Abstract
Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.
Highlights
Modern energy production plants are complex systems, equipped with hundreds of sensors to measure, at relative high frequency, physical parameters, such as pressures, temperatures and flows for operation control and diagnostic purposes
The task of promptly detecting the occurrence of a sensor malfunction, which is often referred to as sensor data validation, is of paramount importance. It has been addressed by a variety of methods including Auto Associative Neural Network (AANN) (Hines et al, 1998), Nonlinear Partial Least Squares Modeling (NLPLS) (Rasmussen et al, 2000), Principal Component Analysis (PCA) (Penha & Hines, 2001) (Baraldi et al, 2011), Auto Associative Kernel Regression (Baraldi et al, 2015) (Garvey et al, 2007), and Multivariate State Estimation Technique (MSET) (Gross et al, 1997) (Coble et al, 2012)
We have developed a novel method for sensor data validation, which combines the use of Continuous Wavelet Transform (CWT) with an image analysis technique
Summary
Modern energy production plants are complex systems, equipped with hundreds of sensors to measure, at relative high frequency, physical parameters, such as pressures, temperatures and flows for operation control and diagnostic purposes. The task of promptly detecting the occurrence of a sensor malfunction, which is often referred to as sensor data validation, is of paramount importance. It has been addressed by a variety of methods including Auto Associative Neural Network (AANN) (Hines et al, 1998), Nonlinear Partial Least Squares Modeling (NLPLS) (Rasmussen et al, 2000), Principal Component Analysis (PCA) (Penha & Hines, 2001) (Baraldi et al, 2011), Auto Associative Kernel Regression (Baraldi et al, 2015) (Garvey et al, 2007), and Multivariate State Estimation Technique (MSET) (Gross et al, 1997) (Coble et al, 2012)
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