AI-enabled predictive maintenance
Assessing the integrity and operation of your asset is easier with BMT DEEP, providing a more cost-effective and safer assessment.
Assessing the integrity and operation of your asset is easier with BMT DEEP, providing a more cost-effective and safer assessment.
BMT DEEP monitors and hosts big data from a variety of Internet of Things (IoT) devices, sensors and sources, providing a holistic overview of an asset’s health and valuable insight for maintenance and operation teams.
It is the connection between the asset and the enterprise which is able to aggregate, federate and normalise large volumes of disparate, historical and real-time operational data into a common, secure, cloud-based solution. By using BMT DEEP you can employ a whole new class of algorithms to solve previously unsolved problems. Consider the case of assessing the risk of engine failure. By characterising all relevant inputs (e.g. run hours, maintenance records, engine temperature, oil pressure, etc.) and a sufficiently large number of engine failure cases, it is possible not only to predict whether an engine is likely to fail but also to diagnose the causes of failures.
AI-based predictive maintenance can be used to consume data from a limitless number of sensors across a distribution network to predict equipment failure and prescribe proactive maintenance before equipment fails. BMT DEEP provides an early assessment of the factors that may have impacted the integrity and operation of your asset from a remote location, providing a safe, controlled environment for the operations teams to assess any damage early in the process. Machine or equipment failures across a fleet or network of assets, can be identified from a remote location, rather than on-site, providing a more cost-effective and safer assessment.
BMT DEEP allows a collaborative working environment, enabling data to be distributed across internal and external stakeholder networks. Data from the asset can be accessed directly by regulatory authorities, maintenance engineers and other stakeholders in near real-time, allowing for a considerable reduction in maintenance man-hours and an increase in overall system or production availability.
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