Abstract: An embodiment establishes a knowledge base based at least in part on sensor data received from a network. The embodiment generates a predicted performance parameter for a designated entity of the network using a first machine learning algorithm. The embodiment compares the predicted performance parameter to an actual performance parameter and determines whether the actual performance parameter exceeds a threshold difference from the predicted performance parameter. The embodiment generates, responsive to determining that the threshold difference is exceeded, incentive data using a second machine learning algorithm, where the incentive data is representative of an action selected by the second machine learning algorithm using an iterative optimization process, and where the iterative optimization process comprises performing the action and determining that the actual performance parameter approaches the threshold value in response to the action.

Abstract: A computer-implemented method, a computer program product, and a computer system for retraining a model in case of a drift in machine learning. A computer detects a drift in machine learning. A computer identifies in a database features and a response of a machine learning model. A computer determines a time window of the drift. A computer extracts, from the database, data of the features and the response in the time window. A computer determines whether extracted data is sufficient for retraining the machine learning model. A computer, in response to determining that the extracted data is not sufficient for retraining the machine learning model, interpolates one or more of the features for a predetermined future time horizon. A computer interpolates a response corresponding to one or more interpolated features. A computer retrains the machine learning model, using the one or more interpolated features and an interpolated response corresponding thereto.

Abstract: Embodiments for providing enhanced edge-based forecasting in a computing environment by a processor. Data from received from one or more data sources may be incorporated into a graph neural network. A forecast of one or more future conditions may be generated based the graph neural network using one or more forecasting models.

Abstract: Embodiments are provided for providing increased efficiency of various industrial systems and processes in a computing system by a processor. One or more anomalies may be monitored and detected for a plurality of processes of an industrial system using a machine learning operation, wherein the one or more anomalies are localized. A diagnosis is generated to address the one or more anomalies.

Abstract: Embodiments for learning semantic description of data based on physical knowledge in a computing environment by a processor. Physical knowledge data and semantic labels associated with data from one or more data sources may be learned. Source attributes of the one or more data sources may be associated with one or more classes and concepts of a plurality of ontologies based on the physical knowledge data and the semantic labels to generate textual descriptors of the data.

Abstract: A processor may collect data from each of two or more stations of a set of stations. The processor may determine a subset of the set of stations that are related. The processor may monitor a residual for a machine learning model for each station in the subset of stations. The processor may detect a change in the operation of a first station of the subset of stations.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to training a learning model based on determined drift. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a selection component that can select an ensemble of deep learning regressors, and an identification component that can identify drift among the ensemble. An analysis component can analyze uncertainty samplings from the ensemble to determine a time instant when drift occurred. A training component can train one or more deep learning models, such as of the deep learning regressors, based upon the identified drift.

Abstract: Embodiments are provided for providing increased performance of various industrial systems and processes in a computing system by a processor. Each of a plurality of dependencies of a plurality of entities in a knowledge graph are modeled as a graph neural network (“GNN”). A reference graph model is generated based on the modeling. One or more anomalies are monitored and detected for a plurality of process based on the reference graph model.

Abstract: An approach for monitoring, detecting and localizing anomalies of HVAC system by using the combination of thermodynamics models, the energy balance of a zone in steady state, and data analytics is disclosed. The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system. The approach retrieves reading from various sensors and insert the current sensor reading into the ideal model. In the presence of anomalies, the parameters of the model will deviate from their nominal values and an appropriate action can be taken based on the severity of the detected and localized anomalies.

Abstract: Embodiments for using learned physical knowledge to guide feature engineering in a computing environment by a processor. Physical knowledge data associated with a dataset may be learned. The physical knowledge data may be translated into a plurality of features for one or more automated feature engineering models to execute for one or more prediction and monitoring operations, wherein the plurality of features represent relationships between the physical knowledge data.

Abstract: A method for additive manufacturing includes identifying a discrepancy between a three-dimensional model and an object model. The three-dimensional model is a model of a three-dimensional object that is being constructed by an additive manufacturing process, and the three-dimensional object is being constructed based on the object model. The method further includes determining a reconfiguration recommendation based on the identified discrepancy. The method further includes reconfiguring the additive manufacturing process based on the reconfiguration recommendation.

Abstract: Provided is a method for monitoring additive manufacturing. The method comprises acquiring an image of a three-dimensional object that is being constructed using an object model. The method further includes isolating a layer of the three-dimensional object from the image. The method further includes generating a three-dimensional model from the layer. The method further includes comparing the three-dimensional model with the object model. The method further includes identifying a discrepancy between the three-dimensional model and the object model. The method further includes providing a notification of the identified discrepancy.

Abstract: An approach for monitoring, detecting and localizing anomalies of HVAC system by using the combination of thermodynamics models, the energy balance of a zone in steady state, and data analytics is disclosed. The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system. The approach retrieves reading from various sensors and insert the current sensor reading into the ideal model. In the presence of anomalies, the parameters of the model will deviate from their nominal values and an appropriate action can be taken based on the severity of the detected and localized anomalies.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining image data, wherein the image data includes a plurality of images; generating visualizations for one or more of the plurality of images included in the image data; obtaining a selection of one of the plurality of images for which a visualization was generated; generating three-dimensional (3D) model data based on the selected one of the plurality of images; and providing the 3D model data of the selected one of the plurality of images for generation of a 3D printable object.

Abstract: Embodiments for localizing abnormal energy consumption at a facility in a cloud computing environment by a processor. One or more residuals for both one or more predictors and a prediction may be generated according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An energy consumption anomaly may be localized according to those of the one or more residuals associated with one or more predictors having an actual energy measurement deviating from a predicted actual energy measurement.

Abstract: Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption may be predicted for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An onset of an energy consumption anomaly may be detected according to the prediction.

Abstract: A first instruction is sent to an integrated smart meter to initialize the integrated smart meter. A second instruction is sent to the integrated smart meter to establish a connection with an energy consuming device. The established connection between the integrated smart meter and the energy consuming device is determined to be acceptable. A third instruction is sent to the integrated smart meter to monitor the energy consuming device. Energy consumption data of the energy consuming device is received from the integrated smart meter.

Abstract: Embodiments for implementing intelligent refrigeration state classification in an Internet of Things (IoT) computing environment by a processor. A signal from a single IoT sensor associated with a refrigeration system may be used to assist in automatically classifying refrigeration states according to a training phase and an operational phase.

Abstract: A social network platform and method thereof for providing Internet of Things (I-o-T) devices with social behavior for communicating natural language (NL) text messages. An I-o-T device is provided with a social device application to form a unit capable of: reading free form NL messages, and responsively perform an action. The social device application generates NL text in response to reading a text message and/or in response to receiving readings from a set of sensors. Types of messages generated include messages for initiating social relationships with other devices which may communicate an acceptance/declination. The platform may be centralized with a server for ranking the importance of read messages based on the relationships and addressing NL text messages to other social units or groups of social units based on the relationships. The platform further enables direct messaging between social unit devices, brokering trust, and moderating information flow between devices.

Abstract: A social network platform and method thereof for providing Internet of Things (I-o-T) devices with social behavior for communicating natural language (NL) text messages. An I-o-T device is provided with a social device application to form a unit capable of: reading free form NL messages, and responsively perform an action. The social device application generates NL text in response to reading a text message and/or in response to receiving readings from a set of sensors. Types of messages generated include messages for initiating social relationships with other devices which may communicate an acceptance/declination. The platform may be centralized with a server for ranking the importance of read messages based on the relationships and addressing NL text messages to other social units or groups of social units based on the relationships. The platform further enables direct messaging between social unit devices, brokering trust, and moderating information flow between devices.