Authors:

Michael Walsh, Giovanni Abbruzzo, Seamus Hickey, Sonia Ramirez-Garcia, Brendan O’Flynn, Javier Torres Sanchez

Publication Type:

Refereed Conference Meeting Proceeding

Abstract:

Transport systems incorporating linear synchronous motors (LSMs) enable linear motion at high speed for emerging factory automation applications. The goal of this work is to determine the feasibility of harvesting energy directly from an operational LSM transport system employed in high volume manufacturing. Microelectromechanical (MEMs) based sensor technology, deployed as part of a wireless cyber physical system (CPS), perform near real-time magnetic field measurement for a mobile LSM vehicle. The vehicle under study is purposed for mobile factory automation and is not wired for communications nor does it have an onboard power source. A series of experiments were designed and conducted to establish the magnetic profile of the system. Empirical data capture was conducted on a cycled LSM test-bed comprising of 2 shuttles and 2 x 3 meter lengths of LSM track (MagneMotion QuickStick®100). Varying vehicle speeds were incorporated in the experimental regime to determine how changes in velocity would impact the magnetic profile of the vehicle. The recorded magnetic field data was analysed and a relationship between LSM vehicle speed and magnetic field frequency was established. The study highlights the potential to employ a single receiving coil to enable energy recovery which in turn could power a cyber-physical system (CPS) tasked with performing condition based monitoring of the LSM transport vehicles. This in turn can form the basis for the development of a predictive maintenance system, deployed to an LSM based transport layer in high volume manufacturing environments.

Conference Name:

24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2019)

Digital Object Identifer (DOI):

10.1109/ETFA.2019.8868949

Publication Date:

10/09/2019

Pages:

864-869

Conference Location:

Spain

Research Group:

Optimisation & Decision Analytics

Institution:

National University of Ireland, Cork (UCC)

Open access repository:

No