Integrating independent component analysis and support vector machine for identifying process status changes

Time series data pattern recognition is critical for statistical process control. This paper assumes that observations from the in-control process consist of in-control signals and random noise. The in-control signals switch to different signal types when the process status changes. In these cases, process data monitoring can be formulated as a pattern recognition task. This paper proposes a novel approach using independent component analysis (ICA) and support vector machine (SVM) for time series data pattern recognition. The proposed method applies ICA to the measurement data to generate independent components (ICs). The ICs include important information contained in the original observations. The ICs then serve as the input vectors for the SVM model to identify the time-series data pattern. Extensive simulation studies indicate that the proposed identifiers perform better than using raw data as inputs. © 2014, Chinese Society for Quality. All rights reserved.