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.

Abstract: An owner or operator of a turbomachine, such as a gas turbine engine, may wish to extend a service interval for the turbomachine. A framework is disclosed that enables automation of various aspects of the review process, including the generation of a risk assessment for the service interval extension. Data, including equipment data, may be aggregated from a plurality of different data silos. A physics-based model may be applied to the equipment data to calculate a remaining useful life of the equipment, which may then be used to generate the risk assessment. In addition, the framework may facilitate multiple levels of evaluation of the risk assessment to increase confidence in a final approval or denial of the service interval extension.