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 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: Systems and methods for gas turbine combustion profile monitoring are disclosed. In one embodiment, a method for detecting an anomaly in a combustion section of a gas turbine is disclosed. The method includes receiving, from a plurality of thermal sensors disposed around an exhaust section of a gas turbine, exhaust profile data of the gas turbine. The method further analyzes the exhaust profile data to calculate statistical features associated with a peak-trough pattern. The method further determines, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the method processes the exhaust profile data for a predetermined period of time and reports an anomaly in a combustion section of the gas turbine if the statistical features remain abnormal for the predetermined period of time.

Abstract: Systems and methods for blade health monitoring are provided. According to one embodiment of the disclosure, a system may include a feature extraction module and an anomaly detection module in communication with the extraction module. The feature extraction module may be configured to continuously receive blade passing signal data associated with clearance of a blade and pre-process the blade passing signal data. Blade clearance feature data may be extracted from the blade passing signal data prior to transmission to the anomaly detection module. The anomaly detection module may be configured to normalize the blade clearance feature data received from the extraction module, analyze the blade clearance feature data to detect a shift in the clearance of the blade, and determine an abnormality of the blade based on the shift exceeding a predetermined shift threshold.

Abstract: Some embodiments are associated with a receipt, at a feature learning platform, of sensor data associated with normal operation of an industrial asset, the sensor data including values for a plurality of sensors over a period of time. The feature learning platform may extract a plurality of features via hierarchically deep learning, which may capture characteristics of normal operation of the industrial asset and provide the learned features to a classification modeling platform. The classification modeling platform may then create classification models utilizing the learned features, and the classification models may be executed to automatically identify a potential anomaly for an operating industrial asset.

Abstract: Systems and methods for gas turbine combustion profile monitoring are disclosed. In one embodiment, a method for detecting an anomaly in a combustion section of a gas turbine is disclosed. The method includes receiving, from a plurality of thermal sensors disposed around an exhaust section of a gas turbine, exhaust profile data of the gas turbine. The method further analyzes the exhaust profile data to calculate statistical features associated with a peak-trough pattern. The method further determines, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the method processes the exhaust profile data for a predetermined period of time and reports an anomaly in a combustion section of the gas turbine if the statistical features remain abnormal for the predetermined period of time.

Abstract: A system including: at least one computing device configured to monitor a compressor blade in a turbomachine by: extracting time of arrival (TOA) data about the compressor blade at a defined interval; correlating turbomachine operating conditions with the TOA data; extracting resonance data about the compressor blade during startup or shutdown of the turbomachine, and extracting static deflection and tip clearance data about the compressor blade during steady state operation of the turbomachine; creating a baseline compressor blade comparison level based upon the extracted resonance data, extracted static deflection and tip clearance data, and correlation between the turbomachine operating conditions and the TOA data; and iteratively extracting updated TOA data and performing the correlating, the extracting of the resonance data, static deflection and tip clearance data using the updated TOA data at the defined interval to create an updated baseline compressor blade comparison level.

Abstract: Systems and methods for blade health monitoring are provided. According to one embodiment of the disclosure, a system may include a feature extraction module and an anomaly detection module in communication with the extraction module. The feature extraction module may be configured to continuously receive blade passing signal data associated with clearance of a blade and pre-process the blade passing signal data. Blade clearance feature data may be extracted from the blade passing signal data prior to transmission to the anomaly detection module. The anomaly detection module may be configured to normalize the blade clearance feature data received from the extraction module, analyze the blade clearance feature data to detect a shift in the clearance of the blade, and determine an abnormality of the blade based on the shift exceeding a predetermined shift threshold.

Abstract: A system for monitoring physiology, having: a RADAR transmitter and a RADAR receiver; a state estimation module configured to process a returned RADAR signal to detect a presence of motion and set a motion state upon said presence of motion, said state estimation module configured to detect a presence of one or more physiological parameters including heartbeat and respiration, and said state estimation module configured to assign a still state or a concern state based on said presence of physiological parameters; a rate estimation module configured to estimate one or more estimated physiological rates including an estimated respiration rate and an estimated heart rate; and an alerting module configured to provide an alert if an alert value exceeds an alert value threshold, wherein the alert value is derived from at least one of the motion state, concern state, still state and the estimated physiological rates.

Abstract: Total analysis systems and methods for simultaneously monitoring a suite of biological and/or chemical species in water and/or other process systems are disclosed. The system provides a sample-volume controlled sensor array comprising a fluid delivery device and a plurality of optical sensor elements for determining the presence and total concentrations of multiple analytes in the process system simultaneously. Image identification algorithms are provided for identifying the analytes based on image intensity, color pattern, positional arrangement, and the like. The methods incorporate multivariate optimization algorithms to analyze multiple sensor responses. This produces analytical results that are typically difficult to obtain without full system or variable compensation. The improved array response may then be utilized to measure, monitor, and control the concentration of analytes in the chemical or biological sample or water system.

Abstract: A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices.

Abstract: Total analysis systems and methods for simultaneously monitoring a suite of biological and/or chemical species in water and/or other process systems are disclosed. The system provides a sample-volume controlled sensor array comprising a fluid delivery device and a plurality of optical sensor elements for determining the presence and total concentrations of multiple analytes in the process system simultaneously. Image identification algorithms are provided for identifying the analytes based on image intensity, color pattern, positional arrangement, and the like. The methods incorporate multivariate optimization algorithms to analyze multiple sensor responses. This produces analytical results that are typically difficult to obtain without full system or variable compensation. The improved array response may then be utilized to measure, monitor, and control the concentration of analytes in the chemical or biological sample or water system.

Abstract: Total analysis systems and methods for simultaneously monitoring a suite of biological and/or chemical species in water and/or other process systems are disclosed. The system provides a sample-volume controlled sensor array comprising a fluid delivery device and a plurality of optical sensor elements for determining the presence and total concentrations of multiple analytes in the process system simultaneously. Image identification algorithms are provided for identifying the analytes based on image intensity, color pattern, positional arrangement, and the like. The methods incorporate multivariate optimization algorithms to analyze multiple sensor responses. This produces analytical results that are typically difficult to obtain without full system or variable compensation. The improved array response may then be utilized to measure, monitor, and control the concentration of analytes in the chemical or biological sample or water system.

Abstract: A technique is disclosed for evaluating and monitoring performance of a heat exchanger system. Operating parameters of the system are monitored and fouling factors for heat transfer surfaces of the exchanger are determined. Trending of fouling may be performed over time based upon the fouling factors, and a model of fouling may be selected from known sets of models, or a model may be developed or refined. Fluid treatment, such as water treatment regimes may be taken into account in evaluation of fouling. An automated knowledge based analysis algorithm may diagnose possible caused of fouling based upon sensed and observed parameters and conditions. Corrective actions may be suggested and the system controlled to reduce, avoid or correct for detected fouling.

Abstract: A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices.

Abstract: The present invention provides a computer implemented method for maintaining a knowledge base. The method taking as input, text examples that are tagged with a structural markup language; and maps knowledge nodes in the knowledge base with the tagged examples to determine: (1) the knowledge nodes that best match the tagged examples, and (2) the knowledge nodes that are best connected to the tagged examples. The results are displayed to the user, who verifies that the tagged examples match the selected knowledge nodes. Based on the user response, indices of the knowledge nodes are updated. The method may further include determining discrepancies between the existing knowledge base and the tagged examples, and further displaying the discrepancies to the user. The user can update the missing information in the knowledge base to remove the discrepancies in the existing knowledge base.

Abstract: A method for diagnosing and classifying faults in a system is provided. The method comprises acquiring operational data for at least one of a system, one or more subsystems of the system or one or more components of the one or more subsystems. Then, the method comprises analyzing the operational data using one or more diagnostic models. Each diagnostic model uses the operational data to determine a probability of fault associated with at least one of the one or more components or the one or more subsystems. Finally, the method comprises deriving an overall probability of fault for at least one of the system, the one or more subsystems, or the one or more components using the one or more probabilities of fault determined by the one or more diagnostic models and one or more hierarchical relationships between the subsystems and components of the system.

Abstract: A method for diagnosing and classifying faults in a system is provided. The method comprises acquiring operational data for at least one of a system, one or more subsystems of the system or one or more components of the one or more subsystems. Then, the method comprises analyzing the operational data using one or more diagnostic models. Each diagnostic model uses the operational data to determine a probability of fault associated with at least one of the one or more components or the one or more subsystems. Finally, the method comprises deriving an overall probability of fault for at least one of the system, the one or more subsystems, or the one or more components using the one or more probabilities of fault determined by the one or more diagnostic models and one or more hierarchical relationships between the subsystems and components of the system.

Abstract: Total analysis systems and methods for simultaneously monitoring a suite of biological and/or chemical species in water and/or other process systems are disclosed. The system provides a sample-volume controlled sensor array comprising a fluid delivery device and a plurality of optical sensor elements for determining the presence and total concentrations of multiple analytes in the process system simultaneously. Image identification algorithms are provided for identifying the analytes based on image intensity, color pattern, positional arrangement, and the like. The methods incorporate multivariate optimization algorithms to analyze multiple sensor responses. This produces analytical results that are typically difficult to obtain without full system or variable compensation. The improved array response may then be utilized to measure, monitor, and control the concentration of analytes in the chemical or biological sample or water system.

Abstract: Automated systems and methods for diagnosing the cause of a trend shift in performance data associated with a system including selecting a fuzzy model describing how the mean associated with a parameter shifts when a predetermined problem occurs and receiving performance data associated with the system. The diagnostic systems and methods also including applying a preferred set of piecewise regressions. The diagnostic systems and methods further including selecting a start date and selecting data sets near the start date and an end date that have a relatively large size without violating normal scatter. The diagnostic systems and methods still further including measuring the mean shift between a plurality of samples using one or more statistical tests and combining the results with the fuzzy model to achieve a diagnosis.