摘要：AbstractThe 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 the highest performance, although other techniques are not far behind. An important conclusion is that methods and ideas from the area of Human Activity Recognition (HAR) are applicable also for vehicles.
关键词：KeywordsMaintenance schedulingproduction planningNeural networks in process controlMeasurementinstrumentation
