Quality‐relevant fault diagnosis with concurrent phase partition and analysis of relative changes for multiphase batch processes

Abstract

Multiplicity of phases as indicated by changes of process characteristics is an inherent nature of many batch processes for both normal and fault cases. To more efficiently perform online fault diagnosis via reconstruction for multiphase batch processes, the phase nature and the relationship between normal and fault cases within each phase should be deeply addressed. This article proposes a quality‐relevant fault diagnosis strategy with concurrent phase partition and analysis of relative changes for multiphase batch processes. First, a concurrent phase partition algorithm is developed. The basic idea is to track the changes of process characteristics at normal and fault statuses jointly so that multiple sequential modeling phases are identified simultaneously for both normal and fault cases. Then, the relative changes from the normal status to each fault case are analyzed in each phase to reveal the specific fault effects more efficiently. The fault effects are decomposed in two different monitoring subspaces, principal subspace, and residual subspace, by capturing their different roles in removing out‐of‐control signals. The significant increases relative to the normal case are judged to be responsible for the concerned alarm monitoring statistics in each phase. The others are composed of general variations that are deemed to still follow normal rules and thus insignificant to remove alarm monitoring statistics. Those alarm‐responsible fault deviations are then used to develop reconstruction models which can more efficiently recover the fault‐free part for online fault diagnosis. The proposed algorithm is illustrated with a typical multiphase batch process with one normal case and three fault cases. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2048–2062, 2014

A Quality-Related Statistical Process Monitoring Method Based on Global plus Local Projection to Latent Structures

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Global Plus Local Projection to Latent Structures

“…Currently, partial least squares (PLS), which is one of those data-driven methods (Severson et al 2016;Ge et al 2012;Li et al 2010;Zhao 2014;Zhang and Qin 2008), is widely used because of its advantages in extracting the latent variables by establishing the relationship between input and output space for quality-relevant process monitoring (Qin 2010). It maintains the maximum correlation between quality and process variables and has better quality-related fault detection capability.…”

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