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To solve the problems of prognosis and further cost optimal planning of strategy EAM the various approaches of analysis of objects are used. The two main directions for the analysis of the system of states are allocated. First is related to cloud processing of telemetry of engineering object. Usually, it is implied that to identify predictors of failure and determining their trends needs large computing resources. The second direction is characterized by necessity of urgent decision-making, so the system of diagnosis and prognosis should work in real time and should be adapted on-board. However, questions of reliability of prognosis, diagnosis arise at the use of recognizing automata in the form of neural networks, hidden Markov chains, and Bayesian networks. The ideal solution of the problem is reduced to the integration of peripheral automata and remote cloud computing in a single system. In this case, the remote PHM cluster besides the functions of computing long-term prognosis and the development of optimal cost-effectiveness maintenance strategies executes. Also functions of learning and retraining of recognizing on-board automata at change of the operating conditions of engineering or change of the physical state of engineering, do not increasing the risk of failures. Using a chronological database of individual objects, as well as a common database of similar objects, remote computer system analyzes the nature of the changed conditions or conditions of engineering and on the basis of CH&P model selects a system of evolution equations for prognosis of the development of predictors and hidden predictors. Then the method of retraining on-board recognizing automata is based on the evolution equations or the new scheme of real time recognizing that does not require training is constructed. The paper gives the experimental results demonstrating the operation of the automata and the remote cluster for commercial vehicles.
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- Distributed Pre-processing of Telemetry for Mobile Engineering Objects
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