Process automation server anomaly detection using machine learning

A system comprising:

• one or more processors;
• and
• memory storing instructions that, when executed, cause the one or more processors to:
• obtain a current set of operating data, the current set of operating data including:
• multiple disparate types of data, each of the disparate types of data reflecting a corresponding operating state of a corresponding component of a process automation facility, and
• for each of the multiple disparate types of data, a corresponding most recently determined instance thereof;
• process, using a trained machine learning model, the current set of operating data to generate output that indicates a current operating condition of a process automation server,
• wherein the process automation server operates on server hardware that is located within the process automation facility, and
• wherein the process automation server controls one or more aspects of a process performed within the process automation facility;
• determine, based on the output generated using the machine learning model, whether the current operating condition of the process automation server is normal;
• and
• in response to determining, based on the output generated using the machine learning model, that the current operating condition of the process automation server is not normal:
• cause automatic performance of at least one remediating action that seeks to remedy the not normal current operating condition of the process automation server.