Abstract: A control circuit can access inspection results from an inspection of a first component and then input those inspection results to a first machine learning model. The inspection results include potential wear indications. By one approach, that first machine learning model is trained using a training corpus that includes inspection results for previously inspected components that are at least similar to the first component. The first machine learning model can output assessment information that, by one approach, identifies some of the potential wear indications as being relevant. By one approach, the aforementioned assessment information may be input a second machine learning model that is trained using a training corpus that includes historical results from previous inspections of the same first component and wherein the second machine learning model outputs prediction information regarding whether a repeated physical processing of the first component will yield a particular result.

Abstract: A system for polishing a surface of a component includes a motorized apparatus including a body, a drive system coupled to the body, and an arm including a proximal end coupled to the body and a distal end opposite the proximal end. The motorized apparatus further includes a tool coupled to the distal end of the arm. The tool is configured to polish the surface of the component. The motorized apparatus also includes an actuator coupled to the arm. The system also includes a controller configured to position the tool relative to the component by positioning at least one of the body and the arm relative to the component to reach the target area on the component while maintaining a distance between the tool and the body that is less than a threshold distance. The threshold distance is less than a full reach of the arm and is determined to prevent vibrations of the tool and the arm from exceeding a predefined level.

Abstract: A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

Abstract: Provided is a system and method for detecting source(s) of oscillation on a power grid. In one example, the method may include receiving measurements from one or more sensors on a power grid, the measurements including data of an oscillation within the power grid, determining, via execution of one or more machine learning model, a candidate set of power system components disposed on the power grid that are candidates for being the source(s) of the oscillation, identifying, via execution of an optimization model, a component from among the candidate set of power system components which is the source (e.g., location, controller type, and/or asset type) of the oscillation, and displaying, via a user interface, information about the identified component.

Abstract: A method of inspecting a component includes storing at least one inspection image file in a memory and receiving a search request associated with the at least one inspection image file. The method also includes accessing a database including a plurality of image files, comparing the hash code of the at least one inspection image file to the hash code of each image file of the plurality of image files, and identifying a first subset of image files based on the hash code comparison. The method also includes comparing the feature data of the at least one inspection image file to the feature data of each image file of the first subset of image files and classifying a second subset of image files as relevant based on the feature data comparison. The method further includes generating search results based on the second subset of image files.

Abstract: A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

Abstract: A method of inspecting a component includes storing at least one inspection image file in a memory and receiving a search request associated with the at least one inspection image file. The method also includes accessing a database including a plurality of image files, comparing the hash code of the at least one inspection image file to the hash code of each image file of the plurality of image files, and identifying a first subset of image files based on the hash code comparison. The method also includes comparing the feature data of the at least one inspection image file to the feature data of each image file of the first subset of image files and classifying a second subset of image files as relevant based on the feature data comparison. The method further includes generating search results based on the second subset of image files.

Abstract: Systems and methods for facilitating proximity detection and location tracking using a hub or bridge are disclosed. An example apparatus is to receive a message from a beacon at a second location within a proximity area of the apparatus, the message indicative of the second location and identifying the beacon; and relay the message to a location server to determine asset location.

Abstract: A method of inspecting a component using an image inspection controller that includes a processor communicatively coupled to a memory includes classifying each sample image in a first database as a first sample or a second sample using a classification module, extracting at least one class generic feature from each first sample to generate a plurality of class generic features, and extracting at least one class specific feature from each second sample to generate a plurality of class specific features. The method further includes combining the class generic features and the class specific features to generate a plurality of supplemental images. The method further includes storing the sample images and the supplemental images in a second database, classifying each sample image and each supplemental image, capturing at least one image of the component using a camera, and identifying at least one feature of the component in the at least one image of the component using the classification module.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate proximity detection and location tracking. An example method includes receiving messages collected by a badge in an environment, the messages including signal strength and a timestamp. The example method also includes assigning a location in the environment to the badge based on a first subset of the messages. The example method also includes identifying an asset in a second subset of the messages. The example method also includes updating a current location associated with the asset based on a relative proximity of the asset to the badge, wherein the current location corresponds to a first time and the updated location corresponds to a second time, and wherein a change in location between the current location and the updated location indicates movement of the asset in the environment.

Abstract: A method of inspecting a component using an image inspection controller that includes a processor communicatively coupled to a memory includes classifying each sample image in a first database as a first sample or a second sample using a classification module, extracting at least one class generic feature from each first sample to generate a plurality of class generic features, and extracting at least one class specific feature from each second sample to generate a plurality of class specific features. The method further includes combining the class generic features and the class specific features to generate a plurality of supplemental images. The method further includes storing the sample images and the supplemental images in a second database, classifying each sample image and each supplemental image, capturing at least one image of the component using a camera, and identifying at least one feature of the component in the at least one image of the component using the classification module.

Abstract: Systems and methods for facilitating proximity detection and location tracking using a hub or bridge are disclosed. An example apparatus is to receive a message from a beacon at a second location within a proximity area of the apparatus, the message indicative of the second location and identifying the beacon; and relay the message to a location server to determine asset location.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate proximity detection and location tracking. An example method includes receiving messages collected by a badge in an environment, the messages including signal strength and a timestamp. The example method also includes assigning a location in the environment to the badge based on a first subset of the messages. The example method also includes identifying an asset in a second subset of the messages. The example method also includes updating a current location associated with the asset based on a relative proximity of the asset to the badge, wherein the current location corresponds to a first time and the updated location corresponds to a second time, and wherein a change in location between the current location and the updated location indicates movement of the asset in the environment.

Abstract: A system for polishing a surface of a component includes a motorized apparatus including a body, a drive system coupled to the body, and an arm including a proximal end coupled to the body and a distal end opposite the proximal end. The motorized apparatus further includes a tool coupled to the distal end of the arm. The tool is configured to polish the surface of the component. The motorized apparatus also includes an actuator coupled to the arm. The system also includes a controller configured to position the tool relative to the component by positioning at least one of the body and the arm relative to the component to reach the target area on the component while maintaining a distance between the tool and the body that is less than a threshold distance. The threshold distance is less than a full reach of the arm and is determined to prevent vibrations of the tool and the arm from exceeding a predefined level.

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.