Failure prediction for an on-line maintenance system in a Poisson Shock environment
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An analog system subject to the Poisson Shock is modeled using past performance data. Failure Dynamics of the system is estimated by curve fitting techniques. Algorithms for fault prediction in an on-line maintenance process are described. Several sequential refinement schemes are introduced to improve fault prediction. Some formulas and properties of system's statistics have been developed. A decision rule is introduced which is based on the criteria of simultaneously maximizing lifetime and minimizing the cost of on-line failures. Poisson Shock generator is implemented by computer for simulation of the on-line maintenance process, The computer simulations of a perfect, no measurement errors and identical drifting parameters, system are presented, The simulations of an imperfect system are studied by adding a noise to the system performance data.