System and method of robust and highly generalized faulty sensor data detection for monitoring applications

A system, comprising:

• memory,
• processor circuitry forming at least one processor communicatively coupled to the memory and being arranged to operate by:
• receiving sensor data from a group of sensors including real time location sensors used to perform localization of an object;
• determining temporal dependencies of sensor data from the same sensor, repeated for multiple sensors, from the group of sensors, and spatial dependencies among sensor data from different sensors from the group of sensors;
• generating indicators that indicate whether sensor data associated with individual sensors of the group of sensors is faulty sensor data comprising inputting the spatial and temporal dependencies into a convolutional neural network (CNN);
• restoring the faulty sensor data to non-faulty sensor data and comprising combining a version of the indicators with a version of the sensor data associated with the indicators to generate combined fault data, and inputting the combined fault data into a multi-attention transformer neural network;
• performing a monitoring task comprising using the non-faulty sensor data;
• and
• pre-training the CNN comprising providing the indicators to both a faulty sensor data classifier neural network and a domain adaptive neural network (DA-NN) arranged to increase generalization of the CNN to cross-domains with the same group of sensors but with different environments from domain to domain affecting the same group of sensors.

At least one non-transitory computer-readable medium comprising instructions thereon that when executed by a computing device, cause the computing device to operate by:

• receiving sensor data from a group of sensors including real time location sensors used to perform localization of an object;
• determining temporal dependencies of sensor data from the same sensor, repeated for multiple sensors, from the group of sensors, and spatial dependencies among sensor data from different sensors from the group of sensors;
• generating indicators that indicate whether sensor data associated with individual sensors of the group of sensors is faulty sensor data comprising inputting the spatial and temporal dependencies into a convolutional neural network (CNN);
• restoring the faulty sensor data to non-faulty sensor data and comprising combining a version of the indicators with a version of the sensor data associated with the indicators to generate combined fault data, and inputting the combined fault data into a multi-attention transformer neural network;
• performing a monitoring task comprising using the non-faulty sensor data;
• and
• pre-training the CNN comprising providing the indicators to both a faulty sensor data classifier neural network and a domain adaptive neural network (DA-NN) arranged to increase generalization of the CNN to cross-domains with the same group of sensors but with different environments from domain to domain affecting the same group of sensors.