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Article

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

by
Caroline König
1,2,* and
Ahmed Mohamed Helmi
3
1
Department of Computer Science, Universitat Politècnica de Catalunya, UPC BarcelonaTech, 08034 Barcelona, Spain
2
ISA DATA S.L, Molins de Rei, 08750 Barcelona, Spain
3
Department of Computer and Systems Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3307; https://doi.org/10.3390/s20113307
Submission received: 12 May 2020 / Revised: 1 June 2020 / Accepted: 4 June 2020 / Published: 10 June 2020
(This article belongs to the Section Intelligent Sensors)

Abstract

Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors.
Keywords: condition monitoring; hydraulic systems; sensor signals; convolutional neural networks; classification condition monitoring; hydraulic systems; sensor signals; convolutional neural networks; classification
Graphical Abstract

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MDPI and ACS Style

König, C.; Helmi, A.M. Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models. Sensors 2020, 20, 3307. https://doi.org/10.3390/s20113307

AMA Style

König C, Helmi AM. Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models. Sensors. 2020; 20(11):3307. https://doi.org/10.3390/s20113307

Chicago/Turabian Style

König, Caroline, and Ahmed Mohamed Helmi. 2020. "Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models" Sensors 20, no. 11: 3307. https://doi.org/10.3390/s20113307

APA Style

König, C., & Helmi, A. M. (2020). Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models. Sensors, 20(11), 3307. https://doi.org/10.3390/s20113307

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