Forecasting Corrosion Depth Based on the Maximum Lyapunov Exponent

Abstract

Abstract: Pipeline of oil and gas have an increase risk because of pipeline punctures and rupture caused by corrosion. With the aim to forecast pipeline corrosion, the prediction model of corrosion time series is presented based on the maximum Lyapunov exponent. In addition to presenting technical details of the method, the corrosion data tested by electricity resistance probe No.6 after inlet valve for vacuum tower bottom pump No.28 is used for further forecasting corrosion situation. Finally ,the predicted results shows that prediction has a more high precision, and the error is less than 6%, as well as the average error is 3.58%, also indicated that the prediction ways based on chaos theory is reasonable in the corrosion research, which can supply a scientific basis for pipeline repair and safety management.

Key words: Chaos theory, Corrosion depth, Electricity resistance probe, Forecasting, Lyapunov exponent

CLC Number:

TG174.2

ZHAO Ming;LIANG Ping;LONG Xinfeng. Forecasting Corrosion Depth Based on the Maximum Lyapunov Exponent[J]. , 2008, 44(1): 217-221.

[1]	QIU Shan, GONG Wenjie, ZHANG Zhisheng. Research on Short-term Load Forecasting Model Based on Bagging-combined Kernel Function Relevance Vector Machine [J]. Journal of Electrical Engineering, 2023, 18(2): 142-148.
[2]	SONG Xingrong, XIONG Shangfeng, WANG Deshun, LI Li, HU Anping, TAO Yibin. Control Strategy of Energy Storage Participating in Primary Frequency Regulation with Wind Power Integration [J]. Journal of Electrical Engineering, 2023, 18(2): 260-268.
[3]	Jun Li, Qibo Liu. Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods^* [J]. Chinese Journal of Electrical Engineering, 2023, 9(1): 158-176.
[4]	ZANG Jiyuan, LIU Yufei, WANG Baicun, MIAO Zhongzhen, XUE Yuan, LI Peigen. Technology Forecasting and Roadmapping of Intelligent Manufacturing by 2035 [J]. Journal of Mechanical Engineering, 2022, 58(4): 285-304.
[5]	JIANG Dongliang, LI Tianhao, LIU Wenhao. Short-term Power Load Forecasting Using Similar Day and SAE-DBiLSTM Model [J]. Journal of Electrical Engineering, 2022, 17(4): 240-249.
[6]	LIU Jiangtao, YAN Qiaona, ZHOU Tao, SHAO Lei, CHEN Zhong. Load Forecasting of Metro Power Supply System Based on Deep Recurrent Neural Network [J]. Journal of Electrical Engineering, 2022, 17(4): 309-317.
[7]	GAO Ao, WANG Shuai, HAN Xingchen, ZHANG Zhisheng. Short-term Load Forecasting Model of WGAN Based on GRU Neural Network [J]. Journal of Electrical Engineering, 2022, 17(2): 168-175.
[8]	TANG Xinzi, GU Nengwei, HUANG Xuanqing, PENG Ruitao. Progress on Short Term Wind Power Forecasting Technology [J]. Journal of Mechanical Engineering, 2022, 58(12): 213-236.
[9]	CAI Xinxiang, HAN Aoyang, ZHOU Shengqi, JIAN Xuehui, ZHANG Zhisheng. Short-term Load Forecasting Model Based on Improved Bagging Algorithm and Fuzzy MP-LSTM Fusion [J]. Journal of Electrical Engineering, 2022, 17(1): 164-170.
[10]	DANG Cunlu, YANG Hailan, WU Wencheng. Short-term Load Forecasting Based on LSTM and CatBoost Combined Model [J]. Journal of Electrical Engineering, 2021, 16(3): 62-69.
[11]	ZHAO Wu, HUO Boyi, HUANG Dan. Nonlinear Transverse Vibration Induced by Fluid Disturbance on BTA Deep Hole Precision Reaming System [J]. Journal of Mechanical Engineering, 2020, 56(17): 155-164.
[12]	DAI Rong, YU Haitao, WANG Quan. Weak Resonant Signal Detection of Resonant Microcantilever Sensors Based on Duffing System [J]. Journal of Mechanical Engineering, 2020, 56(13): 50-59.
[13]	DANG Cunlu,WU Wencheng,LI Chaofeng,LI Yongqiang. Short-term Load Forecasting Based on CatBoost Algorithm [J]. Journal of Electrical Engineering, 2020, 15(1): 76-82.
[14]	LI Hengjie,LV Junqing,CHEN Wei,PEI Xiping. Residential Electric Vehicle Load Forecast Based on Valley Time Series Charging [J]. Journal of Electrical Engineering, 2019, 14(1): 95-100.
[15]	WANG Junliang, ZHANG Jie. Big Data Driven Key Factor Identification for Cycle-time Forecasting of Wafer Lots in Semiconductor Wafer Fabrication System [J]. Journal of Mechanical Engineering, 2018, 54(23): 185-191.

Forecasting Corrosion Depth Based on the Maximum Lyapunov Exponent

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments