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: Methods, apparatus, systems, and articles of manufacture to generate an asset workscope are disclosed. An example system includes an asset health calculator to identify an asset on which to perform maintenance based on generating a first asset health quantifier corresponding to a first asset health status, a task generator to determine a first workscope including first asset maintenance tasks, and a task optimizer to determine a second workscope including second asset maintenance tasks based on the first asset health quantifier and/or first workscope. The example system includes a workscope effect calculator to generate a second asset health quantifier corresponding to a second asset health status when the second workscope is completed on the asset, and to update the asset health calculator, task generator, and/or task optimizer to improve determination of first and/or second workscopes to improve the second asset health quantifier relative to the first asset health quantifier.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to generate a predictive asset health quantifier of a turbine engine. An example apparatus includes a performance model analyzer to determine a fleet behavior parameter by generating a reference performance model using historical information for a fleet of operators using turbine engines, generate a residual performance model based on calculating a difference between the fleet behavior parameter and a plurality of operator behavior parameters, identify an operator as a candidate improvement target based on comparing the operator behavior parameters corresponding to the operator to the fleet in the residual performance model, and determine an adjusted operator behavior parameter for the candidate improvement target.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to generate a predictive asset health quantifier of a turbine engine. An example apparatus includes a performance model analyzer to determine a fleet behavior parameter by generating a reference performance model using historical information for a fleet of operators using turbine engines, generate a residual performance model based on calculating a difference between the fleet behavior parameter and a plurality of operator behavior parameters, identify an operator as a candidate improvement target based on comparing the operator behavior parameters corresponding to the operator to the fleet in the residual performance model, and determine an adjusted operator behavior parameter for the candidate improvement target.

Abstract: The present disclosure is directed to an automated system and method of analyzing performance of engines in a test cell. The method includes electronically accessing engine operational data in a stored database of the test cell. Another step includes electronically retrieving operational data corresponding to the new test if at least one new test is detected in the stored database for an engine. Further, the method includes inputting the operational data corresponding to the new test into a performance model specific to an engine type for the engine. The method also includes electronically analyzing an output of the performance model. An additional step includes electronically generating at least one summary report of engine health of the engine based on the analyzed output. Thus, the method also includes providing the summary report as electronic output to a user.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to generate a predictive asset health quantifier of a turbine engine. An example apparatus includes a performance model analyzer to determine a fleet behavior parameter by generating a reference performance model using historical information for a fleet of operators using turbine engines, generate a residual performance model based on calculating a difference between the fleet behavior parameter and a plurality of operator behavior parameters, identify an operator as a candidate improvement target based on comparing the operator behavior parameters corresponding to the operator to the fleet in the residual performance model, and determine an adjusted operator behavior parameter for the candidate improvement target.

Abstract: Methods, apparatus, systems, and articles of manufacture to generate an asset workscope are disclosed. An example system includes an asset health calculator to identify an asset on which to perform maintenance based on generating a first asset health quantifier corresponding to a first asset health status, a task generator to determine a first workscope including first asset maintenance tasks, and a task optimizer to determine a second workscope including second asset maintenance tasks based on the first asset health quantifier and/or first workscope. The example system includes a workscope effect calculator to generate a second asset health quantifier corresponding to a second asset health status when the second workscope is completed on the asset, and to update the asset health calculator, task generator, and/or task optimizer to improve determination of first and/or second workscopes to improve the second asset health quantifier relative to the first asset health quantifier.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate an asset health quantifier of a turbine engine. An example apparatus includes a health quantifier generator to execute a computer-generated model to simulate an operating condition of a turbine engine using asset monitoring information and generate an asset health quantifier of the turbine engine based on the simulation, and compare the asset health quantifier to a threshold, and a removal scheduler to identify the turbine engine for removal from service based on the comparison to improve an operation of the turbine engine by performing a workscope on the removed 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 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: The present disclosure is directed to an automated system and method of analyzing performance of engines in a test cell. The method includes electronically accessing engine operational data in a stored database of the test cell. Another step includes electronically retrieving operational data corresponding to the new test if at least one new test is detected in the stored database for an engine. Further, the method includes inputting the operational data corresponding to the new test into a performance model specific to an engine type for the engine. The method also includes electronically analyzing an output of the performance model. An additional step includes electronically generating at least one summary report of engine health of the engine based on the analyzed output. Thus, the method also includes providing the summary report as electronic output to a user.

Abstract: A system for collecting and storing performance data for an engine is provided. The system includes one or more sensors configured to generate sensor data signals representative of one or more engine data performance parameters. The system further includes a data sampling component, a data quantizing component, a data storage sampling rate component, a data encoding component and a data storage component. The data sampling component is configured to sample the sensor data signals at a data sampling rate. The data quantizing component is configured to generate quantized data samples corresponding to the sampled sensor data signals. The data storage sampling rate component is configured to determine a data storage sampling rate for the quantized data samples, based on an analysis of at least a subset of the quantized data samples.

Abstract: A system for collecting and storing performance data for an engine is provided. The system includes one or more sensors configured to generate sensor data signals representative of one or more engine data performance parameters. The system further includes a data sampling component, a data quantizing component, a data storage sampling rate component, a data encoding component and a data storage component. The data sampling component is configured to sample the sensor data signals at a data sampling rate. The data quantizing component is configured to generate quantized data samples corresponding to the sampled sensor data signals. The data storage sampling rate component is configured to determine a data storage sampling rate for the quantized data samples, based on an analysis of at least a subset of the quantized data samples.