In manufacturing, users increasingly demand comprehensive maintenance service in their production equipment in order to ensure high availability and to prevent downtimes in critical phases of the production processes, affecting customer delivery times. From the manufacturer’s point of view, it is vital to optimize and to improve the service provided to the final users, allowing appropriate maintenance planning and responding to the demand. Contrary to the classic preventive maintenance programs in use today, predictive maintenance improves the performance of the equipment, strengthening the business model of companies. Thanks to the inclusion of a set of sensing, condition monitoring, predictive analytics and distribute systems technologies, it is possible to perform and provide a remote technical assistance based on continuous monitoring and maintenance support from a distance. This paper shows the benefits and advantages to be achieved by the development of a comprehensive predictive maintenance, through the concept of Industry 4.0, and focuses on remote monitoring and self-diagnosis function of health condition for the equipment. However, the main emphasis of the work presents the data acquisition and analysis processes to develop predictive algorithms for machines in production.
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