PREDICTIVE MAINTENANCE FOR INDUSTRY

The increasing competition with the globalization of the production ecosystem increases the demand for intelligent factories in the industry day by day. This situation is forcing bringing with it a major challenge to the manufacturing sector to produce the next step. Therefore, manufacturers should increase their efficiency and productivity to become more competitive. To this end, they need to adopt advanced analytical approaches. Internet of Things (IoT) is used to collect and store data systematically and to make sense of this data by analyzing it. The collected large data can be interpreted by means of analytical methods to bring new perspectives to the old problems and to allow for new areas of research. Few researches and compilations have been made to inform companies about how they can profitably integrate with the IoT through strategic or operational processes. In this study, predictive maintenance has been investigated to dynamically manage the potentials and trends of maintenance management by means of large data generated in the industrial environment. Predictive maintenance; monitoring of the condition of the equipment and its components before failure and analyzing the data by analytical methods, evaluating the life expectancy and evaluating the possibility of failure and evaluating the data. Thus, it plans and optimizes maintenance by using preventive maintenance policies, real-time data usage and predictive machine learning algorithms in environments where equipment will be exposed to unplanned downtime