Digital Utility Group
The mission of this group is to bring together utility professionals in the power industry who are in the thick of the digital utility transformation.
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Windows-based HMIs are too slow for monitoring process sensors or plant equipment anomalies
Process sensors are the input for predictive maintenance, digital transformation, Industry4.0, smart manufacturing, smart grid, etc. The majority of OT networks use Windows-based HMIs even though Windows was not designed to be an engineering data acquisition tool. In a recent plant test, the Windows-based HMI was not effective and, in fact, provided misleading information on the state of the process sensors and plant equipment. Using raw unfiltered process sensor monitoring and machine learning, the plant now has the potential to demonstrate a significant return on investment (ROI) from improved plant operation as well as improving cyber security protection. The machine learning also provided more meaningful data to the operators. Monitoring tools for process sensors and plant equipment need to be purpose-built, not general-purpose systems such as Windows. As the test data is still being analyzed, more details will be included in future articles including in the November issue of IEEE Computer magazine.
Windows-based HMIs are too slow for monitoring process sensors or plant equipment anomalies
Process sensors are the input for predictive maintenance, digital transformation, Industry4.0, smart manufacturing, smart grid, etc. The majority of OT networks use Windows-based HMIs even though Windows was not designed to be an engineering data acquisition tool. In a recent plant test, the Windows-based HMI was not effective and, in fact, provided misleading information on the state of the process sensors and plant equipment. Using raw unfiltered process sensor monitoring and machine learning, the plant now has the potential to demonstrate a significant return on investment (ROI) from improved plant operation as well as improving cyber security protection. The machine learning also provided more meaningful data to the operators. Monitoring tools for process sensors and plant equipment need to be purpose-built, not general-purpose systems such as Windows. As the test data is still being analyzed, more details will be included in future articles including in the November issue of IEEE Computer magazine.
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