Abstract
The work describes the computing cluster for early fault prognosis and estimates of RUL for technical objects: wind turbines, internal combustion engines, gas turbine, etc.
Basic functions of the cluster, consisting of the algorithms are described: Preventive smart monitoring to detect signs of failure; Definition of computing models of the time evolution of features and accurate RUL estimate; Analysis of telemetry data in order to detecting and further monitoring of the hidden signs which give early prognosis (preventive prognosis) of potential risks of failure in the absence of statistical characteristics; Learning of recognizing automata (HMM, neural networks) for real-time autonomous systems of smart preventive monitoring.
The computational algorithms are based on models that combine the methods of the theory of dynamical systems, the theory of stochastic processes, statistical physics and field theory.
The organization of algorithms in the hierarchical structure based on the principles of degeneration of failure signs helped identify more early signs of failure.
This, in turn, allows to calculate effective Predictive maintenance strategies.
The first experimental results of the cluster operation are shown. In particular, it is show that the hierarchical approach to the RUL prognosis has the structure of one-dimensional graph.
The experiment confirmed the need to integrate remote computing cluster with peripheral recognizing automata in a united network for effective organization of automata operation and their retraining.
