Intelligent Automation Systems for Predictive Maintenance. A Case Study

摘要

A case study is presented, corresponding EC partially supported MINICON project (Minimum cost, minimum size, maximum benefit condition monitoring system), where a predictive maintenance solution for non-critical machinery (such as elevators and machine tools) was sought. Both cases are different. There were no experience in elevator monitoring and diagnosis, and modelling was performed using Neural Networks. On the other hand, machine tools where monitored through vibration systems where some experience exists. In this case, Bayesian Networks were the paradigm of choice as it was also recommended to include some 'adaptation' mechanism for the knowledge modelled in the network. The final system also include a sensor processing unit and a remote maintenance module system that provides an automated remote condition monitoring systems, for both applications. Results indicate the feasibility of partial solutions in monitoring and diagnosis, though future enhancements are needed to compose a complete solution. This paper explains the characteristics of the Bayesian Network solution finally developed for high-speed machine-tools, evaluate their strengths and weaknesses, and indicate the future enhancements.