An analysis and modeling method of the diagnostic characteristics of a mechanical or electromechanical system is presented. Diagnosability analysis is especially relevant given the complexities and functional interdependencies of modern-day systems, since improvements in diagnosability can lead to a reduction of a system's life-cycle costs. The diagnosis process of a mechanical system, involving an observation phase and a testing phase, is described, as well as how failure types (the way particular system failure modes occur) impact the diagnostic process. Failure and diagnostic analysis leads to system diagnosability modeling with the Failure Modes and Effects Analysis (FMEA) and component-indication relationship analysis. Finally, methods are developed for translating the diagnosability model into mathematical methods for computing metrics such as distinguishabilty, testability, and Mean Time Between Unscheduled Removals (MTBUR). These methods involve the use of matrices to represent the failure and replacement characteristics of the system. Diagnosability metrics are extracted by matrix multiplication. These metrics are useful when comparing the diagnosability of proposed designs or predicting the life-cycle costs of fault isolation. / Graduation date: 2000

Design phase decisions based on diagnosability lead to lower system costs and, in turn, higher quality products by means of reducing maintenance time and increasing system reliability. A case for diagnosability is presented. Functions of diagnosability are expounded upon including life cycle costs, statistical analysis, and design criterion to emphasize the necessity of diagnosability analysis early in the design phase. A diagnosability prediction metric is developed for system modeling of component failure rates and unjustified removals. The metric emphasizes ambiguity of system component indications as well as system structure. The metric is evaluated using historical data from the bleed air control system (BACS) on the Boeing 737-300. Seven design changes are suggested based on improving system diagnosability by changing component functions, modifying indications, and adding or changing sensors. The resulting designs are compared via Boeing's life cycle cost mechanism, DEPCOST model, based on cost improvements. It is shown that system improvements based on this prediction technique will increase the quality of a product since increased diagnosability decreases life cycle costs. / Graduation date: 1996

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Donald R. Eaton, Kenneth, H. Doerr. Includes bibliographical references (p. 67-69). Also available online.

Thesis (M.S. in Operations Research and M.S. in Systems Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): David Olwell. Includes bibliographical references (p. 217-224). Also available online.

Thesis--Marburg. / Vita. Includes bibliographical references (p. 127-144).

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