Intelligent automation systems for predictive maintenance: A case study

A case study is presented, where a predictive maintenance solution for non-critical machinery (such as elevators and machine tools) was sought. Both cases are different. There is no experience in elevator monitoring and diagnosis, and modeling has been performed using Neural Networks. On the other hand, machine tools were monitored through vibration systems where some experience exists. In this case, Bayesian Networks are the paradigm of choice as it was also recommended to include some ‘adaptation’ mechanism for the knowledge modeled in the network. The final system also includes a sensor processing unit and a remote maintenance module system that provides an automated remote condition monitoring system, 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.