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: A power usage prediction system implements a long short term memory (LSTM) neural network to receive power usage inputs and generate predicted values of power consumption for a plurality of devices. A user provides configuration input regarding the time steps at which the predicted values are to be generated and the various devices for which the predicted values of power consumption are desired. Whenever a power usage input is received, the LSTM neural network outputs the corresponding hidden state values for a plurality of time steps as the predicted values. The hidden state values are each compared to a final cell state value corresponding to a power consumption threshold of the time interval which includes the time steps. Based on the comparison, a power usage condition is recorded. Various actions to mitigate the high power consumption can be implemented in response to recording the power usage condition.

Abstract: A power usage prediction system implements a long short term memory (LSTM) neural network to receive power usage inputs and generate predicted values of power consumption for a plurality of devices. A user provides configuration input regarding the time steps at which the predicted values are to be generated and the various devices for which the predicted values of power consumption are desired. Whenever a power usage input is received, the LSTM neural network outputs the corresponding hidden state values for a plurality of time steps as the predicted values. The hidden state values are each compared to a final cell state value corresponding to a power consumption threshold of the time interval which includes the time steps. Based on the comparison, a power usage condition is recorded. Various actions to mitigate the high power consumption can be implemented in response to recording the power usage condition.

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.