Vol.10, No.1, February 2021.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Diagnosis of Pneumatic Systems on Basis of Time Series and Generalized Feature for Comparison with Standards for Normal Working Condition

Rosen Kosturkov, Veselin Nachev, Tanya Titova

© 2021 Tanya Titova, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 10, Issue 1, Pages 183-191, ISSN 2217-8309, DOI: 10.18421/TEM101-23, February 2021.

Received: 04 December 2020.

Revised:   23 January 2021.
Accepted: 01 February 2021.
Published: 27 February 2021.

Abstract:

Faults are unwanted events in any industrial production system. Early detection and diagnosis of faults in automated systems is important in order to prevent equipment damage, loss of performance and profits. For this purpose, more and more sophisticated and complex systems for observation and monitoring of basic characteristics in automated processes are being built. Preconditions for increasing their efficiency are processing and analysis of process information is obtained through a significant number of sensors. For pneumatic systems in addition to the identification of certain faults that may affect the normal production process, it is important to consider the possibilities to improve their energy efficiency. In this regards, the work focuses on the detection of leaks. The fault detection is based on the measurement of the compressed air consumption at the inlet of the pneumatic module and synchronization with signal from the PLC to the valve, and controlled the pneumatic cylinder. The experimental study aims to develop methods for automatic detection and classification of leaks that may be used in machine learning algorithms.

Keywords: diagnosis, pneumatic systems, leakages detection, time series, feature, metric space, correlations.

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