Sensemore
Problem
Industrial companies struggle with predicting unexpected downtime in production facilities because current machinery monitoring techniques are not successful to predict failures specifically and to create effective maintenance strategies. In addition, industrial operations account for 30% of world carbon emissions due to machinery energy consumption, production-related waste, and repair-replacement of failed machinery.
Solution
Sensemore analyzes machine health in order to predict machine malfunctions before unexpected stops occur. With its end-to-end solution, from data collection to AI-automatized single-page failure reports, Sensemore enables manufacturers to be aware of the condition of the machines so that the production facilities do not stop operating due to machine malfunctions, reduce maintenance costs, increase production efficiency, and decrease the carbon emission of its users.
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