One Masked Model is All You Need for Sensor Fault Detection, Isolation
  and Accommodation
Yiwei Fu Weizhong Yan
Accurate and reliable sensor measurements are critical for ensuring the
safety and longevity of complex engineering systems such as wind turbines. In
this paper, we propose a novel framework for sensor fault detection, isolation,
and accommodation (FDIA) using masked models and self-supervised learning. Our
proposed approach is a general time series modeling approach that can be
applied to any neural network (NN) model capable of sequence modeling, and
captures the complex spatio-temporal relationships among different sensors.
During training, the proposed masked approach creates a random mask, which acts
like a fault, for one or more sensors, making the training and inference task
unified: finding the faulty sensors and correcting them. We validate our
proposed technique on both a public dataset and a real-world dataset from GE
offshore wind turbines, and demonstrate its effectiveness in detecting,
diagnosing and correcting sensor faults. The masked model not only simplifies
the overall FDIA pipeline, but also outperforms existing approaches. Our
proposed technique has the potential to significantly improve the accuracy and
reliability of sensor measurements in complex engineering systems in real-time,
and could be applied to other types of sensors and engineering systems in the
future. We believe that our proposed framework can contribute to the
development of more efficient and effective FDIA techniques for a wide range of
applications.