Almost all manufacturing organizations have a rich set of data available at their disposal – such as equipment type, the number of days in operation, days from the last service, days of next service, failure history costs for planned and unplanned maintenance and so on. Cognitive predictive maintenance leverages this data in real time and quickly detects failure patterns to identify the root cause of the problem. With this information, engineers can evaluate the current status of every asset and schedule inspection or maintenance just in time to prevent failures or breakdowns. This ensures better conservation of assets, improves equipment life and eliminates premature replacement of machinery thereby saving enormous maintenance costs.
The manufacturing industry faces a huge ‘dark – data’ problem where critical business value remains locked in the unused data generated by sensors in Industrial IoT. Scaling the data science teams for effective predictive maintenance is not an option due to continuous volumes of data. It is quite impossible to manually build models of predictive maintenance and sustain it over the years. It’s imperative that machines start talking to machines while connecting with the outside world, to cut through the dark spans of unanalyzed data with automation, which can reveal the series of triggers that lead to a potential failure.
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