Hard/Soft Information Fusion in the Condition Monitoring of Aircraft
Open Access
- Author:
- Bernardo, Joseph T
- Graduate Program:
- Information Sciences and Technology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 13, 2014
- Committee Members:
- David J Hall, Dissertation Advisor/Co-Advisor
David J Hall, Committee Chair/Co-Chair
Michael David Mcneese, Committee Member
Guoray Cai, Committee Member
Richard Laurence Tutwiler, Special Member - Keywords:
- hard/soft information fusion
condition-based maintenance
condition monitoring
human factors
decision-making - Abstract:
- The synergistic integration of information from electronic sensors and human sources is called hard/soft information fusion. In the condition monitoring of aircraft, the addition of the multisensory capability of human cognition to traditional condition monitoring may create a more complete picture of aircraft condition. A large data set from Naval Air Systems Command (NAVAIR) on maintenance of multi-mission vertical takeoff and landing (VTOL) 22 series B (MV-22B) aircraft provided the opportunity to explore the value of hard/soft information fusion in aviation maintenance. First, cognitive and functional frameworks were applied to hard/soft information fusion in the condition monitoring of aircraft. The steps of the Orasanu decision process model were applied to the macrocognitive functions and processes of the aviation maintainer. Emerging literature on hard/soft information fusion in condition monitoring was organized into the levels of the Joint Directors of Laboratories (JDL) data fusion process model, and the levels were applied to the process functions of aviation maintenance. Second, a research design was created for a retrospective analysis of sensor readings, human observations, and choices made in the maintenance of MV-22B aircraft. The data set from Decision Knowledge Programming for Logistics Analysis and Technical Evaluation (DECKPLATE), a NAVAIR database, provided information collected without the interference of interviewer bias. Third, a research methodology was created for studying hard/soft information fusion in aviation maintenance. Content analysis of the descriptive and corrective action narratives showed faults and aircraft components chosen for repair, replacement, fabrication, or calibration. Problem complexity was found to be an important factor. Additionally, expertise level also had an effect, and it was described through longitudinal trending. Fourth, the addition of human observation to sensor data was highly associated with the aircraft components chosen for action. Additionally, for complex problems, the addition of human observation to sensor data was significantly associated with improved outcomes. Descriptive statistics showed reduced diagnostic effort with human observation in complex problems. The improved outcomes and reduced diagnostic effort with human observation in complex problems may reduce operational maintenance cost, increase mission readiness, and increase flight safety.