Sensor input based anomaly detection

A method comprising:

• receiving sensor input from one or more sensors;
• processing the sensor input, using multiple neural networks, to generate corresponding output values, the multiple neural networks including at least a first neural network trained to identify a first anomaly type and a second neural network trained to identify a second anomaly type that is distinct from the first anomaly type;
• determining, based on first output values of the first neural network, whether a first anomaly detection criterion of the first anomaly type is satisfied, the first anomaly detection criterion based on at least a first threshold number of sequential samples of the sensor input;
• determining, based on second output values of the second neural network, whether a second anomaly detection criterion is satisfied, the second anomaly detection criterion based on a second threshold number of sequential samples of the sensor input;
• and
• generating an anomaly output based on whether at least one of the first anomaly detection criterion or the second anomaly detection criterion is satisfied.

An aircraft comprising:

• an electrical machine;
• one or more sensors coupled to the electrical machine and configured to generate sensor input corresponding to operation of the electrical machine;
• and
• an anomaly detector configured to:
• process the sensor input, using multiple neural networks, to generate corresponding output values, the multiple neural networks including at least a first neural network trained to identify a first anomaly type and a second neural network trained to identify a second anomaly type that is distinct from the first anomaly type;
• determine, based on first output values of the first neural network, whether a first anomaly detection criterion of the first anomaly type is satisfied, the first anomaly detection criterion based on at least a first threshold number of sequential samples of the sensor input;
• determine, second output values of the second neural network, whether a second anomaly detection criterion of the second anomaly type is satisfied, the second anomaly detection criterion based on at least a second threshold number of sequential samples of the sensor input;
• and
• generate an anomaly output based on whether at least one of the first anomaly detection criterion or the second anomaly detection criterion is satisfied.