Quantification of human error in maintenance using graph theory and matrix approach
| Autor: | O. P. Gandhi, V. N. Aju kumar |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
business.industry
Computer science Human error Value (computer science) Absolute probability judgement Digraph Graph theory Management Science and Operations Research Machine learning computer.software_genre Expression (mathematics) Reliability engineering Identification (information) Artificial intelligence Safety Risk Reliability and Quality business Human error assessment and reduction technique computer |
| Zdroj: | Quality and Reliability Engineering International. 27:1145-1172 |
| ISSN: | 0748-8017 |
| DOI: | 10.1002/qre.1202 |
| Popis: | Assessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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