尹爱军,朱文浩,戴宗贤,任宏基.模糊推理和深度学习数模融合的卡尔曼滤波腐蚀预测[J].电子测量与仪器学报,2023,37(4):27-34 |
模糊推理和深度学习数模融合的卡尔曼滤波腐蚀预测 |
Kalman filter corrosion prediction based on data and physical model fusion driven using fuzzy reasoning and deep learning |
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DOI: |
中文关键词: 模糊推理 深度学习 卡尔曼滤波 数模融合 管道腐蚀 时序预测 |
英文关键词:fuzzy reasoning deep learning Kalman filter digital analog fusion pipeline corrosion time series prediction |
基金项目:国家自然科学基金(52275518)、国家重点研发计划(2020YFB1709800)项目资助 |
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Author | Institution |
Yin Aijun | 1. College of Mechanical and Vehicle Engineering, Chongqing University,2. Chongqing Institute of Measurement and Quality Inspection,3. Chongqing Electronic Engineering Vocational College |
Zhu Wenhao | 1. College of Mechanical and Vehicle Engineering, Chongqing University,2. Chongqing Institute of Measurement and Quality Inspection,3. Chongqing Electronic Engineering Vocational College |
Dai Zongxian | 1. College of Mechanical and Vehicle Engineering, Chongqing University,2. Chongqing Institute of Measurement and Quality Inspection,3. Chongqing Electronic Engineering Vocational College |
Ren Hongji | 1. College of Mechanical and Vehicle Engineering, Chongqing University,2. Chongqing Institute of Measurement and Quality Inspection,3. Chongqing Electronic Engineering Vocational College |
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中文摘要: |
腐蚀状态的准确预测对油气储运、化工设备安全可靠运行具有重要意义。 因腐蚀过程复杂,影响因素多,导致常规腐蚀
预测方法中先验模型对环境依赖性大,中长期预测效果差。 本文提出一种融合模糊推理和深度学习的数模融合驱动的卡尔曼
滤波腐蚀预测方法。 首先结合腐蚀物理模型和实际监测数据,建立腐蚀速度模糊规则,得到基于现场环境的结合物理模型的修
正腐蚀速度。 同时针对模糊推理结果存在的预测滞后性,考虑腐蚀监测数据的长期规律性,利用深度学习预测腐蚀速度;然后
融合模糊策略和深度学习预测结果,实现基于卡尔曼滤波的数模融合腐蚀预测。 最后利用天然气管道实际腐蚀监测数据,与高
斯过程回归(Gaussian process regression, GPR),粒子群优化灰度模型(particle swarm optimization gray model,PSOGM),模糊推理
(fuzzy reasoning, FR),多层感知机(multilayer perceptron, MLP)和卡尔曼滤波预测方法(Kalman filter, KF)进行了对比验证分
析。 结果表明本文所提方法具有良好的预测效果,对两年内腐蚀状态的相对预测误差在 1% 范围内, 均方根误差为
0. 000 49 mm,平均绝对百分比误差为 0. 34%。 |
英文摘要: |
Accurate prediction of corrosion state is very important for storage and transportation of oil and gas and safe and reliable
operation of chemical equipment. Due to the complex corrosion process and many influencing factors, the prior model in the conventional
corrosion prediction method is highly dependent on the environment and the medium and long-term prediction effect is poor. In this
paper, a digital analog fusion driven Kalman filter corrosion prediction method integrating fuzzy reasoning and deep learning is proposed.
Firstly, based on the long-term corrosion physical model and the actual short-term monitoring data, the fuzzy rules of corrosion velocity
were established to obtain the modified corrosion velocity based on the field environment. At the same time, aiming at the prediction lag
of fuzzy reasoning results and considering the long-term regularity of corrosion monitoring data, deep learning is used to predict the
corrosion rate. Then, the fuzzy strategy and deep learning prediction results are fused to realize the digital analog fusion corrosion
prediction based on Kalman filter. Finally, using the actual corrosion monitoring data of natural gas pipelines, this prediction method is
compared with GPR, GM, PSOGM, FR, MLP and Kalman filter. The results show that the proposed method has good prediction effect.
The relative prediction error of corrosion state in two years is within 1%, the root mean square error is 0. 000 49 mm, and the average
absolute percentage error is 0. 34%. |
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