Methodology
Methods have been introduced to present an aircraft system analysis. The Human Factors Analysis HFACS is probably the most popular used human error method for accident investigations and analysis in aviation. The method is based on Reason’s organizational theory of human error that claims one too many recordings of situation tokens to hazardous acting tokens. This model is detrimental for monitoring future error results from their organizational precursors. Traditional quantitative approaches cannot be applied in such situations. Still, according to Harris and Li (2019), “neural networks might be a feasible approach to predicting the HFACS domains of unsafe actions from their adverse psychological precursors” (p. 185). They claim that this method is also compatible with the logical foundation of the error. It is suggested that this analytical method illustrates the state of modern procedures, where there are several interactive variables at work. Aircraft Neural networks can simultaneously predict multiple results from many input variables representing the complexity of aircraft the system exclusively instead of just characterizing errors resulting from mere cause and consequence (Bro, 2017).
Presently, Quick Access Recorder and various aircraft regulation research done on the pilot’s efficiency and operating environment based on actual flight data. The QAR software can record all types of aircraft model and mechanical parameters (Wang et al., 2018). Aircraft authorities across the world need all commercial planes to mount aviation QAR to track pilot performance and device behavior. The screening process indicates that the method is appropriate for evaluating landing results; the expanded implementation shows how this method may be used to predict possible risks related to the landing process across different airports. Moreover, the method can draw a comparison of landing accomplishment among various fleets.
Rationale
The analysis of human factors in the aircraft industry is critical for dealing with the mitigation of errors in an essential part of the overall aviation business. This is particularly important as employees are faced with systemic challenges across all sectors of industry and in the aviation sector in particular. in details, the rationale is:
(1) To encourage a positive perception of the human position in aviation safety.
(2) To address the QAR as a measurement and control method for human factors involvement; the QAR application to aircraft regulations for flight control which will be discussed later
illustrates how human factors programs such as QAR can enhance the efficiency of an organization.
(3) To study from a similar area, health, and safety in the aircraft sector and to acknowledge the background of healthcare professionals in relating health conditions of various employees regarding the safety of flight operations.
(4) To interpret the knowledge of flight system analysis to human safety features and propose a tool that will enhance human safety in flights.
Safety culture, for instance, encourages not only the safety of employees but also their health status, so excessive and compromised mode problems are better understood and treated, and this will, in turn, reduce the stress of aircraft employees and, as a result, reduce errors which can lead to aircraft accidents. Health and safety are always related in that they share similar organizational factors, such as production pressure. Health management is also a related area that safety professionals should learn and understand. The paper will seek to accomplish its objectives by developing ideas from various studies on health management and human factors in the aircraft industry. The need to incorporate human factors in safety management is acknowledged and should be highly prioritized.
References
Harris, D., & Li, W. C. (2019). Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts. Ergonomics, 62(2), 181-191.
Wang, L., Ren, Y., & Wu, C. (2018). Effects of flare operation on landing safety: A study based on ANOVA of real flight data. Safety Science, 102, 14-25.
Bro, J. (2017). FDM Machine Learning: An investigation into the utility of neural networks as a predictive analytic tool to go around decision making. Journal of Applied Sciences and Arts, 1(3), 3.