Incorporating uncertainty into diagnostic analysis of mechanical systems Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/g158bm54x

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  • Analyzing systems during the conceptual stages of design for characteristics essential to the ease of fault diagnosis is important in today's mechanical systems because consumers and manufacturers are becoming increasingly concerned with cost incurred over the life cycle of the system. The increase in complexity of modem mechanical systems can often lead to systems that are difficult to diagnose, and therefore require a great deal of time and money to return the system to working condition. Mechanical systems optimized in the area of diagnosability can lead to a reduction of life cycle costs for both consumers and manufacturers and increase the useable life of the system. A methodology for completing diagnostic analysis of mechanical systems is presented. First, a diagnostic model, based on components and system indications, is constructed. Bayes formula is used in conjunction with information extracted from the Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), component reliability, and prior system knowledge to construct the diagnostic model. The diagnostic model, when presented in matrix form, is denoted as the Component-Indication Joint Probability Matrix. The Component-Indication Joint Probability Matrix presents the joint probabilities of all possible mutually exclusive diagnostic events in the system. Next, methods are developed to mathematically manipulate the Component-Indication Joint Probability Matrix into two matrices, (1) the Replacement Matrix and (2) the Replacement Probability Matrix. These matrices are used to compute a set of diagnosability metrics. The metrics are useful for comparing alternative designs and addressing diagnostic problems to the system, component and indication level, during the conceptual stages of design. Additionally, the metrics can be used to predict cost associated with fault isolation over the life cycle of the system. The methodology is applied to a hypothetical example problem for illustration, and applied to a physical system, an icemaker, for validation.
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