Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements

Metrology and Measurement Systems

Junpeng Sun¹  Maolin Cai¹  Yan Shi¹  Zichuan Fan¹  Xiaomeng Tong¹  Weiqing Xu¹ 

关键词: gas flow;    soft sensor;    support vector machine;    temperature time series;   

DOI  :  10.1515/mms-2015-0028

来源: Versita

【 摘 要 】

This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.

【 授权许可】

Unknown