期刊
SENSORS AND ACTUATORS B-CHEMICAL
卷 321, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2020.128414
关键词
Electrochemical sensor; Machine learning; Grey-box model; On-board diagnostics; Combustion engines; NOx emission; Cross sensitivity
资金
- Natural Sciences Research Council of Canada [2016-04646]
- Canada First Research Excellence Fund through Future Energy Systems
A grey-box machine learning based model of an electrochemical O-2-NOx sensor is developed using the physical understanding of the sensor working principles and a state-of-the-art machine learning technique: support vector machine (SVM). The model is used to predict the sensor response at a wide range of sensor operating conditions in the presence of different concentrations of NOx and ammonia. To prepare a comprehensive training and test data set, the production sensor is first mounted on the exhaust system of a spark ignition, a diesel engine, and then on a fully controlled sensor test rig. The sensor is not modified, rather the sensor working temperature, all of the sensor cell potentials, and the pumping current of the O-2 sensing cell are the model inputs that can be varied while the pumping current of the NOx sensing cell is considered as the model output. A 9-feature low order model (LOM) and a 45-feature high order model (HOM) are developed with linear and Gaussian kernels. The model performance and generalizability are then verified by conducting input-output trend analysis. The LOM with Gaussian kernel and the HOM with linear kernel have shown the highest accuracy and the best response trend prediction.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据