Abstract: Embodiments of the disclosure can relate to providing an integrated power plant advisor. In one embodiment, a method for providing an integrated power plant advisor can include receiving a signal associated with a failure of a power plant or a power plant component. The method can further include determining one or more root causes associated with the failure of the power plant or the power plant component. Based at least in part on operational data and training data from one or more power plants, a ranking of the one or more root causes associated with the failure of the power plant or the power plant component can be determined. The method can further include outputting the ranking via a client device. Based at least in part on the ranking, a repair or replacement strategy for the power plant or the power plant component can be identified.

Abstract: A system is provided comprising a memory configured to store instructions and a processor configured to execute the instructions. The processor is configured to execute the instructions to receive sensor data comprising sensed operations for a machinery, the sensed operations sensed via one or more sensors disposed in the machinery, and to derive a first model matrix based on the sensor data. The processor is further configured to derive a covariance regression model based on the first model matrix, wherein the covariance regression model is configured to be executed to derive a predictive event based on operational machinery data as input.

Abstract: Disclosed are biomarkers, methods and assay for the identification of cancer patients who are predicted to benefit from the therapeutic administration of Wnt antagonist. The biomarkers include detection of RNF43 and ZNRF3 gene deletion, reduced RNF43 and ZNRF3 mRNA expression, reduced RNF43 and ZNRF3 protein expression, RNF43 and ZNRF3 inactivation mutation, phosphorylated LRP6, phophorylated Dishevelleds, and the expression of Frizzleds. These biomarkers can be associated with the better outcome for cancer patients treated with Wnt pathway inhibitors.

Abstract: A control system for a gas turbine includes a processor. The processor configured to access one or more operating parameters of the gas turbine. The operating parameters are configured to specify how the gas turbine operates. The processor is configured to predict a rate of degradation to one or more parts of a compressor of the gas turbine due to one or more effects on the parts by operating the gas turbine according to the one or more operating parameters. The processor is configured to send an alert to an electronic device based at least in part on the rate of degradation of the compressor.

Abstract: Power systems and methods of controlling power systems are disclosed. The system may include a power plant system (PPS) including a component and at least one computing device in communication with the PPS. The computing device(s) may be configured to control the PPS by performing processes including determining if an operational anomaly for the component occurred during operation of the PPS. In response to determining that the operational anomaly did not occurr, the computing device(s) may also perform processes including determining a first operational reliability of the component. In response to determining the operational anomaly occurred, the computing device(s) may also perform processes including determining a second operational reliability of the component based on the determined operational anomaly.

Abstract: A monitoring system for a gas turbine includes a processor configured to receive an operating signal indicating an operating parameter of the gas turbine. The processor is configured to predict an occurrence of a lean blowout (LBO) event based on the operating parameter and an entropy ratio of combustion dynamics associated with a combustor of the gas turbine, wherein the LBO event corresponds to when the combustor stops firing. The processor is configured to send an alarm signal indicating the predicted LBO event to an electronic device prior to the occurrence of the LBO event.

Abstract: Embodiments of the disclosure can relate to reliability monitoring. In one embodiment, a method for reliability monitoring can include receiving operational data associated with a power plant or a power plant component. The method may further include receiving training data from one or more different power plants and receiving geographical information system (GIS) data associated with the power plant or the power plant component. Based at least in part on the operational data, the training data, and the GIS data, the method includes determining a failure probability score and a remaining life associated with operation of the power plant or the power plant component. Also, based at least in part on the operational data, the training data, and the GIS data, the method includes detecting one or more anomalies associated with operation of the power plant or the power plant component.

Abstract: A system is provided comprising a memory configured to store instructions and a processor configured to execute the instructions. The processor is configured to execute the instructions to receive sensor data comprising sensed operations for a machinery, the sensed operations sensed via one or more sensors disposed in the machinery, and to derive a first model matrix based on the sensor data. The processor is further configured to derive a covariance regression model based on the first model matrix, wherein the covariance regression model is configured to be executed to derive a predictive event based on operational machinery data as input.

Abstract: Embodiments of the disclosure can relate to providing an integrated power plant advisor. In one embodiment, a method for providing an integrated power plant advisor can include receiving a signal associated with a failure of a power plant or a power plant component. The method can further include determining one or more root causes associated with the failure of the power plant or the power plant component. Based at least in part on operational data and training data from one or more power plants, a ranking of the one or more root causes associated with the failure of the power plant or the power plant component can be determined. The method can further include outputting the ranking via a client device. Based at least in part on the ranking, a repair or replacement strategy for the power plant or the power plant component can be identified.

Abstract: A control system for a gas turbine includes a processor. The processor configured to access one or more operating parameters of the gas turbine. The operating parameters are configured to specify how the gas turbine operates. The processor is configured to predict a rate of degradation to one or more parts of a compressor of the gas turbine due to one or more effects on the parts by operating the gas turbine according to the one or more operating parameters. The processor is configured to send an alert to an electronic device based at least in part on the rate of degradation of the compressor.

Abstract: A monitoring system for a gas turbine includes a processor configured to receive an operating signal indicating an operating parameter of the gas turbine. The processor is configured to predict an occurrence of a lean blowout (LBO) event based on the operating parameter and an entropy ratio of combustion dynamics associated with a combustor of the gas turbine, wherein the LBO event corresponds to when the combustor stops firing. The processor is configured to send an alarm signal indicating the predicted LBO event to an electronic device prior to the occurrence of the LBO event.

Abstract: A control system for a gas turbine includes a processor. The processor configured to access data indicative of environmental conditions of a location of the gas turbine. The processor is configured to predict an occurrence of an event associated with the gas turbine based on the environmental conditions, wherein the event comprises a change in operation of the gas turbine due to the environmental conditions. The processor is configured to send a signal indicating the occurrence of the event to an electronic device.

Abstract: Disclosed are biomarkers, methods and assay for the identification of cancer patients who are predicted to benefit from the therapeutic administration of Wnt antagonist. The biomarkers include detection of RNF43 and ZNRF3 gene deletion, reduced RNF43 and ZNRF3 mRNA expression, reduced RNF43 and ZNRF3 protein expression, RNF43 and ZNRF3 inactivation mutation, phosphorylated LRP6, phophorylated Dishevelleds, and the expression of Frizzleds. These biomarkers can be associated with the better outcome for cancer patients treated with Wnt pathway inhibitors.

Abstract: Disclosed are biomarkers, methods and assay for the identification of cancer patients who are predicted to benefit from the therapeutic administration of Wnt antagonist. The biomarkers include detection of RNF43 and ZNRF3 gene deletion, reduced RNF43 and ZNRF3 mRNA expression, reduced RNF43 and ZNRF3 protein expression, RNF43 and ZNRF3 inactivation mutation, phosphorylated LRP6, phophorylated Dishevelleds, and the expression of Frizzleds. These biomarkers can be associated with the better outcome for cancer patients treated with Wnt pathway inhibitors.

Abstract: A system for performance monitoring of power plants is provided. The system includes a plurality of power plants respectively owned and operated by a plurality of customers, a monitoring center, at least one of the customers and the monitoring center including a plurality of analytic algorithms configured to automate performance anomaly detection, alarming, analytics and prognosis and a secured network by which on-site monitoring (OSM) data generated at each of the plurality of the power plants is receivable by the monitoring center. The monitoring center is configured to analyze the OSM data along with site specific data for each respective power plant in accordance with the plurality of analytics algorithms to derive trends and to take action with respect to each respective power plant in accordance with changes and abnormalities indicated by the derived trends.

Abstract: Systems and methods for fault prediction are described to reduce equipment failure by effectively monitoring equipment, removing anomalous data, and reducing false alarms. Such systems and methods can be used to receive monitoring data, extract information from the data, and combine extracted information for establishing prediction models. Additionally, fault probabilities may be quantified and faults may be predicted based on the probabilities.

Abstract: Systems and methods are disclosed herein for enhancing turbomachine operations. Such systems and methods include a hybrid risk model. The hybrid risk model includes a physics-based sub model and a statistical sub model. The physics-based sub model is configured to model physical components of a turbomachine. The statistical sub model is configured to model historical information of the turbomachine. The hybrid risk model is configured to calculate a turbomachine parameter.

Abstract: Systems and methods for fault prediction are described to reduce equipment failure by effectively monitoring equipment, removing anomalous data, and reducing false alarms. Such systems and methods can be used to receive monitoring data, extract information from the data, and combine extracted information for establishing prediction models. Additionally, fault probabilities may be quantified and faults may be predicted based on the probabilities.

Abstract: A monitoring and diagnostic (M&D) center includes continuous monitoring systems (CMS) for one or more remote sites. Measurement data taken at the remote site(s) is used to generate and analyze output. An on-site validation tool performs diagnostics and prognostics with output generated over different periods of time with different algorithms to generate recommendations regarding hardware upgrades, design improvements, or modified maintenance schedule.

Abstract: A system for monitoring a gas turbine includes a memory containing information from comparable gas turbines and an input device that generates a unit data signal and a risk signal. A processor in communication with the memory and the input device incorporates the unit data signal into the database, projects information for the gas turbine, and calculates a conditional risk that the gas turbine will reach a limit. An output signal includes repair or maintenance schedules. A method for monitoring a gas turbine includes receiving information from comparable gas turbines, adding information from the gas turbine to the information from comparable gas turbines, and projecting information for the gas turbine. The method further includes calculating a conditional risk that the gas turbine will reach a limit and generating an output signal containing repair or maintenance schedules.