Jul 2026· SAE technical paper series· 0 citations· 11 references
Abstract
The reliability of aviation maintenance personnel directly impacts flight safety, yet systematic methodologies for the quantitative prediction of human error probability (HEP) in this domain remain lacking. To address this gap, a novel human factors reliability analysis method for aviation maintenance is proposed, extending the SPAR-H model through Evidential Reasoning (ER). This method is implemented as follows: Maintenance tasks are decomposed into subtasks. Subsequently, the eight types of Performance Shaping Factors (PSFs) for each subtask are evaluated by domain experts according to defined PSF levels. Expert judgments are then aggregated using Evidential Reasoning theory, enabling the calculation of aggregated PSF levels. These aggregated levels are interpolated to determine the corresponding impact multipliers. Finally, the HEP for aviation maintenance operations is calculated by integrating the SPAR-H basic error probability model with task series/parallel logic rules. The proposed methodology is validated using an inspection operation case study. This study establishes a methodological framework for human factors reliability analysis in aviation maintenance, providing a theoretical foundation for developing scientifically grounded prevention and control measures to enhance aviation safety levels.
Aviation maintenance remains a safety‑critical domain where human‑factor errors persist despite technological progress and regulation. Traditional human‑factors approaches are largely retrospective, offering limited predictive insight when task design, cognitive workload, fatigue, and procedural ambiguity exceed human capacity. This study develops and validates a Human Performance Integration (HPI) Model, a predictive, engineering-oriented framework linking maintenance task design with cognitive constraints and error probability. A mixed‑methods design combines technician interviews (n = 21) and 47 incident reports from a regional airline. Principal Component Analysis (PCA) and Support Vector Machine (SVM) classification models non‑linear interactions among workload, fatigue, task complexity, and documentation clarity. The HPI model functions as a proactive design support tool, enabling engineers to evaluate task structures and workload allocations, embedding human performance limits directly into safety critical design decisions.
Idriss Dagal, Bilal Erol· Eksploatacja I Niezawodnosc-...· 0 citations
Human factors remain one of the leading contributors to aviation accidents despite continuous advancements in aircraft technology and safety regulations. This study examined the critical role of human factors in aviation accident prevention, focusing on Crew Resource Management (CRM), Training and Competency Development, and Safety Culture among Aircraft Maintenance Technology (AMT) students, licensed aircraft mechanics, and on-the-job training (OJT) trainees at Indiana Aerospace University during the Academic Year 2025–2026. An explanatory sequential mixed-method research design was employed, integrating quantitative survey data with qualitative interview responses to provide a comprehensive understanding of maintenance-related human factors. A total of 90 respondents participated in the quantitative phase, while 10 participants were purposively selected for the qualitative inquiry. Descriptive statistics, including frequency, percentage, weighted mean, and ranking, were utilized to analyze quantitative data, whereas thematic analysis was applied to qualitative responses. The findings revealed that respondents strongly agreed on the critical importance of CRM, Training and Competency Development, and Safety Culture in preventing aviation accidents. Teamwork, communication, situational awareness, competency-based training, and organizational commitment to safety were consistently identified as essential elements for minimizing maintenance-related errors. However, the study also identified persistent challenges, including communication breakdowns, insufficient emphasis on human factors education, limited practical exposure, inadequate maintenance resources, ineffective shift turnovers, and unclear task allocation. These issues indicate the need for stronger integration of technical and non-technical competencies within aviation education and maintenance organizations. Based on the findings, an action plan emphasizing continuous human factors training, standardized communication protocols, simulation-based learning, and the promotion of a proactive safety culture was developed. The study contributes empirical evidence that supports curriculum enhancement, organizational safety initiatives, and evidence-based strategies for reducing maintenance-related human errors and strengthening aviation safety.
Brian Robinson, Lance Frederic Arcosa, Khazim Joseph Cagigas et al.· Journal of Advanced Studies...· 0 citations
Operational hazard analysis of aviation system operations must consider interactions among weather, ATC actions, airspace constraints, aircraft operations, and human factors - distinct from the functional hazard assessment applied at the aircraft-system level. We present an AI-assisted approach that generates candidate hazard scenarios from NASA's Aviation Safety Reporting System (ASRS). Given a target adverse outcome, it produces a structured hypothesis as categorical factors and a narrative scenario describing an operational event sequence consistent with the structure. Each scenario includes by a plausibility score from historical co-occurrence evidence and traceability to the most similar held-out ASRS reports. We then propose a hybrid variant, conditioning narrative generation on a structured hypothesis produced via evolutionary abduction, improving correctness and reducing variability. We evaluate multiple large language models, zero-shot versus few-shot prompting, and optional fine-tuning, measuring how prompting and model choice affect the validity and realism of the generated structures and narratives.
Cristian Mascia, R. Pietrantuono, Daniel Rodríguez et al.· 0 citations
This study aims to improve maintenance reliability by strategically allocating the workforce, considering human factors and knowledge management in critical asset maintenance within the public transport sector.
The study proposes a quantitative-applied approach integrating multi-criteria decision-making (MCDM), an extended risk priority number (ERPN) for task criticality and a modified priority matrix. The algorithm optimizes resource allocation by mathematically combining operator technical aptitude and willingness.
Implementation of the algorithm resulted in an average 47% reduction in repair times for critical machinery. This operational efficiency generated an estimated annual saving of $91386.77, proving that 98% of the economic benefit stems directly from minimizing asset downtime rather than reducing direct labor costs.
This study was applied in a single transport company, and the results are specific to its operational constraints, which limits direct generalizability to other industrial sectors. Tacit knowledge quantification and task prioritization relied on expert consensus. The model currently assumes full staff availability and does not account for simultaneous unexpected failures.
The methodology provides maintenance managers with a structured, data-driven tool to transition from subjective, ad-hoc personnel assignments to an objective protocol. It allows for the systematic integration of knowledge management into computerized maintenance management systems (CMMS), optimizing hour-machine productivity across heavy fleets.
The formal recognition of tacit knowledge promotes equity in task allocation and addresses the human reliability gap. By mitigating unequal workloads and recognizing individual technical aptitude, the proposed framework fosters a transparent, motivating and highly engaged work environment, which is critical for sectors operating under severe operational pressure.
This study addresses a critical gap in industrial fleet maintenance by mathematically operationalizing not only verified tacit knowledge but also operator willingness, integrating human attitude as a quantifiable variable to reduce system execution entropy.
Cristian García García, Mary Josefina Vergara Paredes, Javier Cárcel-Carrasco et al.· Journal of Quality in Mainte...· 0 citations
The implementation of the ground deceleration function in civil aircraft represents a critically complex process that deeply relies on the seamless collaboration of multiple onboard systems, including but not limited to braking, thrust reversal, spoiler, and steering systems. The operational logic governing these systems is highly intricate, characterized by tightly coupled interactions, stringent safety requirements, and a vast array of diverse physical and logical interfaces. This inherent complexity makes it exceptionally difficult to gain a thorough, system-level understanding of the implementation mechanisms and collaborative principles solely through traditional means of examining extensive, yet often fragmented, design documentation. The limitations of document-based analysis frequently lead to unforeseen integration conflicts, which are typically discovered late in the development cycle, resulting in substantial rework costs and project delays. To address this pervasive industry challenge, this paper selects the aircraft ground deceleration function as a representative case study and proposes an innovative, simulation-based validation methodology. This approach systematically utilizes model state machines to create a dynamic digital representation of the system-of-systems, enabling rigorous validation of aircraft deceleration requirements under various operational scenarios. By adopting this model-based systems engineering (MBSE) paradigm for mechanism representation, our approach effectively captures the nuanced coordination, timing dependencies, and dynamic interactions within the multi-system operational logic. It thereby facilitates the intuitive identification, analysis, and resolution of potential design flaws, including logical conflicts, deadlocks, race conditions, and uncovered or ambiguous requirements. Consequently, the method not only provides a robust framework for validating the aircraft’s function-related design requirements with greater confidence but also offers crucial, data-driven support for the iterative optimization and evolution of the overall functional architecture. The fundamental value proposition of this research lies in its transformative capability to convert implicit design knowledge and assumptions—originally scattered across voluminous documents, specifications, and expert minds—into an integrated set of executable, observable, and analyzable formal models. This digital thread enables systems engineers and designers to identify deep-seated integration and coordination issues proactively during the early conceptual and detailed design stages, rather than relying on discovery during the late, costly integration and testing phases. By shifting validation left in the development V-cycle, this approach significantly reduces the risk of major design changes and associated cost overruns later in the project lifecycle. Ultimately, it effectively enhances the overall maturity, safety, certifiability, and operational reliability of complex aircraft function development, paving the way for more efficient and predictable engineering processes.
Mingqian Wang, Q. Yu, Miao Yu et al.· SAE technical paper series· 0 citations
ABSTRACT The aim of the study was to analyze effective models and algorithms of decision support that contribute to enhancing aviation safety. The study employed decision-making task formalization, probabilistic modeling, machine learning, multi-criteria analysis, and data analysis methods for processing heterogeneous information, including technical parameters, video streams, and behavioral characteristics. The research identified key features of decision support models and algorithms that improve aviation safety through real-time monitoring and adaptive response. Machine learning methods achieved up to 92% accuracy with a response time of 80 milliseconds, while multi-criteria analysis methods, including the analytic hierarchy process, reached 88% accuracy. The integration of probabilistic models with adaptive algorithms enabled consideration of operational environment variability and timely risk assessment. In 2024, 45% of aviation incidents were associated with crew error, 24% with technical malfunctions, and 13% with weather conditions. Intelligent decision support systems could potentially prevent 75% of incidents related to procedural violations. Neural networks were most suitable for behavioral analysis, decision trees for access control, and Bayesian networks for assessing technical failures. The findings contribute to the development of intelligent data analysis methods and decision support algorithms for aviation safety.
N. Dolzhenko, Arman Sagimov, Gulnara Aitmukhanova et al.· Journal of Aerospace Technol...· 0 citations