- Titel
- Implementation of Machine Learning algorithms on PLCnext Technology platform
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-958817
- Konferenz
- 21. AALE-Konferenz. Dresden, 12. März - 14. März 2025
- Quellenangabe
- Tagungsband AALE 2025
; menschzentrierte Automation im digitalen Zeitalter
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2025
Seiten: 339-348
ISBN: 978-3-910103-05-4 - Erstveröffentlichung
- 2025
- DOI
- https://doi.org/10.33968/2025.36
- Abstract (EN)
- In a master’s degree project, the end goal was to build an experiment setup to demonstrate the capabilities of the PLCnext controller introduced by Phoenix Contact in 2018. Various options for the setup design were considered, leading to a setup where the controller performs anomaly detection using machine learning models. A test setup in which anomalies can be introduced has been built. The test stand is a mechanical assembly composed of a crankshaft and sliding block. The crank shaft is driven by a 24V DC motor, and the sliding block is mechanically loaded by an adjustable hydraulic damper. The following anomalies can be introduced in this machine: deviation in the voltage level for the drive motor, increased level of friction in the damper, clearance in the sliding block out of design limits. In operation the slider-crankshaft mechanism causes a time-periodic variation of the motor current. The parameters of this waveform can be analysed for a setup operating in either nominal or abnormal conditions. The anomalies listed above affect the motor torque. Since the torque of a DC motor is directly proportional to its current, each anomaly can be detected from current measurement. All data from the current sensor is stored in working memory of the controller. This measured data is labelled to be transformed into a database. The data obtained when the setup operates without producing errors is labelled as normal data. The data obtained with an anomaly introduced in the system is labelled as anomaly data. A machine learning model can then be trained to label future unknown data as normal data or anomaly data.
- Freie Schlagwörter (EN)
- Anomaly detection, machine learning, PLCnext technology, electromechanical test setup
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-958817
- Veröffentlichungsdatum Qucosa
- 25.02.2025
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0