Abstract
In the industrial sector, maintenance plays a very important role in carrying out production by increasing system reliability and availability. Thee maintenance decision is based primarily on diagnostic modules, prognostics and decision support. Diagnostic consists of detection and isolation of faults, while prognostic consists of prediction of the remaining useful life of systems. Moreover, recent industrial systems are naturally hybrid: their dynamic behavior is both continuous and discrete. This paper presents an integrating architecture of diagnostic and prognostic in a hybrid dynamic system. Indeed, the diagnostic system is based on controlling task execution times during system operation. This method is based on a general modeling approach using hybrid automata. The model proposed is detailed by studying a two-tank system. To validate the model, a Stateflow controller is used. These failures are anticipated by a prognostics process based on a prediction of the remaining life for each component by taking maintenance policy into account. Two new methods are compared: ABAO (As Bad As Old) and AGAN (As Good As New), based on the type of repair strategy.
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