Estimation of Load-Time Curves Using Recurrent Neural Networks Based On Can Bus Signals

Abstract

. Precise knowledge of the load history of safety-relevant structures is a central aspect within the fatigue strength design of modern vehicles. Since the experimental measurement of load variables is complex and therefore associated with high costs, vehicles require estimation of these variables in order to design even more customer-orientedly in the future and thus consistently pursue sustainable lightweight construction. Hence the data measured by sensors in today’s standard production vehicles is based on vehicle bus system signals which can be permanently retrieved. Due to the increasing availabil-ity of large quantities of recorded vehicle data, machine learning methods are moving into the focus of application. In this work, the implementation of Recurrent Neural Networks for the estimation of load-time curves is investigated. In order to close existing gaps in the state of the art, successful concepts of machine learning for sequential data, such as speech processing, are to be transferred to this application case. Long Short-Term Memory cells [1] play a central role for this type of problem. In addition to the adaptation of the network architecture, the integration of engineering knowledge is pursued within the method development process in order to increase the quality of the model. Relevant input variables are specifically selected by feature engineering and new meaningful variables are generated by filtering. Statistical analysis is used to investigate the correlation of these input signals with the estimated quantities. The development