Quality-related fault detection and diagnosis (QrFDD) is an emerging research subject in the field of multivariate statistical process monitoring and has received great attention from academia and industry in recent years. Compared with traditional multivariate statistical process monitoring methods, QrFDD methods can decompose the process variable space into orthogonal subspaces according to the correlation between input and output so that faults affecting output and faults that do not affect output can be diagnosed in different subspaces. Thanks to this feature, the QrFDD methods have important application values in reducing unnecessary maintenance time and costs, as well as improving production efficiency. Since the decade so far proposed, many outstanding research results have been produced; however, the technical route and implementation algorithm of these achievements are not all the same. In this chapter, we will conduct a technical review and summary of the classical achievements, including their principles, implementation algorithms, technical advantages, and defects. At the same time, we will introduce some of our latest research results and look forward to the future development trend of QrFDD from the perspectives of technology and demand.
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