Abstract: In one embodiment, a method includes accessing a vessel's data profiles comprising a first data profile configured for assessing condition or integrity risks, a second data profile configured for assessing statutory, regulatory, and port state control, a third data profile configured for assessing quality of management systems, a fourth data profile configured for assessing class trend of sister vessels, and a fifth data profile configured for assessing sustainability based on fuel consumption and emissions, analyzing the accessed data profiles by a predictive compliance model configured for quantifying and assessing an overall risk of vessels being out of compliance with standards, determining a class-related risk profiling capability and risks of systems and components of the vessel with respect to condition and class compliance based on the analysis, and sending instructions to a client system for presenting the class-related risk profiling capability and the risks to a user (e.g., a vessel operator).

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.

Abstract: A monitoring system includes a processor configured to calculate a first distance of a first signal, wherein the first distance represents changes in magnitude of the first signal over a period of time, and wherein the first signal is associated with a desired signal output of a feedback loop system. The processor is configured to receive a second signal from an output of the feedback loop system. The processor is configured to calculate a second distance of the second signal, wherein the second distance represents changes in magnitude of the second signal over the period of time. The processor is configured to determine a first difference between the first distance and the second distance. The processor is configured to provide an error signal indicating an error if the difference exceeds a threshold value.

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.

Abstract: A system includes a processor. The processor is configured to identify relevant factors related to a configuration of a power production system comprising a gas turbine system based at least on a fleet data for a fleet of the gas turbine system. The processor is further configured to build one or more risk models configured to derive a probability of achieving a performance level for the configuration based on the relevant factors. The processor is additionally configured to execute the risk models to derive an engineering recommendation, a commercial recommendation, or a combination thereof.

Abstract: A monitoring system includes an analytical engine system coupled to a sensor of an engine system. The analytical engine system is configured to receive data corresponding to operation of the engine system, to determine a distance metric corresponding to the operating parameters of the engine system, to compare the distance metric for a monitored lubricant temperature to a model threshold, and to generate a lubricant alert signal when the distance metric for the monitored lubricant temperature is greater than the model threshold. The received data includes the monitored lubricant temperature of a bearing and operating parameters of the engine system. The distance metric is based at least in part on the monitored lubricant temperature relative to a lubricant temperature statistical model, which is based at least in part on the operating parameters of the engine system.

Abstract: In one embodiment, a processor is configured to execute the instructions to receive a first data comprising sensed operations for one or more turbine systems in a fleet of turbine systems. The sensed operations are sensed via a plurality of sensors disposed in the one or more turbine systems. The processor is also configured to execute the instructions to extract a second data comprising a plurality of events included in a turbine controller event log, to derive at least one sensor model based on the first data, to derive at least one association rule based on the first data, the second data, or a combination thereof, to execute the instructions to derive a combination model by combining the at least one sensor model and the at least one association rule.

Abstract: A monitoring system includes an analytical engine system coupled to a sensor of an engine system. The analytical engine system is configured to receive data corresponding to operation of the engine system, to determine a distance metric corresponding to the operating parameters of the engine system, to compare the distance metric for a monitored lubricant temperature to a model threshold, and to generate a lubricant alert signal when the distance metric for the monitored lubricant temperature is greater than the model threshold. The received data includes the monitored lubricant temperature of a bearing and operating parameters of the engine system. The distance metric is based at least in part on the monitored lubricant temperature relative to a lubricant temperature statistical model, which is based at least in part on the operating parameters of the engine system.

Abstract: A monitoring system includes a processor configured to calculate a first distance of a first signal, wherein the first distance represents changes in magnitude of the first signal over a period of time, and wherein the first signal is associated with a desired signal output of a feedback loop system. The processor is configured to receive a second signal from an output of the feedback loop system. The processor is configured to calculate a second distance of the second signal, wherein the second distance represents changes in magnitude of the second signal over the period of time. The processor is configured to determine a first difference between the first distance and the second distance. The processor is configured to provide an error signal indicating an error if the difference exceeds a threshold value.

Abstract: A system for detecting an at-fault combustor includes a sensor that is configured to sense combustion dynamics pressure data from the combustor and a computing device that is in electronic communication with the sensor and configured to receive the combustion dynamics pressure data from the sensor. The computing device is programmed to convert the combustion dynamics pressure data into a frequency spectrum, segment the frequency spectrum into a plurality of frequency intervals, extract a feature from the frequency spectrum, generate feature values for the feature within a corresponding frequency interval over a period of time, and to store the feature values to generate a historical database. The computing device is further programmed to execute a machine learning algorithm using the historical database of the feature values to train the computing device to recognize feature behavior that is indicative of an at-fault combustor.

Abstract: A system for detecting an at-fault combustor includes a sensor that is configured to sense combustion dynamics pressure data from the combustor and a computing device that is in electronic communication with the sensor and configured to receive the combustion dynamics pressure data from the sensor. The computing device is programmed to convert the combustion dynamics pressure data into a frequency spectrum, segment the frequency spectrum into a plurality of frequency intervals, extract a feature from the frequency spectrum, generate feature values for the feature within a corresponding frequency interval over a period of time, and to store the feature values to generate a historical database. The computing device is further programmed to execute a machine learning algorithm using the historical database of the feature values to train the computing device to recognize feature behavior that is indicative of an at-fault combustor.

Abstract: A system for assessing combustor health during operation of the combustor includes a combustor, a sensor configured to sense combustion dynamics pressure data from the combustor, and a computing device that is in communication with the sensor and configured to receive the combustion dynamics pressure data from the sensor. The computing device is programed to extract a feature from the combustion dynamics pressure data and generate feature values for the feature over a period of time. The computing device is also programmed to generate a cumulative form of the feature that is based on the feature values over a time series and to compare the cumulative form to a historical cumulative form.

Abstract: A turbine performance diagnostic system creates a performance report for one or more turbines and includes an assessment module that receives operating data from at least one turbine and produces a performance report from the operating data. The assessment module includes a change detection module configured to determine when at least one parameter has changed beyond an associated threshold and generate an alarm and a root cause analyzer coupled to the change detection module that predicts a root cause of the alarm utilizing a Bayesian Belief Network (BBN). The performance report includes an indication of the predicted root cause.

Abstract: A method, system and computer program product for life management and monitoring of a high temperature gas turbine including components having a thermal barrier coating is disclosed. The method, system and computer program product uses design, monitoring and diagnostics, and inspection data to determine the cumulative damage and remaining useful life of gas turbine components and unit risk and classification probability to determine thermal barrier coating damage probability, remaining useful life, and inspection recommendations.

Abstract: Methods and systems for use in predicting wind turbine failures are provided. One example method includes determining a parametric profile for a component of a wind turbine from operating data of a plurality of wind turbines, determining an anomaly profile for the component of the wind turbine from anomaly alerts from a plurality of wind turbines, and determining a probability of failure for the component of the wind turbine based on the parametric profile and the anomaly profile. The parametric profile defines at least one parametric event associated with the component prior to failure of the component, and the anomaly profile defines at least one anomaly associated with the component prior to failure of the component.

Abstract: Methods and systems for use in predicting wind turbine failures are provided. One example method includes determining a parametric profile for a component of a wind turbine from operating data of a plurality of wind turbines, determining an anomaly profile for the component of the wind turbine from anomaly alerts from a plurality of wind turbines, and determining a probability of failure for the component of the wind turbine based on the parametric profile and the anomaly profile. The parametric profile defines at least one parametric event associated with the component prior to failure of the component, and the anomaly profile defines at least one anomaly associated with the component prior to failure of the component.

Abstract: A turbine performance diagnostic system creates a performance report for one or more turbines and includes an assessment module that receives operating data from at least one turbine and produces a performance report from the operating data. The assessment module includes a change detection module configured to determine when at least one parameter has changed beyond an associated threshold and generate an alarm and a root cause analyzer coupled to the change detection module that predicts a root cause of the alarm utilizing a Bayesian Belief Network (BBN). The performance report includes an indication of the predicted root cause.

Abstract: A system for creating a financial risk model for at least one turbine includes a database storing operational characteristics of one or more turbines and a processing module coupled to the database that receives information from the database and creates the financial risk determination. The processing module includes a performance degradation modeler that receives performance data and creates a model there from and a financial risk calculator coupled to the performance modeler that determines financial exposure to a contract based on the model received from the performance modeler.

Abstract: A controller for use in managing an operational lifetime of at least one wind turbine is communicatively coupled to at least one wind turbine and a server sub-system. The controller is configured to receive operational data from the wind turbine, transmit the operational data to the server sub-system, and transmit a request for historical data corresponding to the wind turbine to the server sub-system. The controller is further configured to receive a response from the server sub-system, wherein the response includes historical data corresponding to the wind turbine, and to determine an estimate of a time failure of the wind turbine based on at least one of the operational data and the historical data.

Abstract: A controller for use in managing an operational lifetime of at least one wind turbine is communicatively coupled to at least one wind turbine and a server sub-system. The controller is configured to receive operational data from the wind turbine, transmit the operational data to the server sub-system, and transmit a request for historical data corresponding to the wind turbine to the server sub-system. The controller is further configured to receive a response from the server sub-system, wherein the response includes historical data corresponding to the wind turbine, and to determine an estimate of a time failure of the wind turbine based on at least one of the operational data and the historical data.