In many industries, maintenance usually follows protocols provided by the equipment manufacturer, where recommendations for maintenance are based on hours of use or some other fixed time interval rather than on predictive analysis.
This rules-based approach can be costly for manufacturers in two ways – performing maintenance when it is not needed and missing developments between intervals which could lead to breakdowns or damage of equipment.
When using IoT sensors to monitor equipment in real time, maintenance can be provided more accurately. Additional possibilities offered by driving maintenance based on IoT and data analytics are, for example, that operators can identify fault patterns by comparing the performance of one piece of equipment against a global database of similar equipment.
With that information maintenance staff can decide to change the usage of the equipment that is driving faults. Also, by using meta data about equipment (e.g., models, production data, etc.) even more accurate analyses and predictions can be achieved. For example, it is more likely that an older model of an engine produces more heat and noise than a newer version.
Performing maintenance as accurately as possible, eliminates unnecessary downtime for scheduled maintenance and can prevent serious equipment trouble by detecting changes in performance that indicate a potential failure. Estimates indicate that a manufacturer can reduce maintenance costs by 5 to 10 percent and increase output by 3 to 5 percent by avoiding unplanned outages.
IoT regulators and suppliers of equipment could have visibility into equipment records and could provide timely inspections and certifications to ensure equipment reliability. Third-party repair partners could monitor the data for preventive maintenance and record their work on the system.
Estimates indicate that a manufacturer can reduce maintenance costs by 5 to 10 percent and increase output by 3 to 5 percent by avoiding unplanned outages.
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