# One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation

arXiv: https://arxiv.org/abs/2403.16153 · HTML: https://mlsanity.com/paper/2403.16153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://mlsanity.com/paper/2302.00861/) (2023)
- [Ti-MAE: Self-Supervised Masked Time Series Autoencoders](https://mlsanity.com/paper/2301.08871/) (2023)
- [Time Series Generation with Masked Autoencoder](https://mlsanity.com/paper/2201.07006/) (2022)
- [MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series](https://mlsanity.com/paper/2205.02100/) (2022)
- [MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting](https://mlsanity.com/paper/2210.02199/) (2022)
- [Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand](https://mlsanity.com/paper/2302.06818/) (2023)
- [Masked Multi-Step Multivariate Time Series Forecasting with Future Information](https://mlsanity.com/paper/2209.14413/) (2022)

## Abstract (arXiv)

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.