A label machine for mechanical systems: Discovering operating states with unsupervised learning from load time series

Abstract Labeling time series data according to operating states is often a time‐consuming task that requires expert domain knowledge of the underlying mechanical system. In this paper, we propose a data‐driven algorithm that identifies and detects operating states from time series data by grouping time ranges of similar signal behavior together using an unsupervised machine learning approach. The scattering transform and principal component analysis are utilized to extract signal characteristics from time series data which are subsequently clustered by a Gaussian mixture model to generate operating states. To evaluate our approach, we compare the automatically generated operating states with a manual definition of operating states created through expert knowledge. Based on a publicly available eBike dataset, the results demonstrate that the data‐driven definition of operating states can yield similar results to a rule set based on expert knowledge.