Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.
deep neural network (DNN)
friction noise
mode-coupling mechanism
squeal instability estimation
squeal sensitivity map
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
05 Jul 2023
05 Jul 2023
Historique:
received:
07
06
2023
revised:
29
06
2023
accepted:
04
07
2023
medline:
17
7
2023
pubmed:
14
7
2023
entrez:
14
7
2023
Statut:
epublish
Résumé
This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree of freedom model was described to analyze the closed solutions generated in the lead screw, and the friction noise sensitivity was examined. The analysis showed that the main source of friction noise in the lead screw was the bending mode pair, and friction-induced instability occurred when the ratio of the stiffness of the bending pair modes was 0.9-1. We also built an architecture to predict multiple outputs from a single model using deep neural networks and demonstrated that friction-induced instability can be predicted by deep neural networks. In particular, instability with nonlinearity was predicted very accurately by deep neural networks with a maximum absolute difference of about 0.035.
Identifiants
pubmed: 37448018
pii: s23136169
doi: 10.3390/s23136169
pmc: PMC10346787
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministry of Trade, Industry and Energy
ID : 20004965
Références
Sensors (Basel). 2020 Jan 22;20(3):
pubmed: 31979141