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 AR based fault detection and maintenance system analyzes real-time video streams from a remote user device to identify a specific context level at which a user is to handle an equipment and provides instructions corresponding to the specific context level. The instructions enable generating AR simulations that guide the user in executing specific operations including repairs on faulty components of the equipment. The received video stream is initially analyzed to identify a particular equipment which is to be handled by the user. Fault prediction procedures are executed to identify faults associated with the equipment. The instructions to handle the faults are transmitted to the user device as AR simulations that provide step-by-step simulations that enable the user to execute operations as directed by the instructions.

Abstract: An AR based fault detection and maintenance system analyzes real-time video streams from a remote user device to identify a specific context level at which a user is to handle an equipment and provides instructions corresponding to the specific context level. The instructions enable generating AR simulations that guide the user in executing specific operations including repairs on faulty components of the equipment. The received video stream is initially analyzed to identify a particular equipment which is to be handled by the user. Fault prediction procedures are executed to identify faults associated with the equipment. The instructions to handle the faults are transmitted to the user device as AR simulations that provide step-by-step simulations that enable the user to execute operations as directed by the instructions.

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.