Wavelet-based profile monitoring using order-thresholding recursive CUSUM schemes

With rapid development of advanced sensing technologies, rich and complex real-time profile data are available in many industrial processes such as steelforming manufacturing, chemical manufacturing, semiconductor manufacturing. These profile data provide valuable information about the quality or reliability performance of the process or product, and it is often desirable to utilize them to develop efficient methodologies for process monitoring and fault diagnosing. In this article, we propose a novel wavelet-based profile monitoring procedure that is based on the order-thresholding transformation of recursive CUSUM statistics of multiple wavelet coefficients. Extensive simulation studies and a case study of tonnage profile data demonstrate that our proposed procedure is efficient for detecting the unknown local changes on the profile.