Automated Usage Characterization of Mining Vehicles For Life Time Prediction
The life of a vehicle is heavily influenced by how it is used, and usage information is critical to predict the future condition of the machine. In this work we present a method to categorize what task an earthmoving vehicle is performing, based on a data driven model and a single standalone accelerometer. By training a convolutional neural network using a couple of weeks of labeled data, we show that a three axis accelerometer is sufficient to correctly classify between 5 different classes with an accuracy over 96\% for a balanced dataset with no manual feature generation. The results are also compared against some other machine learning techniques, showing that the convolutional neural network has superior performance, although other techniques are not far behind. The methods and ideas are significantly influenced by the area of Human Activity Recognition (HAR) where body worn sensors are used to detect human movement and transportation modes.
Erik Jakobsson, Erik Frisk, Mattias Krysander and Robert Pettersson
2020
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Last updated: 2021-11-10
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