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by Kalonji Bankole | Published June 25, 2019
In this code pattern, we’ll investigate a method to integrate custom machine learning models with IBM Maximo. These models are trained to estimate the remaining useful life of each registered asset. If an asset is expected to fail in the near future, a work order can automatically be generated.
Instrumented, connected assets generate volumes of operational data, both structured and unstructured. This data can be used to identify equipment failure risks, if the organizations has the analytics tools to convey this insight to the personnel who are responsible for asset operations.
In this code pattern, we’ll show you how to build and apply custom machine learning models to identify risks and suggest proactive maintenance to avoid service disruption. These models will also estimate how long a mechanical asset can be used before needing maintenance or replacement.
We’ll also show you how to import the custom models into a Maximo instance. IBM Maximo is a system that is used for managing assets and workflow processes. Maximo can be used to increase efficiency by automating processes, such as work orders, notifications, anomaly detection, and so on.
Remaining Useful Life
Find the detailed steps for this pattern in the README. The steps will show you how to:
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