Machine learning clustering models for determining the condition of a communication system

A system, comprising:

• one or more computers;
• and
• one or more 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, a set of feature vectors for a particular time period, each feature vector including feature values that represent properties of a satellite communication system at a respective time during the particular time period;
• providing, by the one or more computers, each feature vector in the set of feature vectors as input to a machine learning model, the machine learning model being trained to receive a feature vector that includes feature values representing properties of the satellite communication system and assign the feature vector to one of a plurality of clusters of feature vectors based on the properties of the satellite communication system represented by the feature vector, each cluster of the plurality of clusters corresponding to a respective potential operating condition of the satellite communication system;
• receiving, by the one or more computers and from the machine learning model, and, a machine learning model output for each feature vector, the machine learning output for each feature vector indicating a particular cluster to which the feature vector is assigned;
• and
• generating and providing data that indicates, for each of multiple potential operating conditions, a likelihood that the potential operating condition is the actual operating condition based on a quantity of the feature vectors that have been assigned to the cluster corresponding to the potential operating condition during the particular time period.

One or more 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, a set of feature vectors for a particular time period, each feature vector including feature values that represent properties of a satellite communication system at a respective time during the particular time period;
• providing, by the one or more computers, each feature vector in the set of feature vectors as input to a machine learning model, the machine learning model being trained to receive a feature vector that includes feature values representing properties of the satellite communication system and assign the feature vector to one of a plurality of clusters of feature vectors based on the properties of the satellite communication system represented by the feature vector, each cluster of the plurality of clusters corresponding to a respective potential operating condition of the satellite communication system;
• receiving, by the one or more computers and from the machine learning model, and, a machine learning model output for each feature vector, the machine learning output for each feature vector indicating a particular cluster to which the feature vector is assigned;
• and
• generating and providing data that indicates, for each of multiple potential operating conditions, a likelihood that the potential operating condition is the actual operating condition based on a quantity of the feature vectors that have been assigned to the cluster corresponding to the potential operating condition during the particular time period.