Recurrent error pathways in HFACS data: analysis of 95 mishaps with remotely piloted aircraft.
BACKGROUND
According to Reason's model of accident causation, mishaps tend to fall into recurrent patterns. This model is the foundation for the Human Factors Analysis and Classification System (HFACS), which is now widely used in aviation accident analysis. The purpose of this study was to determine if it was possible to use HFACS to identify recurrent error pathways within an existing mishap database.


METHODS
All MQ-1 Predator remotely piloted aircraft (RPA) mishaps and safety incidents reported to the Air Force Safety Center during fiscal years 1997-2005 were evaluated and entered into a mishap database (N = 95). An exploratory principal component analysis (PCA) was used to assess the factorial structure within the set of crewmember-related mishaps (N = 48). The results of the PCA were used to define recurrent pathways from latent to active failure and hence mishaps. A tree diagram was subsequently created to quantitatively assess the associations identified in the PCA.


RESULTS
Four factors were identified, each consisting of one of the HFACS categories of active failure and one or more categories of latent failure. Two additional factors were identified, consisting of fatigue and motivational latent failures. Based on analysis of the tree diagram, the majority of mishaps were caused by latent failures involving organizational factors and the technological environment.


CONCLUSIONS
It was possible to identify four recurrent error pathways associated with the four types of HFACS active failures within this sample of RPA mishaps. Two of these error pathways, accounting for 57% of crewmember-related mishaps, were consistent with situation awareness errors associated with perception of the environment.
Tvaryanas
Anthony P
AP
311th Performance Enhancement Directorate, Brooks City-Base, TX, USA. aptvarya@nps.edu
Thompson
William T
WT
D000060:Accidents, Aviation / Q000145:classification* / Q000706:statistics & numerical data*; D001364:Awareness; D003663:Decision Trees; D005163:Factor Analysis, Statistical; D006801:Humans; D015233:Models, Statistical; D013597:Systems Analysis; D013647:Task Performance and Analysis; D014481:United States