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: It is important to accurately measure the emissions of a turbomachine for a variety of reasons. However, continuous emission monitoring systems (CEMS) can be expensive to install and maintain. Accordingly, a digital platform is disclosed that hosts physics-based and/or statistical models that can be tailored to specific turbomachines and calibrated over the life of the turbomachine. The model for a turbomachine can be applied to data collected from the turbomachine to predict the emissions of the turbomachine. This enables monitoring of emissions, remotely and without the need of a CEMS. In addition, the platform may utilize the predicted emissions for alerts, compliance monitoring, health monitoring, control of the turbomachine, reporting, and/or the like.