An integration method using distributed optical fiber sensor and Auto-Encoder based deep learning for detecting sulfurized rust self-heating of crude oil tanks

Abstract Sulfurized rust is the production of corrosion in crude oil tanks. It will be oxidized and self-heating when contacting with air, and the rise of temperature can cause severe accidents. This paper focuses on the temperature measurement of distributed optical fiber sensor (DOFS) and the research on anomaly detection methods aided by deep learning. An experimental apparatus was set up to simulate the temperature change during sulfurized rust self-heating, then some artificial ambient temperature was added to interference anomaly detection. The DOFS returned normal temperature, artificial ambient temperature and self-heating temperature data for analysis. Furthermore, four Auto-Encoder (AE) based algorithms and several traditional machine learning methods were tested on the collected temperature data for anomaly detection. Test revealed that Convolutional Neural Networks Auto-Encoder (CNN-AE) was successful in detecting the anomaly situations at an accuracy level of 0.98. The study demonstrates that DOFS and deep learning would be a potential solution for detecting anomaly temperature change to prevent self-heating accident caused by sulfurized rust.