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

Sensors
Caroline König, Ahmed Mohamed Helmi

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 u...Continue Reading

References

Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yoshua BengioPascal Vincent
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jan 1, 2016·Journal of Intelligent Manufacturing·Gregory W VoglMoneer Helu

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Methods Mentioned

BETA
feature extraction

Software Mentioned

iCM
EtherCAT

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