Most manufacturers have been using predictive analytics for years in the form of spreadsheets and manual data entry. Although useful, these methods were subject to operator assumptions and human error.
As the number of sensors on the factory floor increases, data gathering becomes automated and data sets are easier to analyse. New predictive analytics techniques also significantly improve data accuracy. Engineers can use these data sets for predictive maintenance purposes to determine the condition of in-service equipment and identify when it will need to be repaired or replaced.
Predictive analytics is able to compare real-time machine data gathered from sensors to a history of machine failure. It uses complex algorithms to spot behavioural patterns before a breakdown.
By combining sensor technology and big data analytics, manufacturers can minimise equipment failure. Knowing that a motor or drive is likely to break soon means the manufacturer can repair it or order a replacement before a breakdown occurs. It also allows manufacturers to schedule maintenance work for a convenient time, instead of shutting down production as and when breakdowns happen.
Predictive maintenance not only minimises downtime, it also gives maintenance engineers and plant managers peace of mind. It frees up the time of maintenance staff, so that they can deal with tasks that are more valuable to the business. In the near future, it is conceivable that a smart production line could order spare parts automatically when necessary, with minimal human intervention.