Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ted Ooijevaar"'
Publikováno v:
International Journal of Prognostics and Health Management, Vol 10, Iss 2 (2019)
This paper presents a benchmark study in which three vibration based bearing diagnostic algorithms are compared. The three methods are a data driven approach developed by the Linz Center of Mechatronics (LCM), a physics based method of Flanders Make
Externí odkaz:
https://doaj.org/article/58e6d89968254403b6c89270c85b501f
Autor:
Pieter Moens, Vincent Bracke, Colin Soete, Sander Vanden Hautte, Diego Nieves Avendano, Ted Ooijevaar, Steven Devos, Bruno Volckaert, Sofie Van Hoecke
Publikováno v:
Sensors, Vol 20, Iss 15, p 4308 (2020)
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to miss
Externí odkaz:
https://doaj.org/article/476b0fa7aebc4d2b8c8131edad378dd3
Autor:
Rob Salaets, Valentin Sturm, Ted Ooijevaar, Veronika Putz, Julia Mayer, Abdellatif Bey-Temsamani
Publikováno v:
PHM Society European Conference. 7:449-457
Cutting tool wear needs to be monitored closely to ensure good quality of machined parts. However, manual inspection is both expensive and time consuming, therefore there is a need for automated monitoring methods. We present a technique that can rec
Publikováno v:
Journal of Sensors and Sensor Systems, Vol 9, Pp 143-155 (2020)
This paper presents the extension of an empirical study in which a universally applicable fault diagnosis method is used to analyse vibration data of bearings measured with accelerometers. The motivation for extending the previously published results
Publikováno v:
CASE
In today's fast growing vehicle industry, the number of functionalities (comfort features, monitoring features, safety features, etc.) is steadily increasing. Each of these functionalities are developed independently from each other, hence the sensor
Publikováno v:
Procedia CIRP. 85:201-206
Machining large and / or freeform composite parts, such as airplane wings or fuselages, presents challenges in terms of supporting (fixture) and cutting. If the supports are not well placed, the flexible composite parts will deform due to machining f
Autor:
Bruno Volckaert, Steven Devos, S. Van Hoecke, Ted Ooijevaar, Clemens Hesch, Yuan Di, Kurt Pichler
Publikováno v:
IFAC-PapersOnLine. 52:376-381
A key barrier in the industrial adoption of condition monitoring is the lack of large and reliable data sets about the full lifetime of bearings in machines. This data is useful for model training as well as for validation purposes. This paper demons
Autor:
Steven Devos, Vincent Bracke, Sofie Van Hoecke, Pieter Moens, Colin Soete, Ted Ooijevaar, Diego Nieves Avendano, Bruno Volckaert, Sander Vanden Hautte
Publikováno v:
Sensors
Volume 20
Issue 15
SENSORS
Sensors, Vol 20, Iss 4308, p 4308 (2020)
Sensors (Basel, Switzerland)
Volume 20
Issue 15
SENSORS
Sensors, Vol 20, Iss 4308, p 4308 (2020)
Sensors (Basel, Switzerland)
The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to miss
Publikováno v:
Tagungsband.
Publikováno v:
Lecture Notes in Mechanical Engineering ISBN: 9783319957104
A non-continuous condition monitoring approach through periodic vibration measurement has become a common practice in the industry. However, this approach can lead to serious misinterpretation, where rapidly growing faults, that might occur in rollin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a3daa6a4f2b4421fbe510dff5ee0eb8
https://doi.org/10.1007/978-3-319-95711-1_46
https://doi.org/10.1007/978-3-319-95711-1_46