Data-Driven Condition Monitoring of a Hydraulic Press Using Supervised Learning and Neural Networks

Faried Makansi () and Katharina Schmitz
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Faried Makansi: RWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), 5074 Aachen, Germany
Katharina Schmitz: RWTH Aachen University, Institute for Fluid Power Drives and Systems (ifas), 5074 Aachen, Germany

Energies, 2022, vol. 15, issue 17, 1-19

Abstract: The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the framework of industrial applications are not established yet. In this contribution, a procedure is presented for the development of data-driven condition monitoring solutions for industrial hydraulics using supervised learning and neural networks. The proposed method involves feature extraction as well as feature selection and is applied on simulated data of a hydraulic press. Different steps of the development process are investigated regarding the design options and their efficacy in fault classification tasks. High classification accuracies could be achieved with the presented approach, whereas different faults are shown to require different configurations of the classification models.

Keywords: condition monitoring; fault detection and diagnosis; industrial hydraulics; hydraulic press; supervised learning; neural networks; feature extraction; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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