Industrial condition monitoring with smart sensors using automated feature extraction and selection

Abstract

Smart sensors with internal signal processing and machine learning capabilities are a current trend in sensor development. This paper suggests a set of complementary and automated algorithms for feature extraction and selection to be used with smart sensors. The suggested methods for feature extraction can be applied on smart sensors and are capable of extracting signal characteristics from signal shape, time domain, time-frequency domain, frequency domain and signal distribution. Feature selection subsequently is capable of selecting the most important features for linear and nonlinear fault classification. The paper also highlights the potential of smart sensors in combination with the suggested algorithms that provide both data and further functionality from self-monitoring to condition monitoring in industrial applications. The first example applications are condition monitoring of a complex hydraulic machine where smart signal processing allows classification and quantification of four different fault scenarios. Additionally redundancies in the systems were used for self-monitoring and allowed to detect simulated sensor faults before they become critical for fault classification. The second example application is remaining lifetime prediction of electromechanical cylinders that shows applicability to big data and transparency of the solution by providing detailed information about sensor significance.