Acoustic Material Monitoring in Harsh Steelplant Environments

This paper provides novel insights into the robustness of machine learning and signal-processing-based acoustic material classification for material transport in modern iron- and steelmaking. The proposed method is designed to deal with the specific harsh and challenging environmental conditions encountered in steel plants. Robust classification depends on the dataset and its contamination with noise. The present work investigates the application of noise detection together with classification algorithms and shows the impact on classification performance. Four contributions are addressed: (i) an evaluation of an outlier detection method for time series, which is based on the short-term enhanced root mean square value RMS (RMSe), (ii) a comparison of different artificial neural network (ANN) structures applied for acoustic classification of material classes, (iii) results on the test dataset splits and (iv) evaluation of the robustness of proposed convolutional neural network (CNN) architecture against environmental disturbances such as the adversarial dropping sound of contaminants. With the combination of preprocessing and CNN on a material transport process dataset, we show an improvement of the overall classification accuracy. It proves the significance of preprocessing a contaminated dataset and the applicability of CNN for real-world acoustic sensoring systems.