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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-12607971-B2, on 21 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000292

Machine learning-based systems for parameter deviation monitoring and alerting in engines and pump controls

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Sensors, Controls & Machine Vision › Industrial Control
Original source
US-12607971-B2, the United States Patent and Trademark Office, 21 April 2026
Document SHA-256
fde0df4d7f66f5617429eceee6121080c57e1b9d02f4b00c16054c30c9966ec9

Abstract

Exemplary embodiments are disclosed of controllers with machine learning and systems including the same. In exemplary embodiments, a controller is configured to include or be operable for executing a machine learning process during which the controller learns normal level(s) of parameter(s) (e.g., system component(s) and/or sensor(s) parameters, etc.) to be monitored across a full operational range of a system component, which, in turn, enables the controller to compare the monitored parameter(s) to the learned normal level(s) for detection of issue(s) or problem(s) associated with the monitored parameter(s), e.g., regardless of the RPM at which the engine, machine, motor, or other system component is operating, etc.

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Citation

OpenTechnical Publication OT-2026-000000292, “Machine learning-based systems for parameter deviation monitoring and alerting in engines and pump controls,” published September 15, 2026 at 19:02:42 UTC, https://opentechnical.org/p/OT-2026-000000292.

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