Succinctly summarizing machine usage via multi-subspace clustering of multi-sensor data

Modern industrial equipments of all kinds are instrumented with a large number of sensors that continuously transmit their readings wirelessly, giving rise to what is often referred to as the `industrial internet'. Such data are often explored by engineers to determine the different usage patterns and behavior of similar machines. In this paper we describe a technique to automatically summarize the usage and behavioral patterns of a collection of similar machines by a small set of rules that nevertheless cover a large fraction of the observed data. We characterize the usage and behavior of a machine over a day, by a collection of single-sensor histograms; thus each day is a point in a high-dimensional space. We first cluster days according to each sensor separately and then combine the clusters using communities in a specially constructed graph that considers common days within clusters of different sensors. In the process some clusters of a single sensor get merged. Finally, we discover rules, each comprising of memberships in clusters of possibly different sensors. Thus, we use the term multi-subspace clustering to describe such a collection of cluster-based rules. Last but not the least, we attempt to cover a large fraction of observed days with a small number of such rules. We present empirical results on voluminous (100s of GBs) real-life sensor data and also compare our technique with related work in subspace clustering and histogram summarization.