Statistical process monitoring using independent component analysis based disturbance separation scheme

Abstract

In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, firstly, ICA is applied to manufacturing process data to find the independent components containing only the white noise of the process. The traditional control chart is then used to monitor the independent components for process monitoring. In order to evaluate the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbances are evaluated. The experimental results reveal that the proposed method outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.