Open AccessData Driven Soft Sensor for Condition Monitoring of Sample Handling System (SHS)
Author(s) -
Abhilash Pani,
Jinendra Gugaliya,
Mekapati Srinivas
Publication year - 2020
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2020.101423
Subject(s) - soft sensor , robustness (evolution) , sample (material) , condition monitoring , computer science , wireless sensor network , real time computing , reliability engineering , data mining , process engineering , automotive engineering , engineering , electrical engineering , process (computing) , computer network , biochemistry , chemistry , chromatography , gene , operating system
Gas sample is conditioned using sample handling system (SHS) to remove particulate matter and moisture content before sending it through Continuous Emission Monitoring (CEM) devices. The performance of SHS plays a crucial role in reliable operation of CEMs and therefore, sensor-based condition monitoring systems (CMSs) have been developed for SHSs. As sensor failures impact performance of CMSs, a data driven soft-sensor approach is proposed to improve robustness of CMSs in presence of single sensor failure. The proposed approach uses data of available sensors to estimate true value of a faulty sensor which can be further utilized by CMSs. The proposed approach compares multiple methods and uses support vector regression for development of soft sensors. The paper also considers practical challenges in building those models. Further, the proposed approach is tested on industrial data and the results show that the soft sensor values are in close match with the actual ones.