Abstract
Measuring the mechanical load on linear guides provides many possibilities regarding predictive maintenance and process monitoring. In this contribution, we provide an in depth evaluation of a Diamond Like Carbon (DLC) based sensor system integrated into the runner block’s raceway that is capable of directly measuring the load on individual rolling elements. An efficient algorithm based on an Extended Kalman Filter (EKF) for local sensor fusion and load estimation is presented and proven to reliably retrieve the load regardless of the rolling element’s position. Afterwards, we compare locally measured loads to results from a theoretical load distribution model, providing valuable insight into modeling parameters and a verification of the sensor measurement principle. In a final step, an algorithm to invert the load distribution model is derived and used for an evaluation of the sensor system, achieving Root-Mean-Square (RMS) estimation errors of equivalently 1.4 kN in the preload range and 2.75 kN overall for one dimensional loads. Load mode distinction was equally successful with a suppression RMS error of 0.7 kN in the preload range and 2.87 kN in total.
Zusammenfassung
Die Lastbestimmung an Linearführungen ist eine mögliche Grundlage für viele Anwendungen im Bereich der vorrausschauenden Wartung und der Prozessüberwachung. In dieser Arbeit wird ein in die Laufbahnen eines Führungswagens integriertes, DLC basiertes Sensorsystem ausführlich untersucht. Dieses System ermöglicht es, die mechanische Last auf einzelnen Wälzkörpern direkt lokal zu messen. Wir entwarfen einen effizienten Algorithmus basierend auf einem EKF für die lokale Sensordatenfusion und konnten demonstrieren, dass sich damit die Last zuverlässig und unabhängig von der Position des betreffenden Wälzkörpers ermitteln lässt. Im Nachgang wurden die lokal ermittelten Lasten mit einem theoretischen Lastverteilungsmodell abgeglichen. Die hieraus gewonnenen Ergebnisse liefern wertvolle Informationen für die Parametrierung des Modells und dienen gleichzeitig als Verifikation des gesamten Messprinzips. Für die abschließende Bewertung des Sensorsystems wurde ein Algorithmus entworfen, der die Bestimmung der äußeren mechanischen Last durch Invertierung des Lastverteilungsmodells ermöglicht. Dabei wurde ein äquivalenter RMS Fehler von 1,4 kN im Vorspannungsbereich erreicht, im gesammten Lastbereich 2,75 kN für eindimensionale Lasten. Die Unterscheidung von Lastfällen war ebenfalls erfolgreich, mit einem verbleibenden RMS Fehler von 0,7 kN im Vorspannungsbereich und 2,87 kN insgesamt in den nicht vorhandenen Lastrichtungen.
Funding statement: This work did not receive any specific grant.
About the authors
David Krampert finished his M.-Sc. studies of Embedded Systems Engineering in 2017 at the Albert-Ludwigs-University Freiburg. In the time from 2018–2021 he was a PhD student at the Albert-Ludwigs-University Freiburg, researching integrated load measurement techniques and sensor concepts in the area of linear guides at the Bosch Rexroth AG. He is now working as a development engineer with a focus on sensors and instrumentation at the Bosch Rexroth AG.
Sebastian Unsleber studied Nano Technology from 2008 until 2012 and finished his PhD thesis at Julius-Maximilians-University Würzburg in 2017. Afterwards, he worked as Software Developer at Preh GmbH & Co. KG and Project Manager at Bosch Rexroth AG in Schweinfurt, where he now holds a position as Senior Manager for Product Management since 2021.
Leonhard Reindl studied Technical Physics at the TU München and accepted a position in the central research and development department of Siemens AG. Here, he developed micro-acoustic components for mobile communication and radar applications and established the research area of wireless identification and radio sensors. At the same time, he was a PhD student at the TU Wien. After 14 years at Siemens AG, he accepted a position as professor at the Institute for Electrical Information Technology at TU Clausthal where he established a research group for radio technology. Starting 2003 until 2020 he held the chair for Electrical Instrumentation at the Institute for Microsystems Technology IMTEK, where he was focused especially on interdisciplinary research close to industry applications. In October 2020 he retired, but he is still overseeing some research and development projects.
Stefan J. Rupitsch studied Mechatronics and finished his PhD thesis at the Johannes Kepler University Linz in 2008. Afterwards, he held different positions at the Friedrich-Alexander-University Erlangen-Nürnberg at the Chair of Sensor Technology. In 2018, he gained his post-doctorate degree in the area Electrical Instrumentation and Sensorics. Starting from December 2020, he is professor and head of the Laboratory for Electrical Instrumentation and Embedded Systems at the Department of Microsystems Engineering (IMTEK) at the Albert-Ludwigs-University Freiburg.
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