Human Factors in Aircraft Maintenance Errors: Structured Review and Probability Modeling

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@article{1715572,
      author = {Nazmul Hasan Anik Chawdhury, Mahabub Sultan},
      title = {Human Factors in Aircraft Maintenance Errors: Structured Review and Probability Modeling},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2487-2494},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715572.pdf},
      abstract = {Aircraft maintenance remains a high-consequence activity because latent deviations may survive task completion and only become visible during later operations, inspections, or abnormal conditions. This paper revises the original structured critical review by integrating a literature-informed predictive modeling demonstration based on fatigue, workload, documentation quality, training quality, handover quality, and experience. The review confirms that maintenance error is rarely the product of a single unsafe act. Instead, it emerges from the interaction of technician-level performance limits, local task conditions, procedural quality, supervisory choices, and broader organizational pressures. Fatigue and workload remain recurrent risk amplifiers, whereas documentation quality, competence development, communication quality, and organizational learning act as protective controls when they are operationally robust. To translate these mechanisms into a forward-looking safety tool, an illustrative logistic regression framework was fitted to a simulation-based dataset of 600 task-level observations derived from literature-consistent directional assumptions. The model achieved an area under the receiver operating characteristic curve of 0.777 on the hold-out set, with the strongest positive coefficients observed for workload and fatigue, while documentation quality emerged as the strongest protective predictor. Scenario analysis further showed a predicted error probability of 92.9% under high-fatigue, high-workload, poor-documentation conditions, compared with 1.4% under low-fatigue, better-documented, better-trained conditions. These results should not be interpreted as external validation of a deployable airline safety model, because the current exercise is simulation-based rather than trained on real organizational event records. Nevertheless, the combined review and model demonstrate how retrospective human-factors knowledge can be operationalized into prospective risk indicators inside maintenance safety management systems.},
      keywords = {aircraft maintenance; human factors; maintenance error; aviation safety; HFACS-ME; fatigue; workload; documentation quality; logistic regression; safety management system.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715572}
  }