Preventive maintenance through predictive analytics

With unstructured maintenance data hampering strategic decision-making, the Navy’s Military Sealift Command turned to machine learning for help.

When the Navy’s Military Sealift Command realized that its decades-long horde of unstructured maintenance data was hampering strategic decision-making, officials turned to machine learning for help.

The command teamed up with Abeyon, a firm that specializes in artificial intelligence solutions, to create the data analysis tool Clarifi — a preventive capability that monitors the condition and reliability of all 100 of the command’s ships.

Using sample documents representing the larger unstructured dataset, the team built a machine learning-based text analysis model to explore and identify relationships among the equipment data and entities.

The tool’s pilot version turned nearly 30 years’ worth of data locked in Word documents into educated decisions regarding ships and their maintenance. Overall, the tool has increased operational efficiency and helped lower costs by millions of dollars.

As the technology matures, Clarifi could offer recommendations on equipment health, condition and potential for failure. Instead of guessing whether a machine has reached its end, the command can now look to the past to predict problems and preempt them.

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