Abstract
Following the successful application of machine learning methods in order to predict brake squeal as a classification task, this contribution addresses the transfer of those methods on to particle emission data, in order to correctly predict brake particle emissions as a regression task. First results proving the transferability of those methods will be presented.
Deep learning prediction models are generated for particle emission data sets acquired from pin-on-disk experiments. Given the brake system loading sequences, the neural architectures are predicting the amount of PM10 and PM2.5 particle emissions in #/𝑐𝑚3, measured by an Engine Exhaust Particle Sizer (EEPS) Spectrometer and an Optical Particle Sizer (OPS) device. Across the different experiments, 27 different input measurement dimensions are available, while the particle output is measured in #/𝑐𝑚3 in 48 different particle-size bins from 0.3 𝑛𝑚 to 10 μ𝑚. Our study analyses the overall performance of optimal prediction methods, as obtained through hyperparameter studies, and compares whether qualitative differences in the prediction tasks can be read from the respective neural prediction models. Prediction tasks can differ along the time dimension, i.e. by the length of the input and the output sequence, as well as the along the dimension of size resolution of the output, i.e. whether 48 or only 2 particle-size bins are predicted. Applying a greedy backward elimination method the results are utilized to identify key physical parameters, i.e. measurement channels, that are required for accurate predictions and substantial contribution to understanding the system’s particle emissions.
The final objective of this endeavor is to include data-driven NVH and particle emission prediction modules into a next-generation brake control strategy for electric vehicles for emission-reduced and energy efficient braking. The systematic study across different system integration levels is therefore a fundamental building block for integrating machine learning-based intelligence into future brake systems.
Talk in the series “Train Your Engineering Network”.
