Predictive maintenance of equipment for reducing repair cost & increased uptime

The Problem

One of the major challenges faced by the manufacturing industry is not being able to accurately predict when an equipment requires maintenance. Predicting such requirements can help saves companies massively on repair cost and ensure there is no or minimal downtime on their equipment. The cost of machine downtime is high: according to the International Society of Automation, $647 billion is lost globally each year. Over the years businesses have overhauled maintenance processes to alleviate downtime and improve effectiveness. There still seems to be confusion, however, around the best way to use data in the quest for optimum operational efficiency.

Solution

Kranium’s machine learning models can be used to process massive amounts of sensor data faster than ever before. This gives companies an unprecedented chance to improve upon existing maintenance operations and add something novel – predictive maintenance. Using Kranium’s predictive models companies can predict when machinery requires maintenance services.

Kranium’s AI models can be trained to explore insights from the device performance data which is collected from connected IoT sensors. These insights ccan help manufacturers to know the performance of individual devices. Such insights will assist companies in predicting possible failure of the devices in the future.