Stochastic processes to support predictive maintenance for industrial environments

This paper presents and discusses the usage of stochastic processes to support predictive maintenance for industrial environments. The aim of the work executed is to give a contribution for the development of supervision and monitoring systems, attempting to improve and automate the conformity quality control during production, mainly in companies where this task is computer assisted. The automated forecast of production failures and defects is a strategy that helps to achieve real time quality control and optimises the machine maintenance intervals, minimising unscheduled stops. The developed work covers sensorial information acquisition, forecast modeling and monitoring system parameters' identification, estimation and validation. This paper begins presenting how important is the usage of sensorial systems for numeric control machine monitoring and supervision. Then, a detailed description of the study and experimental analyses done is also presented. This text finishes by showing the achieved results, conclusions and perspectives for future work.