Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

Abstract

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.