 To deeply analyze HE which is a critical factor causing system failure 29,30 or even widespread adverse consequences, 31 many FTA methods combined with task analysis (TA) and human reliability analysis (HRA) are proposed. 22,28,32,33 TA shows how the operator performs tasks sequentially through interacting with colleagues or machines, and HRA shows what HEs may occur during performing each step of tasks.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of man‐machine systems under safety‐critical multitasking situations

Che,

Li,

Zeng

et al. 2024

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Multitasking is increasingly common in highly complex and safety‐critical systems, especially under abnormal situations. Mental overload (MOL) may occur and result in forgetting/mistaking tasks uncertainly. Such probabilistic errors could have catastrophic consequences and contribute greatly to the multitasking risk of man‐machine systems. In this paper, to better identify the risks of safety‐critical multitasking situations, MOL mechanism is investigated and a risk analysis method considering MOL is proposed. MOL occurs when the demand for resources estimated by Multiple Resources Model exceeds an operator's ability. Then, the operator's performance degrades, and s/he tends to mistake or abandon parts of tasks. Based on the MOL mechanism, a MOL‐performance dependency (MOL‐PDEP) gate is proposed to incorporate MOL into risk analysis. Its inputs are concurrent tasks, and it triggers the related hazardous human behavior events with certain probabilities if MOL occurs. Through this gate, the dependence among these events and their nondeterministic cause relationships to MOL are added to traditional fault tree (FT), which presents a challenge issue to FT analysis. An implicit method is proposed to analyze the FT with MOL‐PDEP gate and calculate the accident probability. A case study on a helicopter crash accident demonstrates the effectiveness of the proposed method.