Aleksandr Kirillov, 1, Sergey Kirillov, 1, Vitaliy Iakimkin, 1, and Michael Pecht 2
1 SmartSys Prognosis Center, Moscow, Russia
2 Center for Advanced Life Cycle Engineering, University of Maryland at College Park, College Park, MD 20742, USA
The chapter describes a mathematical model of the early prognosis of the state of high-complexity mechanisms. Based on the model, systems of recognizing automata are constructed, which are a set of interacting modified Turing machines. The purposes of the recognizing automata system are to calculate the predictors of the sensor signals (such as vibration sensors) and predict the evolution of hidden predictors of dysfunction in the work of the mechanism, leading in the future to the development of faults of mechanism. Hidden predictors are determined from the analysis of the internal states of the recognizing automata obtained from wavelet decompositions of time series of sensor signals. The results obtained are the basis for optimizing the maintenance strategies. Such strategies are chosen from the classes of solutions to management problems. Models and algorithms for self-maintenance and self-recovery systems are discussed.