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Correlation analysis between the vibroacoustic behavior of steering gear and ball nut assemblies in the automotive industry

Paul Alexandru Bucur (pabucur***at***edu.aau.at)
Klaus Frick (klaus.frick***at***ntb.ch)
Philipp Hungerländer (philipp.hungerlaender***at***aau.at)

Abstract: The increase in quality standards in the automotive industry requires specifications to be propagated across the supply chain, a challenge exacerbated in domains where the quality is subjective. In the daily operations of ThyssenKrupp Presta AG, requirements imposed on the vibroacoustic quality of steering gear need to be passed down to their subcomponents. We quantify the influence of ball nut assemblies on the steering gear by finding optimal encodings of their respective vibroacoustic signals, iteratively maximizing the correlation of their order spectra under orthogonality constraints. We compare the performance of linear and non-linear variants of this approach known as Canonical Correlation Analysis and establish the superiority of the neural network based variant in terms of attainable correlation. The practical relevance of our findings is guaranteed, since the visualization of the weights enables the identification of core influence areas.

Keywords: canonical correlation analysis, neural networks, automotive

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Applications -- Science and Engineering (Statistics )

Category 3: Applications -- Science and Engineering (Mechanical Engineering )

Citation: Technical report, Alpen-Adria Universität Klagenfurt, Mathematics, Optimization Group, TR-AAUK-M-O-28-06-18, 2018.

Download: [PDF]

Entry Submitted: 06/28/2018
Entry Accepted: 06/28/2018
Entry Last Modified: 06/28/2018

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