Motion process monitoring using optical flow-based principal component analysis-independent component analysis method

In this article, for the first time, the optical flow and principal component analysis followed independent component analysis are combined for monitoring the motion process of robotic-arm-based system. Two kinds of optical flow, namely dense optical flow and sparse optical flow, extracted from each two successive frames of motion process in forms of video stream are used as the samples of motion-related variables of principal component analysis-independent component analysis algorithm. Relative work illustrates the effectiveness of principal component analysis-independent component analysis method for non-Gaussian process monitoring. The proposed dense optical flow-principal component analysis-independent component analysis and sparse optical flow-principal component analysis-independent component analysis algorithms use three-way array as their data which follows non-Gaussian distribution. Data unfolding, data normalization, and proper definition of the control limit are introduced. Based on dense optical flow-principal component analysis-independent component analysis and sparse optical flow-principal component analysis-independent component analysis algorithms, the corresponding motion process monitoring scheme is developed, and a case study of robotic-arm-based marking system is taken to evaluate the performance of these methods. The results demonstrate the capability and efficiency of the proposed methods.