Emad Ali

Self-Learning Condition Monitoring for Smart Electrohydraulic Drives


Front	Back

ISBN:

978-3-8440-7383-6

Series:

Fluidmechatronische Systeme
Herausgeber: Dresdner Verein zur Förderung der Fluidtechnik e.V.
Dresden

Keywords:

Fluidmechatronische Systemtechnik; Fluid Power; Hydraulics; Condition Montroing; Mechatronics

Type of publication:

Thesis

Language:

English

Pages:

190 pages

Figures:

100 figures

Weight:

281 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

48,80 € / 61,10 SFr

Published:

May 2020

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Abstract

The thesis deals with the task of monitoring the hydraulic drives conditions in order to detect faults and hence, aims to enhance their robustness and availability.The focus of the work lay on the fluid related problems, and the analysis is performed from the mechatronic point of view.

Methodologies were investigated with emphasis on the economic and practical aspects for in-field applications. A novel approach is suggested based on the unsupervised paradigm of machine learning algorithms.

Finally, the applicability of the approach in early, intermediate and advanced fault situations, is examined.