Data-Driven State Detection for an asset working at heterogenous regimens

Abstract The current trend of industrial digitalization paved the way to Machine Learning applications which are adding value to data coming from the assets. In this context, the case study of a State Detection in an asset characterized by heterogeneous working regimens is proposed, with the aim of automatically recognizing the type of the ongoing production and of identifying its different operating conditions. The activity is executed by exploiting the data available on the asset controller and applying and comparing two different clustering algorithms, namely K-Means and HDBSCAN. The paper describes hence the application case and the adopted approaches, while providing insights on the most preferable choice for any of the two objectives, in order to pave the ground for condition-based maintenance activities.