Topology-inspired neural network autoencoding for electronic system fault detection

Abstract

Systems and methods for fault detection in a sensor network include receiving sensor data from sensors in the sensor network with a communication device. The sensor data is analyze to determine if the sensor data is indicative of a fault with a fault detection model, the fault detection model including; predicting the sensor data with an autoencoder by encoding the sensor data and decoding encoded the sensor data, autoregressively model the sensor data with an autoregressor, combining the modeled sensor data and the predicted sensor data with a combiner to produce reconstructed sensor data, and comparing the reconstructed sensor data to the sensor data with an anomaly evaluator to determine anomalies. An anomaly classification is produced by comparing the anomalies to historical anomalies with an anomaly classifier. Faults in the sensor network are automatically mitigated with a processing device based on the anomaly classification.