Abstract: A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.

Abstract: An aerial monitoring system and method for identification and location of object features is disclosed. The aerial monitoring system and method includes training an image processing engine to identify predefined object features in images. Training involves the image processing engine generating a model for identifying predefined object features in images. Identifying the predefined object features includes using a drone outfitted with a drone camera to capture and geotag monitoring images of target objects. The monitoring images are both infrared and non-infrared. The image processing engine applies the model to the monitoring images to determine whether the monitoring images include object features that fit within one of multiple categories. The image processing engine uses a fuzzy clustering process to group objects into cluster locations. The image processing system outputs the identification and location of the object features. The output is used for maintenance planning related to the objects.

Abstract: An aerial monitoring system and method for identification and location of object features is disclosed. The aerial monitoring system and method includes training an image processing engine to identify predefined object features in images. Training involves the image processing engine generating a model for identifying predefined object features in images. Identifying the predefined object features includes using a drone outfitted with a drone camera to capture and geotag monitoring images of target objects. The monitoring images are both infrared and non-infrared. The image processing engine applies the model to the monitoring images to determine whether the monitoring images include object features that fit within one of multiple categories. The image processing engine uses a fuzzy clustering process to group objects into cluster locations. The image processing system outputs the identification and location of the object features. The output is used for maintenance planning related to the objects.

Abstract: A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.