Abstract: A system and method are provided for controlling a wind turbine. Accordingly, a controller of the wind turbine detects a loss of traction of the slip coupling based on a difference between data indicative of a rotor operating parameter and data indicative of a generator operating parameter. The controller then determines an angle of slip corresponding to the loss of traction as a function of the difference. Based, at least partially on the angle of slip, a degradation value for the slip coupling is determined. A control action is implemented based on the degradation value.

Abstract: A method for detecting anomalies during operation of an asset to improve performance of the asset includes collecting, via a server, data relating to operation of the asset or a group of assets containing the asset. The data includes normal and abnormal asset behavior of the asset or the group of assets containing the asset. Further, the method includes automatically removing, via an iterative algorithm programmed in the server that utilizes one or more inputs or outputs of an anomaly detection analytic, portions of the data containing the abnormal asset behavior to form a dataset containing only the normal asset behavior. The method also includes training, via a computer-based model programmed in the server, the anomaly detection analytic using, at least, the dataset containing only the normal asset behavior. Moreover, the method includes applying, via the server, the anomaly detection analytic to the asset so as to monitor for anomalies during operation thereof.

Abstract: A method for controlling a wind turbine connected to an electrical grid includes receiving, via a controller, a state estimate of the wind turbine. The method also includes determining, via the controller, a current condition of the wind turbine using, at least, the state estimate, the current condition defining a set of condition parameters of the wind turbine. Further, the method includes receiving, via the controller, a control function from a supervisory controller, the control function defining a relationship of the set of condition parameters with at least one operational parameter of the wind turbine. Moreover, the method includes dynamically controlling, via the controller, the wind turbine based on the current condition and the control function for multiple dynamic control intervals.

Abstract: A method for operating a wind turbine includes determining one or more loading and travel metrics or functions thereof for one or more components of the wind turbine during operation of the wind turbine. The method also includes generating, at least in part, at least one distribution of cumulative loading data for the one or more components using the one or more loading and travel metrics during operation of the wind turbine. Further, the method includes applying a life model of the one or more components to the at least one distribution of cumulative loading data to determine an actual damage accumulation for the one or more components of the wind turbine to date. Moreover, the method includes implementing a corrective action for the wind turbine based on the damage accumulation.

Abstract: A system and method are provided for controlling a wind turbine. Accordingly, a controller of the wind turbine detects a loss of traction of the slip coupling based on a difference between data indicative of a rotor operating parameter and data indicative of a generator operating parameter. The controller then determines an angle of slip corresponding to the loss of traction as a function of the difference. Based, at least partially on the angle of slip, a degradation value for the slip coupling is determined. A control action is implemented based on the degradation value.

Abstract: A method for operating a wind turbine includes determining one or more loading and travel metrics or functions thereof for one or more components of the wind turbine during operation of the wind turbine. The method also includes generating, at least in part, at least one distribution of cumulative loading data for the one or more components using the one or more loading and travel metrics during operation of the wind turbine. Further, the method includes applying a life model of the one or more components to the at least one distribution of cumulative loading data to determine an actual damage accumulation for the one or more components of the wind turbine to date. Moreover, the method includes implementing a corrective action for the wind turbine based on the damage accumulation.

Abstract: A method for controlling a wind turbine connected to an electrical grid includes receiving, via a controller, a state estimate of the wind turbine. The method also includes determining, via the controller, a current condition of the wind turbine using, at least, the state estimate, the current condition defining a set of condition parameters of the wind turbine. Further, the method includes receiving, via the controller, a control function from a supervisory controller, the control function defining a relationship of the set of condition parameters with at least one operational parameter of the wind turbine. Moreover, the method includes dynamically controlling, via the controller, the wind turbine based on the current condition and the control function for multiple dynamic control intervals.

Abstract: A method for detecting anomalies during operation of an asset to improve performance of the asset includes collecting, via a server, data relating to operation of the asset or a group of assets containing the asset. The data includes normal and abnormal asset behavior of the asset or the group of assets containing the asset. Further, the method includes automatically removing, via an iterative algorithm programmed in the server that utilizes one or more inputs or outputs of an anomaly detection analytic, portions of the data containing the abnormal asset behavior to form a dataset containing only the normal asset behavior. The method also includes training, via a computer-based model programmed in the server, the anomaly detection analytic using, at least, the dataset containing only the normal asset behavior. Moreover, the method includes applying, via the server, the anomaly detection analytic to the asset so as to monitor for anomalies during operation thereof.

Abstract: A detection system to detect an object in a blowout prevention system of a production system includes a sensor coupled to the blowout prevention system and configured to send an ultrasonic pulse toward the object. The sensor is further configured to receive a signal including the ultrasonic pulse and noise after the ultrasonic pulse interacts with the object. The detection system also includes a controller coupled to the sensor and configured to identify the ultrasonic pulse in the signal using a first cancellation signal at a first time and a second cancellation signal at a second time. The controller is further configured to determine that the first cancellation signal corresponds to the noise in the signal at the first time, and determine that the second cancellation signal corresponds to the noise in the signal at the second time. The controller is configured to determine a characteristic of the object based on the ultrasonic pulse.

Abstract: A method for operating a combustion chamber is provided. The method includes obtaining a carbon monoxide reading at an exit of the combustion chamber via a carbon monoxide sensor, and deriving an oxygen set point trim based at least in part on the carbon monoxide reading and a carbon monoxide set point via a controller. The method further includes determining a stability status of the combustion chamber via a combustion stability sensor, and adjusting an oxygen set point of the combustion chamber with the oxygen set point trim based at least in part on the stability status via the controller. The oxygen set point defines a desired oxygen level at the exit of the combustion chamber.

Abstract: A detection system includes a first sensor configured to send a first ultrasonic pulse toward an object in a blowout prevention system. The first ultrasonic pulse has a first parameter. The detection system also includes a second sensor spaced from the first sensor and configured to send a second ultrasonic pulse toward the object. The second ultrasonic pulse has a second parameter that is different from the first parameter of the first ultrasonic pulse. The first parameter and the second parameter are one of an amplitude, a frequency, a duration, an emission time, and an excitation code. The second sensor is further configured to receive the first ultrasonic pulse after the first ultrasonic pulse interacts with the object. The detection system is configured to determine that the first ultrasonic pulse received by the second sensor was sent by the first sensor.

Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing an incremental ML model comprising a plurality increments which sequentially increase a complexity of a predictive function of the incremental ML model, receiving performance information from an edge system that processes incoming data of an industrial asset using a current increment of the incremental ML model, dynamically determining to modify the current increment of the incremental ML model used by the edge system with a next increment of the incremental ML model having increased complexity based on the received performance information, and transmitting the next increment of the incremental ML model to the edge system.

Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing machine learning (ML) models and local edge information where the ML models are already deployed, receiving, via a network, meta information of an edge system associated with an industrial asset in response to a cold start of the edge system, dynamically determining an optimum ML model for the cold start of the edge system from among the already deployed ML models based on the received meta information and the local edge information, and transmitting the determined optimum ML model to the edge system.

Abstract: The example embodiments are directed to a system and method for optimizing data the is transmitted from an edge device to a central server such as the cloud platform. In one example, the method may include one or more of receiving incoming data which is associated with an industrial asset positioned at an edge of an Internet of Things (IoT) network, transforming the incoming data into a pattern of data points within a feature space based on a machine learning model configured to detect patterns within the data, selecting a subset of data points from the pattern based on a distance between data points in the pattern of data points with respect to a previous pattern of data points in a previous dataset associated with the industrial asset, and transmitting the selected subset of data points to a central platform via the IoT network.

Abstract: The example embodiments are directed to a system for triggering a model update for an edge device in an IIoT network. In one example, the method may include one or more of receiving data of an operation performed by an industrial asset, the received data comprising input for a machine learning (ML) model associated with the industrial asset, determining that the received data comprises a change in data pattern with respect to a training data set which was used to previously train the ML model, storing the received data comprising the change in data pattern in a new data set, and in response to the new data set reaching a minimum threshold size, at least one of updating the ML model based on the new data set and transmitting a request to update the ML model based on the new data set.

Abstract: The example embodiments are directed to a system and methods for determining to update a machine learning model based on model degradation. In one example, the method may include one or more of receiving data acquired at an edge of an Internet of things (IoT) network from an industrial asset, executing a machine learning model with the received data as input to generate a predictive output associated with the industrial asset, determining that a performance of the machine learning model on the edge has degraded based on the generated predictive output of the machine learning model, and transmitting information about the degraded performance of the machine learning model to a central server within the IoT network.

Abstract: The example embodiments are directed to a system and method for sharing machine learning model parameters among edge devices in a clustered group of edge devices sensing data about an industrial asset. In one example, the method may include one or more of storing unique parameters of a machine learning (ML) model associated with an industrial asset which are unique with respect to unique parameters of other edge systems in the group of edge systems, receiving common parameter information from the group of edge systems which is shared among the group of edge systems, generating updated parameter values for an ML model based on a combination of the unique parameters and the received common parameter information, and executing the updated ML model based on incoming data from the industrial asset to generate predictive information about the industrial asset.

Abstract: An illustrative method of estimating a temperature for at least one of a plurality of sites based on temperature information from at least one of a plurality of temperature measurement stations includes several steps. The sites are clustered into a plurality of clusters. Respective centroids for the clusters are determined. The centroids are associated with respective measurement stations. One of a preselected plurality of estimating techniques is respectively assigned to the measurement stations. The assigned estimating technique provides a temperature estimate at the measurement station that is more accurate than others of the preselected plurality of estimating techniques. A temperature at the at least one of the plurality of sites is estimated using the estimating technique assigned to the measurement station associated with the centroid of the cluster that includes the site of interest.

Abstract: A method for operating a combustion chamber is provided. The method includes obtaining a carbon monoxide reading at an exit of the combustion chamber via a carbon monoxide sensor, and deriving an oxygen set point trim based at least in part on the carbon monoxide reading and a carbon monoxide set point via a controller. The method further includes determining a stability status of the combustion chamber via a combustion stability sensor, and adjusting an oxygen set point of the combustion chamber with the oxygen set point trim based at least in part on the stability status via the controller. The oxygen set point defines a desired oxygen level at the exit of the combustion chamber.

Abstract: A detection system to detect an object in a blowout prevention system of a production system includes a sensor coupled to the blowout prevention system and configured to send an ultrasonic pulse toward the object. The sensor is further configured to receive a signal including the ultrasonic pulse and noise after the ultrasonic pulse interacts with the object. The detection system also includes a controller coupled to the sensor and configured to identify the ultrasonic pulse in the signal using a first cancellation signal at a first time and a second cancellation signal at a second time. The controller is further configured to determine that the first cancellation signal corresponds to the noise in the signal at the first time, and determine that the second cancellation signal corresponds to the noise in the signal at the second time. The controller is configured to determine a characteristic of the object based on the ultrasonic pulse.