Abstract
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 sy...
Highlights
More and more robotic arms are applied in industry for improving product quality and reducing product cost
Data-driven multivariate statistical process monitoring (MSPM) methods have been widely used to capture the characteristics of the process for further establishing accurate and reliable monitoring model,[4,5,6,7,8,9,10] such as principal component analysis (PCA) and partial least squares (PLS)
The main disadvantage of profile-based and spatial monitoring methods is that these methods are typically applicable to grayscale images and they cannot be directly used for the monitoring of color images
Summary
More and more robotic arms are applied in industry for improving product quality and reducing product cost. Additional details on the ICA monitoring can be found in related works.[11,13] A multivariate statistical monitoring framework is proposed, in which PCA is first employed to project the optical flow data onto the dominant subspace and a subsequent application of ICA is employed to extract ICs from the retained PCs. The current study directly uses this PCA-ICA method to extract the independent components, and the focus is on establishing an optical flow–based motion fault detection system for monitoring the motion process. The current study directly uses this PCA-ICA method to extract the independent components, and the focus is on establishing an optical flow–based motion fault detection system for monitoring the motion process This is the first time we use unfolding optical flow feature as sample variables of PCA-ICA algorithm for motion process monitoring. Determine R, PR, and calculate the control limit SPE_line of SPE
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