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

摘要

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.