Machine learning models for detecting the causes of conditions of a satellite communication system

A system, comprising:

• one or more computers;
• and
• one or more non-transitory computer-readable media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining, by the one or more computers, one or more feature vectors that respectively correspond to different times, each feature vector including feature values that represent properties of components of a satellite communication system at the time corresponding to the feature vector, the properties including, for one or more of the components, at least one of a status or a metric for the component;
• providing, by the one or more computers, the one or more feature vectors as input to one or more machine learning models, wherein each of the one or more machine learning models are trained to (i) receive at least one feature vector that includes feature values representing properties of the components of the satellite communication system and (ii) output an indication of potential causes of a condition of the satellite communication system based on the properties of the components of the satellite communication system;
• receiving, by the one or more computers and from each of the one or more machine learning models, one or more machine learning model outputs that indicate one or more potential causes of a condition of the satellite communication system based on the properties of the components of the satellite communication system represented by the one or more feature vectors;
• selecting, by the one or more computers from the one or more potential causes and based on the one or more machine learning model outputs received from each of the one or more machine learning models, a particular cause of the condition of the satellite communication system;
• and
• providing, to a device, an indication of the particular cause of the condition of the satellite communication system.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• obtaining, by the one or more computers, one or more feature vectors that respectively correspond to different times, each feature vector including feature values that represent properties of components of a satellite communication system at the time corresponding to the feature vector, the properties including, for one or more of the components, at least one of a status or a metric for the component;
• providing, by the one or more computers, the one or more feature vectors as input to one or more machine learning models, wherein each of the one or more machine learning models are trained to (i) receive at least one feature vector that includes feature values representing properties of the components of the satellite communication system and (ii) output an indication of potential causes of a condition of the satellite communication system based on the properties of the components of the satellite communication system;
• receiving, by the one or more computers and from each of the one or more machine learning models, one or more machine learning model outputs that indicate one or more potential causes of a condition of the satellite communication system based on the properties of the components of the satellite communication system represented by the one or more feature vectors;
• selecting, by the one or more computers from the one or more potential causes and based on the one or more machine learning model outputs received from each of the one or more machine learning models, a particular cause of the condition of the satellite communication system;
• and
• providing, to a device, an indication of the particular cause of the condition of the satellite communication system.