A rapid increase in the occurrence of loss of control in general aviation has raised concern in recent years. Loss of control (LOC) pertains to unique characteristics in which external and internal events act in conjunction. The Federal Aviation Administration (FAA) has approved an Integrated Safety Assessment Model (ISAM) for evaluating safety in the National Airspace System (NAS). ISAM consists of an event sequence diagram (ESD) with fault trees containing numerous parameters, which is recognized as casual risk model. In this paper, we outline an integrated risk assessment framework to model maneuvering through cross-examining external and internal events. The maneuvering is in the critical flight phase with a high number of LOC occurrences in general aviation, where highly trained and qualified pilots failed to maintain aircraft control irrespective of the preventive nature of the events. Various metrics have been presented for evaluating the significance of these parameters to identify the most important ones. The proposed sensitivity analysis considers the accident, fatality, and risk reduction frequencies that assist in the decision-making process and foresees future risks from a general aviation perspective.
Aviation accidents are recognized as the most tragic accidents among all modes of transportation because of their serious nature and high number of injuries and deaths. Nevertheless, the increase in air travel over the past decades reflects consumers’ recognition of air travel as the safest and fastest mode of transportation. An accident can happen any time in aviation due to various factors: aircraft types, flight crew skills and experience, environmental changes, and so forth. A flight is categorized on basis of its purpose (e.g., commercial, private, or military) and operation (agriculture, sightseeing, herding, air ambulance/emergency medical services (EMS), and border surveillance).
Traditionally, commercial aviation has been given much importance because it involves a high number of operations and consumers. It has been favored in the development of advanced and sophisticated safety measures, which has resulted in a decline in air accidents. On the other hand, operations in general aviation (GA) have increased over the past decades, resulting in an increase in accident rate: “six times higher than for small commuter operations and 40 times higher than for transport category operations” [
Different aviation communities have worked on upgrading safety parameters by categorizing flight phases as follows: takeoffs, landings, traffic patterns, stalls, altitude recovery, controlled flights into terrain, and. [
The FAA has approved an Integrated Safety Assessment Model (ISAM) tool for evaluating safety in the National Airspace System (NAS) for current and NextGen proposals. It consists of a set of an event sequence diagram (ESD) and a fault tree (FT) for capturing specific scenarios. Factors quantified in a probabilistic model consist of organizational, human, technical, and environmental factors. It has achieved safety improvements without undermining the benefits of efficiency and capacity. The baseline risk assessment evaluates the system impact on the basis of historical data that provides an opportunity to value its impact with or without systems integration, for example, air traffic, airport, vehicles/aircraft, and operations [
The LOC has accounted for high casualties and death rates for decades but gained prominence recently [
The maneuvering flight phase is the most vulnerable to loss of control, where a highly trained pilot failed to main aircraft control irrespective of the preventable nature of an event. The study in [
Probabilistic Safety Assessment (PSA) is a tool to assess the event risk in the system design. It was adopted and previously used to investigate the most sensitive parameters in mechanical and nuclear facilities [
The fault tree branches represent components in a system (e.g., may or may not function). The details are extracted from fault trees by tracking them down to the main root cause (e.g., system failed to respond, activated, and operate). Each node at which the branch divides is governed by gates “OR” and “AND” based on eliciting status that is linked with the top fault tree event. The probabilities are assigned on the basis of events that contribute to upper event probability and continue to the top main event. We used the ESD of controlled flight into terrain (CFIT) to model the maneuvering phase. The pivoting event categories of CFIT remained the same for the aircraft maneuvering phase, that is, loss of situational awareness (LSA) and crew resource management (CRM). We populated events under pivoting event trees representing event scenarios (externally and internally) for aircraft LOC. In Figure
ESD used for LOC-I during aircraft maneuvering.
This study used the mapping approach to reflect maneuvering parameters in internal and external events for safety risk assessment. The external and internal events influence each other and are calibrated into event tree and fault tree to reflect accident progressions in sequence scenarios within a system. Thus, external events map the internal events on the basis of accident progression. In case of similarity in accident progression, the same ET would be used to represent the scenario as an internal event. While having different accident progressions, the initiator of the ET is replaced with an external event by mapping internal initiators into them. In addition, the failure of several components by external events may invoke system effects that reflect an external FT and an internal event FT. This is explained using a simple example with the Boolean algebra equation of Figure
ESD-controlled flight into terrain.
The two risks in a PSA model are described by a failure’s sequences represented as “System A failure” and “System B failure.” The triangles in Figure
Example of fault tree (FT).
In continuation of the main PSA model, the two systems of ET and FT resulted in a one-top FT event which presents all accident sequences and the system failure model, reflecting a wide angle under a single identity. The calculation of Boolean’s operation would give a minimal cut-set for the entire system as shown in Figure
Example of integrated FT for aircraft collusion (entire system failure).
The events are modelled as explicit (independent) in the above approach. However, this approach did not represent a true picture because the events showed dependency and shared causes in component failure [
We went further by applying a nonstaggered testing method. In continuation of the above example, a component group size “ All four components failing independently (i.e., Two independent failures in six combinations and two CCF components (i.e., Two CCF failures in three combinations and two other CCF components (i.e., One independent failure in four combinations and three CCF components (i.e., All four CCF component failures in one combination (i.e.,
Example of fault tree representing alpha factor model, CCG = 4.
The PSA model supports an independent occurrence of a basic event, which is where it differs from the CCF model. The CCF failure effect on the fault tree model negates some of the benefits of redundancy, where redundant elements or components provide extra protection against failures. Thus, CCF assists in estimating intermediate failure and defining the common cause failure to multiple end states. The cut-set method is used to rank the top even probability which is derived from basic event sets. The system unreliability
BDD method graphically represents data in the form of binary tree and is proposed for the reliability analysis [
Example of ESD underlying fault tree.
The conversion process from the fault tree to the BDD is completed in two steps. First, the fault tree is converted into a binary decision, for example, an if than else
Conversion of fault tree to BDD.
The equation for pivoting event 1 will be
Let
Apply the above rules on the gates, where the above equation will be First, conversion of every fault tree to a BDD Second, BDDs to DBDDs conversion for a nonoccurrence case Third, paths to each end state and integrate BDD with BDDs and/or DBDDs Fourth, evaluating these combined BDDs
Example of combined BDD at the end state.
We developed a list of parameters for the maneuvering phase based on NTSB investigation reports of accident occurrences. These parameters were used in developing fault trees for event scenarios that have outcomes in successful and failure/partially successful situations. Various initiating events are modelled as a component node under an integrated framework forming an event tree as discussed earlier. For example, ice/snow, inadequate wind compensation, and so forth can create problems in crew performance, engine seize, malfunction, and others. Each node in a tree represents a component with a breach of its system safety, which determines the flow chain on each node. The baseline events nodes for the maneuvering phase are listed in “Possible Baseline Fault Tree of Maneuvering Phase Nodes for GA Inflight LOC” in Abbreviations. However, a complete list of fault tree events representing all nodes is enclosed in “Possible Fault Tree of Maneuvering Scenario Nodes for GA Inflight LOC” in Abbreviations.
The assignment of risk and risk reduction measures in the fault tree requires a supplementary step, wherein failure modes are processed with hazard identification. The list of hazards is categorized into four groups: technical, environmental, organizational, and human factors. The mapping of categories in an event serves the purpose of ISAM integration and validation. The assignment of hazards to the base event/parameter can be understood by a communication failure example as shown in Figure
Example of hazard mapping to base event in fault tree.
In next phase, this study will proceed further by assigning probability in order to quantify the maneuvering ESD and fault trees based on a historical dataset. The data used is from 2008 to 2015. The accident data and investigation reports for GA flights in the United States were extracted from National Transportation Safety Board (NTSB) investigations [
The ISAM is one of the recommended toolsets and methods to evaluate risk parameters for air transportation [
An advantage of using a casual model is its hybrid nature, where ESD and FT express event scenarios working side by side as shown in Figure
Example of hybrid model.
Example of event sequence diagram.
There are many methods to estimate importance measures to identify top significant events in a system [
Importance measures.
Matrices | Principle |
---|---|
Fussell-Vesely (FV) |
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Risk achievement worth (RAW) |
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Risk reduction worth (RRW) |
|
The probabilities representing the event significance are conditional to baseline event probabilities that are derived from historical dataset. Some events are assigned a zero value based on their nonoccurrence, which did not mean that they have a true probability value to be zero, because it may occur in future.
The factorial design approach is considered to be less sensitive to baseline values. It analyzes the effects of several parameters for dependent variables. Each factor consists of a high level and a low level value. The full factorial design is helpful in understanding relations between parameters through reflecting variations in parameters. We applied the factorial design model to each fault tree event in order to obtain events with a high effect on the maneuvering scenario. In this regard, recent studies on the aviation safety model have used a factorial design on parameters to evaluate sensitivity [ First, identify low and high probabilities for each event, where 0 represents low value and 1 represents high value. Second, work out all combinations of Third, an event effect on accident scenario is computed by calculating the difference in the average accident frequency of low probability from the average accident frequency of high probability. Fourth, rank each event’s effects.
The advantage of factorial design over others is that the results are based on the high and low values of each factor (i.e., 0 and 1 probability) rather than being dependent on baseline values. In the following section, we discuss the results of accident frequency, fatality frequency, and risk reduction frequency, based on aforesaid importance measures.
We applied importance measures in the model to identify significant parameters. A similarity is observed in results of RRW and factorial design, which are quite different from FV as shown in Figure
Importance measure-event’s ranking based on accident frequency.
The reason for the difference in FV results from others is FV’s criteria for the closeness to zero. It indicates a limitation where it coincides with the base value, for example, the accident frequency when the probability of a single event “
It is understood that event frequency is not the same as consequences. The accident consequences can distinguish from frequency by determining the probability of an accident fatality. The fatality probability shows the probability of an accident event to occur. The fatality probability data is comprised of hazard estimation values for parameters. For maneuvering phase parameters, we used a checklist to estimate fatality risk that was published in the Flight Safety Foundation (FSF) handbook on risk management [
Risk assessment factors.
Destination risk factors | Value |
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ATC approach radar with MSAWS | 0 |
ATC minimum radar vectoring charts | 0 |
ATC radar only |
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ATC radar coverage limited by terrain masking |
|
No radar coverage available (out of service/not installed) |
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No ATC service |
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|
|
Airport located in or near mountainous terrain |
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ILS | 0 |
VOR/DM |
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Nonprecision approach with the approach slope from the FAF to the airport TD shallower than 2 3/4 degrees |
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NDB |
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Visual night “black-hole” approach |
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|
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Complete approach lighting system | 0 |
Limited lighting system |
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|
|
Controllers and pilots speak different primary languages |
|
Controllers’ spoken English or ICAO phraseology is poor |
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Pilots’ spoken English is poor |
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|
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No published departure procedure |
|
|
|
Risk multiplier | Value |
|
|
|
|
Scheduled | 1.0 |
Nonscheduled | 1.2 |
Corporate | 1.3 |
Charter. | 1.5 |
Business owner/pilot | 2.0 |
Regional | 2.0 |
Freight | 2.5 |
Domestic | 1.0 |
International | 3.0 |
|
|
Australia/New Zealand | 1.0 |
United Stated/Canada | 1.0 |
Western Europe | 1.3 |
Middle East | 1.1 |
Southeast Asia | 3.0 |
Euro-Asia (Eastern Europe and Commonwealth of Independent States) | 3.0 |
South America/Caribbean | 5.0 |
Africa | 8.0 |
|
|
Night—no moon | 2.0 |
IMC | 3.0 |
Night and IMC | 5.0 |
|
|
Single-pilot flight crew | 1.5 |
Flight crew duty day at maximum and ending with a night nonprecision approach | 1.2 |
Flight crew crosses five or more time zones | 1.2 |
Third day of multiple time-zone crossings | 1.2 |
We noted events with high risk, “CCF (unable to avoid stall, inadequate speed),” “poor manual flight control,” “certification/inadequate remedial action/lack of experience,” and “inadequate flight procedure,” to be significant as shown in Figure Mostly used gates in a fault tree are “OR” gates. In case of “AND” gates, the events become less significant because they only occur with a condition of failure of both underneath events, which makes a subevent less important. Under a fault tree structure, the importance measures are the same for events of “0” probability. In order to determine significance of component, one risk importance measure could be sufficient. Based on the above-mentioned discussion, we will proceed with factorial design in the following analysis work as it relies on high and low value rather than being dependent on the baseline value.
Comparison of fatality versus accident frequencies.
The objective of this study would not be complete without considering the combined impact of events. An event pertains to a tendency to interact with others in various order forming groups. For example, “health and drugs” can influence performance of other parameters: “inadequate flight procedure,” “inadequate wind compensation and prefight weather planning,” “inadequate control speed,” “communication failure,” and so forth. In this way we formed various groups of events based on influencing the nature of each event, which are called “combined influence events.” Based on the results, we have listed in Table
Chronology of top ten events’ occurrences.
Description of event IDs | # of Obs. |
---|---|
Aircraft inability to avoid stall | 133 |
Obstacles, fatigue ops, and ostentatious display | 115 |
Poor manual flight control | 69 |
Inadequate flight procedure | 57 |
Encountering microburst and air turbulence | 40 |
Inadequate wind compensation and weather planning | 40 |
Inadequate control, speed, and aircraft type | 30 |
Certification/lack of experience | 28 |
Low visible terrain and IMC | 28 |
Health and drugs | 23 |
The result parameters and logic change due to a shift in event behavior from individual to a combined influence. It is based on amplifying the frequency of events to multiple times. We used the sensitivity analysis tool to calculate the relative change in frequency, assuming a simultaneous increase in a probability of 1 percent across all relevant events mathematically.
Example of calculation for combined influence event sensitivity.
Event ID | Description of event IDs | Baseline acc. freq. | New acc. freq. | Sensitivity |
---|---|---|---|---|
AIAS | Aircraft inability to avoid stall |
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⋯ | ⋯ | ⋯ | ⋯ | |
IFP | Inadequate flight procedure |
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EMAT | Encounter microburst and air turbulence |
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|
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⋯ | ⋯ | ⋯ | ⋯ | |
H&D | Health and drugs |
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|
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⋯ | ⋯ | ⋯ | ⋯ | |
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||||
Total |
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2.704% |
The events are combined in a various order based on their compatibility and interaction. An event having the same feature may be represented under different scenarios. However, a relative change in the baseline value of event influences the group of parameters without affecting the baseline value of the same label events under different groups of parameters. We incorporated a relative change of 1 percent in an event to model an impact under considered parameters by assuming that an improvement in one event would produce the same impact on other events within the same group of parameters. However, we realized that an event cannot be totally either dependent or independent but the truth lies somewhere in between and requires further research.
The “health and drugs” event is ranked as a top sensitive event as shown in Table
Combined influenced top ten events sensitivity results for accident data.
Event ID | Description | # of obs. | Sensitivity |
---|---|---|---|
H&D | Health and drugs | 23 | 0.875 |
PMFC | Poor manual flight control | 69 | 0.625 |
IFP | Inadequate flight procedure | 57 | 0.5 |
IWC-WP | Inadequate wind compensation and weather planning | 40 | 0.375 |
CIA-LOE | Certification/lack of experience | 28 | 0.375 |
ICSA | Inadequate control, speed, and aircraft type | 30 | 0.375 |
OFOD-LAF | Obstacles, fatigue ops, and ostentatious display | 115 | 0.375 |
ISFG | Incorrect situational flight guidance | 15 | 0.25 |
EMAT | Encounter microburst and air turbulence | 40 | 0.25 |
COMMF | Communication failure | 4 | 0.25 |
However, some events have gained significance irrespective of low accidental frequency, for example, “communication failure b/w cockpit and ATC.” It is shown that the event is highly active in combining various groups. Therefore, we can state that event relative frequency determines whether an event is significant or not.
In Figure
CCF top ten events’ sensitivity results for accident data.
Event ID | Description | # of obs. | Sensitivity |
---|---|---|---|
ICSA | Inadequate control, speed, and aircraft type | 30 | 0.990 |
CIA-LOE | Certification/lack of experience | 28 | 0.990 |
EMAT | Encountering microburst and air turbulence | 40 | 0.981 |
IIC | Improper installation of component | 4 | 0.962 |
COMMF | Communication failure | 4 | 0.962 |
MFC | Maintenance failure component | 4 | 0.962 |
WSO-HAF | Without supplemental oxygen at high altitude flight | 4 | 0.962 |
IWC-WP | Inadequate wind compensation and weather planning | 40 | 0.425 |
OFOD-LAF | Obstacles, fatigue ops, and ostentatious display | 115 | 0.277 |
H&D | Health and drugs | 23 | 0.210 |
The result indicates top significant event as “health and drugs” for pilot pitfalls resulting in CRM events (i.e., “poor manual flight control” and “inadequate flight procedures,” as shown in Figure
Sensitivity versus frequency for accident data.
Sensitivity versus frequency for CCF-accidents data.
In continuation of the above logic and baselines values, the results indicate the top significant event as “inadequate control, speed, and aircraft type” as shown in Figure
Sensitivity versus frequency for fatality data.
Based on the above-mentioned discussion, we can state that certification/lack of experience and health and drugs are most significant pitfalls that influence pilot’s behavior in neglecting or violating standard operating procedures (SOPs) related to aircraft control and speed and operational procedures. Additionally, the events of improper installation and maintenance problem can play their role in triggering aforesaid events, irrespective of their rare occurrence. Overall, we concluded that there is a need to adopt mitigation measures in handling events of certification/lack of experience and health and drugs.
In most cases, events with high frequency are given importance for adopting mitigation measures, which is not always the right approach. The risk reduction assignment would help in investigating weaknesses in current practices. The loopholes in a system could be comprised of any one factor from the following: They failed to address the hazard. They did not exist because of new system improvements.
We applied risk reduction measures based on current practices. They will help in differentiating events with risks which are either manageable or unmanageable. Manageable events are those events that are less significant after risk reduction is applied and vice versa. The current practices are comprised of flight standards, hazards awareness and training, company culture, and aircraft equipment as shown in Table
Risk reduction/mitigation assessment factors.
|
|
Corporate/company management | |
Places safety before schedule | 20 |
CEO signs off on flight operations manual | 20 |
Maintains a centralized safety function | 20 |
Fosters reporting of all CFIT incidents without threat of discipline | 20 |
Fosters communication of hazards to others | 15 |
Requires standards for IFR currency and CRM training | 15 |
Places no negative connotation on a diversion or missed approach | 20 |
|
|
Specific procedures are written for | |
Reviewing approach or departure procedures charts | 10 |
Reviewing significant terrain along intended approach or departure course | 20 |
Maximizing the use of ATC radar monitoring | 10 |
Ensuring pilot(s) understand that ATC is using radar or radar coverage exists | 10 |
Altitude changes | 10 |
Ensuring checklist is complete before initiation of approach | 10 |
Abbreviated checklist for missed approach | 10 |
Briefing and observing MSA circles on approach charts as part of plate review | 10 |
Checking crossing altitudes at IAF positions | 10 |
Checking crossing altitudes at FAF and glideslope centering | 10 |
Independent verification by PNF of minimum altitude during stepdown DME (VOR/DME or LOC/DME) approach | 20 |
Requiring approach/departure procedure charts with terrain in color, shaded contour formats | 20 |
Radio-altitude setting and light-aural (below MDA) for backup on approach | 10 |
Independent charts for both pilots, with adequate lighting and holders | 10 |
Use of 500-foot altitude call and other enhanced procedures for NPA | 10 |
Ensuring a sterile (free from distraction) cockpit, especially during IMC/night approach or departure | 10 |
Crew rest, duty times, and other considerations especially for multiple-time-zone operations | 20 |
Periodic third-party or independent audit of procedures | 10 |
Route and familiarization checks for new pilots | |
Domestic | 10 |
International | 20 |
Airport familiarization aids, such as audiovisual aids | 10 |
First officer to fly night or IMC approaches and the captain to monitor the approach | 20 |
Jump-seat pilot (or engineer or mechanic) to help monitor terrain clearance and the approach in IMC or night conditions | 20 |
Insisting that you fly the way that you train | 25 |
|
|
Your company reviews training with the training department or training contractor | 10 |
Your company’s pilots are reviewed annually about the following |
20 |
Reasons for and examples of how the procedures can detect a CFIT “trap” | 30 |
Recent and past CFIT incidents/accidents | 50 |
Audiovisual aids to illustrate CFIT traps | 50 |
Minimum altitude definitions for MORA, MOCA, MSA, MEA, and so forth | 15 |
You have a trained flight safety officer who rides the jump seat occasionally | 25 |
You have flight safety periodicals that describe and analyze CFIT incidents | 10 |
You have an incident/exceedance review and reporting program | 20 |
Your organization investigates every instance in which minimum terrain clearance has been compromised | 20 |
You annually practice recoveries with GPWS in the simulator | 40 |
You train the way that you fly | 25 |
|
|
Aircraft includes | |
Radio altimeter with cockpit display of full 2,500-foot range—captain only | 20 |
Radio altimeter with cockpit display of full 2,500-foot range—copilot | 10 |
First-generation GPWS | 20 |
Second-generation GPWS or better | 30 |
GPWS with all approved modifications, data tables, and service bulletins to reduce false warnings | 10 |
Navigation display and FMS | 10 |
Limited number of automated altitude callouts | 10 |
Radio-altitude automated callouts for nonprecision approach (not heard on ILS approach) and procedure | 10 |
Preselected radio altitudes to provide automated callouts that would not be heard during normal nonprecision approach | 10 |
Barometric altitudes and radio altitudes and radio altitudes to give automated “decision” or “minimums” callout | 10 |
An automated excessive “bank angle” callout | 10 |
Autoflight/vertical speed model | 10 |
Autoflight/vertical speed mode with no GPWS | 20 |
GPS or other long-range navigation equipment to supplement NDB-only approach | 15 |
Terrain-navigation display | 20 |
Ground-mapping radar | 10 |
The top significant events are obstacles, fatigue operation, and ostentatious display at low altitude as shown in Figure
Sensitivity versus frequency for risk reduced data.
The fault tree has a limitation where it uses binary characters (occur or not occur) for quantification. It is unable to differentiate an event that almost happened but did not happen from those events that almost never happened. The event nodes can be made complex by enhancing binary to mixed double algorithms, which could make the system structurally complex. To avoid this complexity in a fault tree, it can be suggested to accommodate it in ESDs for simplicity. We have concluded that the individual handling of events may not reflect efficiency, productivity, and consistency in a model. It requires a holistic risk-informed approach to achieve accuracy, as mentioned earlier.
As per our understanding, this study is the first to evaluate loss of control parameters pertaining to the maneuvering phase in GA, where an integrated framework was adopted to cover various internal and external events having different scenarios. This logic reduces the likelihood of an event being ignored. We have presented matrices reflecting significant parameters. These parameters were further studied by sensitivity analysis by considering accident frequency, fatality frequency, and risk reduction frequency. Among different importance measures, factorial design was given preference because it was based on the high and low value of an event, rather than relying on baseline values. The overall system impact approach was used to calculate the combined influence event approach in order to counter the limitation of the fault tree structure. Based on the results, we concluded that events sensitivity is not an absolute term that varies with event interpretations in different groups and scenarios. However, we did not cover the relationship of event counts with their importance, which is beyond the scope of this study.
We identified that the most significant events that contribute to maneuvering accidents are obstacles, fatigue operations, insufficient situational guidance, and low visible terrain. These events have a catalyst effect in triggering other events, especially at low altitude fight operations. Without considering these parameters, it is difficult to manage loss of control in the maneuvering phase. The risk reduction approach indicated that the current procedural practices are good enough to counter certain risks associated with flight crew performance and equipment operations. It reflects a need to enhance crew training to counter incapacitation and distractions. However, it is difficult to justify risk reduction measures related to changes in environment and weather due to uncertainty, while event pertaining to flight crew negligence (e.g., health and drugs and license/certification) requires policy-based initiatives to enforce risk reduction measures by either a carrot or a stick, for example, policy initiative to stop the availability of over-the-counter drugs and establish a mechanism for routine tests for all types of drugs. In addition to that, studies on equipment performance have suggested autonomous equipment measures to handle loss of control [
Currently, many ideas are floating around regarding unmanned air vehicles (UAVs)/drones and flying vehicles that operate at low altitude. These ideas are considered as the future mode of transportation, where many GA operations will be replaced with drone technology for various applications, for example, border control, surveillance, logistics, agriculture, fire management, and relief work. For these reasons, we can foresee the aforesaid highlighted parameters could be responsible for loss of control during maneuvering of drones. However, many additional parameters will be required in studying the drone perspective, for example, malfunction of batteries, irregular traffic patterns, incorrect path planning, safety and security measures against virus or hacking, flashing effects on sensors, sensor malfunction, and electromagnetic surge effects from weather and environment.
This study provides foundation for future research, where we utilized these results in developing system strategies and subsequently changes in current policies in order to deal with events like health and drugs and certification or lack of experience factors. Currently, the development of such interventions is beyond the scope of this study. The advantage of addressing interventions would be beneficial in reminding pilot or crew about forgotten things that will ultimately reduce skill based errors.
Improper balancing weight
Improper installation of component
Maintenance failure component
Inadequate construction of component by factory
Incorrect situational flight guidance
Inadequate flight procedure
Poor manual flight control
Encountering microburst and air turbulence
Inadequate control, speed, and aircraft type
Inadequate wind compensation and weather planning
Noninstrument rated pilot enter IMC
Low visible terrain and IMC
Without supplemental oxygen at high altitude flight
Obstacles, fatigue ops, and ostentatious display
Health and drugs
Certification/lack of experience
Aircraft inability to avoid stall
Communication failure.
Improper balancing weight
Incapacitation crew
Ground crew unstable approach
Incorrect actions
Improper installation of component
Maintenance failure of component
Inadequate construction of component by factory
Incorrect situational flight guidance
Inadequate flight procedure
Poor manual flight control
Encountering microburst and air turbulence
Failing to manage wind-shear
Inadequate control, speed, and aircraft type
Inadequate wind compensation and weather planning
Noninstrument rated pilot enter IMC
Low visible terrain and IMC
Without supplemental oxygen at high altitude flight
Obstacles, fatigue ops, and ostentatious display
Health and drugs
Certification/lack of experience
Distracted attention
Lack of visual orientation
Flight crew unstable approach
Aircraft inability to avoid stall
Adverse weather situation
External aircraft circumstances
Internal cabin circumstances
Spatially disoriented
Crew resource management
Loss of situational awareness
Communication failure
Defective component.
Dr. Sameer Ud-Din has a Ph.D. degree from Civil and Environmental Engineering Department, Korea Advanced Institute of Science and Technology, Republic of Korea.
The authors declare that they have no conflicts of interest.
This work was supported by the Brain Korea 21 Plus project (Center for Creative SOC Infrastructure System Technology, 21A2013200003) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning. Korea Ministry of Land, Infrastructure and Transport (MOLIT) as “U-City Master and Doctor Course Grant Program” financially supports this work. The authors would like to thank Dr. Hyun Gook Kang, Associate Professor of Nuclear Engineering Program at Rensselaer Polytechnic Institute (RPI), NY, USA, for providing logistic support.