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首页> 数字期刊群 >本期导读>基于自回归与长短期记忆网络混合模型的热电偶动态补偿方法研究

基于自回归与长短期记忆网络混合模型的热电偶动态补偿方法研究

325    2023-12-20

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作者:崔志文, 李文军, 虞思思, 金敏俊

作者单位:中国计量大学计量测试工程学院, 浙江 杭州 310018


关键词:动态温度测量;热电偶;动态误差补偿;自回归与长短期记忆网络混合模型


摘要:

热电偶在动态温度测量时由于热惯性存在动态误差。为补偿热电偶的动态误差,提出一种基于自回归与长短期记忆网络混合模型的补偿算法。该算法通过自回归模型对热电偶动态响应进行辨识,再由长短期记忆网络作为非线性补偿器校正动态误差。采用不同强度的高斯白噪声模拟噪声环境,仿真构建热电偶模拟测量数据集。在模拟测量数据集上对算法做验证。计算结果表明,该算法在不同噪声环境下均能有效地减少动态误差。搭建热电偶动态温度测量实验平台,以K型镍铬/镍硅热电偶为实验对象,取得实验测量数据集。实验和计算结果表明,经算法补偿后的热电偶动态响应得到改善,平均动态误差为0.0028,标准差为0.0102。


Research on dynamic compensation method of thermocouples based on autoregressive and long short term memory network hybrid model
CUI Zhiwen, LI Wenjun, YU Sisi, JIN Minjun
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
Abstract: Thermocouples have dynamic error in dynamic temperature measurement due to thermal inertia. In order to compensate the dynamic error of thermocouples, a compensation algorithm based on autoregressive and long short term memory network hybrid model is proposed. The dynamic responses of thermocouples were identified by autoregressive model, and then the long short term memory network was used as nonlinear compensator to correct the dynamic error. Gaussian white noise with different intensity was used to simulate noise environments, and the analog measurement dataset of thermocouples was constructed. The algorithm was verified on the analog measurement dataset. The calculation results show that the algorithm can effectively reduce the dynamic error under different noise environments. The dynamic temperature measurement experimental platform was assembled. Taking K-type Ni–Cr/Ni–Si thermocouples as the experimental object, the experimental measurement dataset was obtained. The experimental and calculation results show that the dynamic responses of the thermocouples compensated by the algorithm were improved, with an average dynamic error of 0.0028 and the standard deviation of 0.010 2.
Keywords: dynamic temperature measurement;thermocouple;dynamic error compensation;autoregressive and long short term memory network hybrid model
2023, 49(9):63-72  收稿日期: 2021-11-09;收到修改稿日期: 2021-12-03
基金项目:
作者简介: 崔志文(1997-),男,浙江杭州市人,硕士研究生,专业方向为热工参数检测和仪器
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