One of the major problems faced by the high-tech manufacturing industry is the need for automated and timely detection of anomalies which can lead to failures of the manufacturing equipment. Failures of the high-tech manufacturing equipment have a direct negative impact on the operating margin and consequently profit of the high-tech manufacturing industry. Automated and timely detection of anomalies is a difficult problem, the major challenge being the need to understand the interactions between large amount of machine components. Even very experienced system engineers are not aware of all interactions, especially if those need to be derived from high velocity sensor data. This, in turn, makes it impossible to recognize early warning signals and take action before failure happens.

In this paper we present HUGO -- a system for real-time analysis of component interactions in high-tech manufacturing equipment. HUGO automatically discovers (based on the available sensor data) correlations between machine components and helps engineers analyze them in real-time so as to be able to detect deterioration of the manufacturing equipment conditions in a timely fashion.