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Time Domain Full Vehicle Interior Noise Calculation from Component Level Data by Machine Learning
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on June 3, 2020 by SAE International in United States
Event: 11th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference
Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH) yet. Related works mainly discuss the topic with respect to structural health monitoring, psychoacoustics, traffic noise and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive development. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to improve the prediction process for a steering system. Starting from operational measurements, a procedure to calculate the sound pressure level in the passenger cabin is developed and investigated. Component time domain data serves as basis for the computation. The important inputs are determined by a correlation analysis. Input selection is followed by data reduction. After preprocessing, a supervised learning environment is established. By performing a multivariate regression, the noise transfer between the component coupling and the passenger seat is approximated. Artificial Neural Networks (ANN) are used to fit the function among the parameters. Different ANN architectures are trained by backpropagating errors. Investigations range from single hidden layer Shallow Neural Networks (SNN) to multilayer Deep Neural Networks (DNN). The machine learned system descriptions are then used to predict the full vehicle system response. The work concludes by evaluating the Neural Network performances in comparison with an additionally measured validation dataset.