Application of neural networks in predictions of brake wear particulate matter emission

Abstract

According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

A Novel Approach for Brake Emission Estimation based on Traffic Microsimulation, Vehicle System Dynamics, and Machine Learning Modeling

“…Their model could present much lower degrees of freedom than standard ANN brake wear models. Furthermore, the potential of the ANN model in PM 10 and PM 2.5 particle prediction emitted by the braking system was investigated by (Vasiljević et al, 2022). All these studies aimed to prove the possibility of using ANN models to estimate the emission rates of the vehicle braking system, and their ndings con rmed the high ability of such AI-based models in brake wear prediction.…”

Optimizing Friction Block Location on Brake Pads for High-Speed Railway Vehicles Using Artificial Neural Networks

“…During the training process, the neural network is presented with input-output examples, also referred to as training data, which it uses to adjust the weights of the nodes and learn underlying patterns in the data via the process of backpropagation, utilizing an algorithm known as gradient descent to minimize the difference between the network's predictions and the actual output. Once the training is completed, the neural network can be utilized to make predictions on new unseen data, passing the input through the network and performing computations at each layer before passing it on to the next, with the final prediction or decision being produced by the output layer [30,31].…”

“…The processed data are then passed through an activation function to obtain the final output from that neuron. The neuron's output is determined using the following equation [31][32][33].…”

“…The sigmoid activation function is differentiable; thus, either is typically used. In this study, the sigmoid function is used and can be defined by the following equation [31].…”