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.

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

“…However, the working process of the valve core of the hydraulic valve is a reciprocating motion. These VA methods, which have been successfully applied in rotating machinery, will not be suitable for fault diagnosis and a condition monitoring signal analysis of non-rotating machinery, such as the hydraulic valve [17][18][19].…”

Real-Time and Robust Hydraulic System Fault Detection via Edge Computing

“…Several works exist that used the same data sets as in this study. Helwig et al [15,20] convert the time domain data into frequency domain using fast fourier transform, and generate statistical features, such as the slope of the linear fit, median, variance, skewness, the position of the maximum value, and kurtosis [21]. They then calculated features for fault label correlation and selected the n features by ranking or sorting the correlation (CS).…”

Improving the Performance of Gas Sensor Systems with Advanced Data Evaluation, Operation, and Calibration Methods

“…A hybrid of both methods is the extraction of shape-describing features where the best segmentation of the cycle is found through algorithms like adaptive linear approximation (ALA) [147], [148], allowing for automation of the whole process. Instead of shape, features can also describe higher statistical moments, like variance or skewness [149], or other parameters of a cycle segment, even noise [150], as long as the resulting value is reproducible for the same experimental conditions.…”

“…This approach, however, can take a considerable amount of computing time for large feature sets. Automatic feature selection methods like recursive feature elimination support vector machine (RFESVM) for linear separability [151] and ReliefF for non-linear separability [152] have shown good performance on many different datasets [148].…”