Purpose This research proposes a multi–input multi–output (MIMO) model, based on a data–driven ap proach, to assess passenger vibration comfort on rail vehicles. The MIMO model represents the mathematical relationship be tween input and output variables that contains modal properties and vibration transmission. An optimization algorithm esti mates the frequency response functions at the interface seat– passenger of the trains. Methods The MIMO model evaluated the frequency response functions along x–, y– and z– axis. Multivariate analysis con sidered the calculation of the partial coherence functions, the cross–correlation functions, and the Anderson–Darling non parametric test. Results This research considered the acceleration measure ments on two Trains, one Tramway, and one Underground. This research developed accelerations analyses in the time do main; the transmissibility of accelerations in the frequency do main. The measuring points were on the seat base and the inter face seat–passenger of the trains. The frequency response data, obtained experimentally according to x– y– and z–axis, gener ated a multi–input and multi–output frequency response model. This research is based on experimental investigations and sta tistical tests. The calculation of partial coherence evaluated the percentage of spectrum output due to a specific (conditional) input. The partial coherences assess the energy contribution of each input to the output. The cross–correlation test showed the phase between input and output accelerations. The Anderson– Darling test showed the provenance of the data sample from a population with a specific distribution. Anderson–Darling’s nonparametric test attaches weight to tails. Conclusions The seats of the trains were exposed to complex vibrations according to the three directions x, y, and z. The three inputs are linear accelerations along the vertical, lateral, and forward direction of the seat base. If the accelerations on the seat base represented the inputs to the MIMO model, the ac celerations acquired at the passenger–seat interface represented the output of the MIMO model

Comfort Assessment in Railway Vehicles by an Optimal Identification of Transfer Function

Cavacece Massimo
Writing – Original Draft Preparation
2022

Abstract

Purpose This research proposes a multi–input multi–output (MIMO) model, based on a data–driven ap proach, to assess passenger vibration comfort on rail vehicles. The MIMO model represents the mathematical relationship be tween input and output variables that contains modal properties and vibration transmission. An optimization algorithm esti mates the frequency response functions at the interface seat– passenger of the trains. Methods The MIMO model evaluated the frequency response functions along x–, y– and z– axis. Multivariate analysis con sidered the calculation of the partial coherence functions, the cross–correlation functions, and the Anderson–Darling non parametric test. Results This research considered the acceleration measure ments on two Trains, one Tramway, and one Underground. This research developed accelerations analyses in the time do main; the transmissibility of accelerations in the frequency do main. The measuring points were on the seat base and the inter face seat–passenger of the trains. The frequency response data, obtained experimentally according to x– y– and z–axis, gener ated a multi–input and multi–output frequency response model. This research is based on experimental investigations and sta tistical tests. The calculation of partial coherence evaluated the percentage of spectrum output due to a specific (conditional) input. The partial coherences assess the energy contribution of each input to the output. The cross–correlation test showed the phase between input and output accelerations. The Anderson– Darling test showed the provenance of the data sample from a population with a specific distribution. Anderson–Darling’s nonparametric test attaches weight to tails. Conclusions The seats of the trains were exposed to complex vibrations according to the three directions x, y, and z. The three inputs are linear accelerations along the vertical, lateral, and forward direction of the seat base. If the accelerations on the seat base represented the inputs to the MIMO model, the ac celerations acquired at the passenger–seat interface represented the output of the MIMO model
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/91298
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