An intelligent maintenance based on machine learning approach for wireless and mobile systems

To enhance wireless and mobile system dependability, audit operations are necessary, to periodically check the database consistency and recover in case of data corruption. Consequently, how to tune the database audit parameters and which operation order and frequency to apply becomes important aspects, to optimize performance and satisfy a certain degree of Quality of Service, over system life-cycle. The aim of this work is then to suggest an intelligent maintenance system based on reinforcement Q-Learning approach, built of a given audit operation set and an audit manager, in order to maximize the performance (performability and unreliability). For this purpose, a methodology, based on deterministic and stochastic Petri nets, to model and analyze the dependability attributes of different scheduled audit strategies is first developed. Afterwards, an intelligent (reinforcement Q-Learning) software agent approach is developed for planning and learning to derive optimal maintenance policies adaptively dealing with the highly dynamic evolution of the environmental conditions. This intelligent approach, is then implemented with feedforward artificial neural networks under the supervised gradient back-propagation learning to guarantee the success even with large state spaces, exploits intelligent behaviors traits (learning, adaptation, generalization, and robustness) to derive optimal actions in different system states in order to achieve an intelligent maintenance system.