A Method of Applying Flight Data to Evaluate Landing Operation Performance

What can we learn from severity index on flight data monitoring? Analysis of safety resilience in flight operations during COVID-19 disruptions

The unexpected spread of the pandemic raised concerns regarding pilots' skill decay resulting from the significant drops in the frequency of flights by about 70%. This research retrieved 4761 Flight Data Monitoring (FDM) occurrences based on the FDM programme containing 123,140 flights operated by an international airline between June 2019 and May 2021. The FDM severity index was analysed by event category, aircraft type, and flight phase. The results demonstrate an increase in severity score from the pre-pandemic level to the pandemic onset on events that occurred on different flight phases. This trend is not present in the third stage, which indicates that pilots and the safety management system of the airline demonstrated resilience to cope with the flight disruptions during the pandemic. Through the analysis of event severity, FDM enables safety managers to recommend measures to increase safety resilience and self-monitoring capabilities of both operators and regulators.Practitioner summary: The onset of the pandemic led to a rise in the severity of flight data monitoring events in a large airline, likely linked to a lack of operational practice and skills decay. This was demonstrated across different flight phases and aircraft types. In the settled pandemic period, the severity index returned to pre-pandemic levels, indicating that the resilience of individual pilots and safety management systems is critical to operational safety.HIGHLIGHTSThe FDM event severity scores significantly increased following the pandemic onset, especially for event categories involving pilot core competencies.The FDM event severity scores stagnated or decreased during the later pandemic stage indicating resilience among the airline pilots and the airline's safety management system.The airline and pilots demonstrated resilience by effectively mitigating the effects of proficiency decay which took place as the pandemic started.FDM analysis has shown to be effective in establishing a proactive SMS programme to mitigate the negative impacts of the pandemic on aviation safety.

Investigating the impacts of COVID-19 on aviation safety based on occurrences captured through flight data monitoring

The COVID-19 pandemic led to growing concerns about pilots' proficiency due to the significant decrease in flight operations. The objective of this research is to provide a proactive approach to mitigate potential risks in flight operations associated with the impact of the COVID-19 pandemic using flight data monitoring (FDM). The results demonstrated significant associations between the pandemic impacts and FDM exceedance categories, flight phases and fleets. Manual flying skill decay, lack of practice effects on use of standard operating procedures and knowledge of flight deck automation should be considered by airlines when preparing for the return to normal operations. An FDM Programme allows prediction of the probability and severity of occurrences for developing an effective SMS within an airline. To mitigate the impacts of the pandemic, tailored training sessions must be implemented, and airlines should strive to avoid additional optional procedures where practicable. Practitioner summary: The COVID-19 pandemic has raised concerns regarding pilot proficiency due to lack of practice effects. Results from the Flight Data Monitoring Programme show significant associations between the pandemic impacts and occurrence categories, fleets, and flight phases. FDM can be applied to mitigate the probability and severity of occurrences for airlines developing effective safety management systems.HIGHLIGHTSThere is a significant association between the COVID-19 pandemic stages and FDM events in different flight phases, FDM categories, and aircraft typesThe COVID-19 pandemic led to a significant increase in FDM exceedances, especially for precursors on runway excursion and go-aroundsAirlines should carefully plan training sessions for pilots as the disruptions due to the pandemic led to a lack of practice effect in flight operationsReviewing FDM data may have contributions to establish proactive SMS and mitigate COVID-19 impacts to aviation safety.

The risk-taking behavioural intentions of pilots in adverse weather conditions: an application of the theory of planned behaviour

This paper examined pilots' risk-taking behavioural intentions based on the theory of planned behaviour, as well as the impact of experience on behavioural intentions in adverse weather conditions. Two hundred and seventy-three airline pilots and flying cadets were divided into two groups and asked to complete a questionnaire based on two decision-making scenarios. This questionnaire measured pilots' intentions to take risks, along with the attitude towards the behaviour, subjective norms, perceived behavioural control (PBC), risk perception, and self-identity. The results showed that attitude, subjective norm, PBC, and risk perception explained 52% of the variance in behavioural intentions. Additionally, pilots' risk-taking decisions can be influenced by experience. Inexperienced pilots had a relatively stronger intention to take risks and a more favourable attitude towards risky behaviour. Moreover, pilots were more likely to rely on their own direct experience in the decision-making process. Practitioner summary: This study examined the pilots' risk-taking intentions under adverse weather conditions using a questionnaire based on the TPB theory. Results demonstrated that the TPB model can be applied to the risk-taking scenario and that experience can influence pilots' decisions. These findings have implications for improving flight safety and lowering accident rates.

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.