Door fault identification

A door fault identification system, comprising:

• at least one processor;
• and
• at least one memory comprising data associated with door faults, an artificial neural network and corresponding weights of the artificial neural network, and a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the door fault identification system to:
• receive sensor data from a plurality of sensors of an electronic lock associated with one or more characteristics of a door;
• analyze the sensor data with the artificial neural network to determine behavior data indicative of a behavior of the electronic lock;
• and
• compare, with the artificial neural network, the behavior data to a plurality of representative data associated with a plurality of door faults to determine a corresponding likelihood that the sensor data corresponds with each of the door faults based on the artificial neural network;
• wherein the plurality of instructions further causes the door fault identification system to:
• receive input indicative of maintenance performed on the door;
• receive new sensor data from the plurality of sensors in response to receipt of the input indicative of the maintenance performed;
• analyze the new sensor data to determine new behavior data indicative of the behavior of the electronic lock;
• compare the new behavior data to the plurality of representative data associated with the plurality of door faults to determine a new corresponding likelihood that the new sensor data corresponds with each of the door faults based on the artificial neural network;
• and
• update the weights of the artificial neural network based on the maintenance performed and the comparison of the new behavior data to the plurality of representative data, wherein each of the weights is associated with a corresponding door fault.

An auto-operator, comprising:

• a plurality of sensors configured to generate sensor data associated with one or more characteristics of a door;
• at least one processor;
• and
• at least one memory comprising data associated with door faults, an artificial neural network and corresponding weights of the artificial neural network, and a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the auto-operator to:
• receive the sensor data from the plurality of sensors;
• analyze the sensor data to determine behavior data indicative of a behavior of the auto-operator;
• and
• compare the behavior data to a plurality of representative data associated with a plurality of door faults to determine a corresponding likelihood that the sensor data corresponds with each of the door faults based on the artificial neural network;
• wherein the plurality of instructions further causes the auto-operator to:
• receive input indicative of maintenance performed on the door;
• receive new sensor data from the plurality of sensors in response to receipt of the input indicative of the maintenance performed;
• analyze the new sensor data to determine new behavior data indicative of the behavior of the auto-operator;
• compare the new behavior data to the plurality of representative data associated with the plurality of door faults to determine a new corresponding likelihood that the new sensor data corresponds with each of the door faults based on the artificial neural network;
• and
• update the weights of the artificial neural network based on the maintenance performed and the comparison of the new behavior data to the plurality of representative data, wherein each of the weights is associated with a corresponding door fault.