Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.

Abstract: A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

Abstract: A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

Abstract: Optimization systems and methods for optimizing business operations and asset systems are disclosed. A system includes digital twins corresponding to asset systems; business models corresponding to business operations; and an electronic control unit (ECU). The ECU is programmed to: implement an asset optimizer module, where implementing the asset optimizer module interconnects the digital twins for optimization; execute the asset optimizer module, where the asset optimizer module optimizes the digital twins to obtain one or more optimization parameters for the asset systems; implement a system optimizer module, where the system optimizer module receives the one or more optimization parameters and the business models; execute the system optimizer module, where the system optimizer module generates operation protocols for the business models; and output, to a user, the operation protocols for implementation in a real-world asset system.

Abstract: An analytics core and/or an analytics core associated with aggregation are presented. For example, a system includes a monitoring component, a catalog component, a model suite component, and a model processing/learning component. The monitoring component monitor and analyzed data associated with one or more assets. The catalog component manages analytics associated with the one or more assets, where the catalog component manages a set of models for the one or more assets. The model suite component selects a subset of models from the set of models. The model processing/learning component process the subset of models and performs learning associated with the subset of models to predict a health state for the one or more assets.

Abstract: A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents at least one of a condition or performance associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

Abstract: A wash optimization system and related methods are provided that increase the efficiency and the effectiveness of engine washes. A system comprising at least one processor receives sensor data representing one or more measured parameters of a turbine engine and determines at least one performance parameter based on the sensor data. The at least one performance parameter represents one or more particulate values associated with the turbine engine. The system generates a health state for the turbine engine based on the at least one performance parameter and generates a wash identifier based on the health state of the turbine engine.

Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.

Abstract: A computer-implemented tool and method of operating a calculational model is provided. The method includes selecting a model from a predetermined plurality of available models, entering input data corresponding to preformatted data entry fields that are predetermined for the selected model, converting the entered input data to a predetermined format corresponding to the selected model, and determining, using the selected model, a model result corresponding to the converted input data and the selected model.