Abstract: The unexpected failure of a turbomachine can be costly and dangerous. Processes may collect data from a turbomachine, calculate durations of machine events from the collected data, and apply a model to those machine-event durations to predict future machine-event durations and/or detect trends in the machine-event durations. This predictive output may be utilized to inform downstream functions regarding the health of the turbomachine. For example, a downstream function may utilize the predictive output to detect degradation or a potential future failure in the turbomachine and trigger remedial functions, such as alerts and/or controls, to prevent or mitigate the degradation or failure of the turbomachine.

Abstract: Industrial machines positioned along a gas transmission pipeline are controlled using setpoints. Operators of such pipelines would benefit from real-time data and recommendations to guide them in optimizing performance of the pipelines. Accordingly, a compression optimization system is disclosed that monitors and provides real-time data and notifications, recommends optimal control setpoints using a machine-learning or other artificial-intelligence model, which may self-learn to realistically represent the pipeline, and executes simulations for hypothetical scenarios. The compression optimization system enables efficient management of the gas transmission pipeline.