An Approach to Bayesian Multi-Mode Statistical Process Control based on Subspace Selection

Over the last years the need for new monitoring techniques that are capable to cope with high complexity systems and increasing number of sensors has been continuously growing. A special case arises in the monitoring of multi-mode systems, where data gathered from multiple distributed sensors do not represent unequivocally the mode the system is operating in. In such scenarios, the sensors data can represent high-dimensional distribution of severe overlapping clusters. We propose a Statistical Process Control (SPC) framework that aims at dealing with the above-mentioned scenarios. The proposed schema is based on randomly selected subsets of sensors combined with Bayesian decision theory. As a special use-case of multi-mode systems, we apply our framework to data gathered from Metrology devices in the semiconductor industry. The outcome of the monitoring scheme is the identification of a new fault as a new operation mode of the system. We show that the use of combined subsets of sensors along with probabilistic modeling has good potential for the monitoring of such multi-mode systems.