Open AccessSimulation-Based Data Sampling for Condition Monitoring of Fluid Power Drives
Author(s) -
Faried Makansi,
Katharina Schmitz
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1097/1/012018
Subject(s) - computer science , component (thermodynamics) , limiting , field (mathematics) , data modeling , sampling (signal processing) , hydraulic machinery , power (physics) , data mining , control engineering , engineering , database , mechanical engineering , physics , mathematics , filter (signal processing) , quantum mechanics , pure mathematics , computer vision , thermodynamics
Machine learning techniques are continuously gaining attention and importance in several technical domains. In the field of engineering, they can potentially provide manifold advantages for condition monitoring. However, availability of extensive operation data is a limiting factor. In this contribution, a simulation-based approach is presented, which allows an efficient generation of training data. Based on a lumped parameter simulation, a database of time-series data is generated for a hydraulic reference system. In order to incorporate states of faulty machine operation in the database, means to model component faults in the simulation are assessed. Further, a procedure for an automated training data generation is presented.