Unsupervised Machine Learning Methods to Estimate a Health Indicator for Condition Monitoring using Acoustic and Vibration Signals: A Comparison Based On a Toy Data Set From a Coffee Vending Machine KU Leuven
Automating the task of assessing an asset’s status based on sensor data would not only relieve trained engineers from this time intensive task, it would also allow a continuous follow-up of assets, potentially resulting in a fine-grained view on the asset’s status. In this work three unsupervised machine learning approaches that define a Health Indicator (HI) based on acoustic and vibration signals were empirically assessed. Such a HI indicates ...