Experimental fault detection of input gripping pliers in bottling plants
Abstract
This paper presents a signal-based fault detection scheme for input gripping pliers of the blow molding machine in plastic bottling plants, using accelerometers data. The focus of the diagnosis is on the bearings that support the pliers movements on their mechanical cam. Therationale of the algorithm lies in interpreting the pliers\x92 bearings as the balls in a traditional rolling bearing. Then, strategies inspired by bearing diagnosis are employed and adapted to the specific case of this work. The developed algorithm is validated with experimental tests, following a fault injection step, directly on the real blow molding machine. [Paper, [Code]]
Reference
N. Valceschini, M. Mazzoleni, L. Pitturelli, F. Previdi, "Experimental fault detection of input gripping pliers in bottling plants," in 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) 2022, vol. 55, n.6, pp. 778-783, ISSN: 2405-8963 doi: 10.1016/j.ifacol.2022.07.221 , 2022.
Bibtex
@article{VALCESCHINI2022778,
title = {Experimental fault detection of input gripping pliers in bottling plants},
journal = {IFAC-PapersOnLine},
volume = {55},
number = {6},
pages = {778-783},
year = {2022},
note = {11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022},
issn = {2405-8963},
doi = {10.1016/j.ifacol.2022.07.221},
author = {N. Valceschini and M. Mazzoleni and L. Pitturelli and F. Previdi}
}