Elsevier

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Highlights

A specific experimental apparatus was used for simulating sulfurized rust self-heating temperature.

DOFS was used for measurement of simulated sulfurized rust self-heating temperature.

According to the regular pattern of outdoor temperature, the interference data is made. Different algorithms were used for sulfurized rust self-heating anomaly detection and the results were compared.

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.

Keywords

Oil tank

Sulfurized rust

Distributed optical fiber sensor

Auto-encoder

Anomaly temperature change detection

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