Proof of concept: Machine learning based filling level estimation for bulk solid silos

Abstract

Rigid silos are often under pressure and filled with hazardous materials. Therefore, in very few cases their level can be checked visually. For example, the level of mobile bulk solids silos, which are often used on construction sites, is checked by a worker throwing a stone against the silo and uses the sound to estimate the invisible silo cavity. This method is subjective and shows great errors based on experience. The proposed method should implement this principle robustly by machine application. A sensor unit is to give a mechanical impulse to the outer silo wall with a percussion mechanism and record the resulting sound with a microphone. Just as a human being is able to make an experience-based statement about the filling level of the silo over the growing amount of sounds heard, different machine learning approaches are to be considered in order to imitate this procedure. Machine learning approaches usually aim to find patterns between different sizes. A strongly approximated mathematical model will be used to prove that there is a noisy but demonstrable direct correlation between the level and the acoustic impulse response. This should justify future efforts to find suitable machine learning methods for this application.