Isolation Enhanced Principal Component Analysis

Gertlert, Janos; Li, Weihua; Huang, Yunbing; McAvor, Thomas

doi:10.1016/s1474-6670(17)37557-2

Cited by 30 publications

(28 citation statements)

References 10 publications

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“…(If it contains rn independent columns, only the trivial solution wI = 0 exists.) By the duality of PCA and AR, all the above arguments can be transferred to partial PCA (Certler et al, 1998). Therefore, in general, no more than rn -1 elements of the x(z) vector can be eliminated from a partial PCA.…”

Section: Partial Pcamentioning

confidence: 99%

“…When a faulty condition is detected, one needs to determine the root cause of this problem. Contribution charts (MacCregor et al, 1994) and multi-block approaches (Chen and McAvoy, 1997) have been proposed to help solve this problem, but neither of them provides a complete solution.Analytical redundancy (AR) methods have well developed fault isolation capabilities utilizing a structured or directional design of the residual set (Certler and Singer, 1990; Massoumnia et al, 1989;Certler, 1998). Parity relations belong to the AR methods.…”

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confidence: 99%

“…The extension to plant faults is straightforward, but needs training data with plant faults (Certler et al, 1998). Multiple faults need more complicated procedures for isolation (Jia et al, 1999;Stork e t al., 1997).…”

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Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

2000

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0 n-line monitoring of chemical processes is extremely important for plant safety and good product quality. Multivariate statistical methods have found many applications in monitoring complex chemical processes (Kresta et al., 1991; Piovoso et al ., 1992). A detailed procedure for multivariate monitoring, where principal component analysis (PCA) is used to model normal process data, is proposed by Kresta et al. (1991). In PCA monitoring, an implicit statistical model is built using data obtained when the process is operating well and under control. Unusual events are detected by referencing the measured process behaviour against the model. The PCA method can compress high dimensional and correlated process measurements into much lower dimensions while keeping important information.Though modern multivariate approaches are very effective a t detecting process faults, one important issue they do not specifically address is fault isolation. When a faulty condition is detected, one needs to determine the root cause of this problem. Contribution charts (MacCregor et al., 1994) and multi-block approaches (Chen and McAvoy, 1997) have been proposed to help solve this problem, but neither of them provides a complete solution.Analytical redundancy (AR) methods have well developed fault isolation capabilities utilizing a structured or directional design of the residual set (Certler and Singer, 1990; Massoumnia et al., 1989;Certler, 1998). Parity relations belong to the AR methods. Certler and McAvoy (1997) showed that there is a close duality between PCA and parity relations, and proposed a partial PCA method embedding the fault isolation properties of the structured parity relations into PCA. Certler et al. (1 998) gave a strict proof of the duality, and proposed a direct algebraic method of generating a set of partial PCA models from the full PCA model. A potential problem for the partial PCA approach i s that PCA is a linear method and most processes are nonlinear.Efforts have been made to introduce nonlinearity into PCA. Cnanadesikan (1 977) proposed a generalized PCA (CPCA) in which the normal data set is extended to include nonlinear functions of its elements, and PCA is performed on the extended data set. Etezadi-Amoli and McDonald (1 983) used I dimensional polynomials to approximate m dimensional data with / < rn latent factors. The coefficients of the polynomials in the reduced I dimensional space are computed by the linear least squares method. Kramer (1 992) proposed a nonlinear PCA (NPCA) method based on a five-layered neural network, but such a network is very difficult to train. The NPCA approach proposed by Dong and McAvoy (1 996) uses a similar neural network structure, but employs the *Author to whom correspondence may be addressed. E-mail address: mcavoy@ glue.umd.edu Partial principal component analysis (PCA) and parity relations are proven to be useful methods in fault isolation. To overcome the limitation of applying partial PCA to nonlinear problems, a new approach utilizing clustering analysis is p...

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Section: Partial Pcamentioning

confidence: 99%

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confidence: 99%

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Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

2000

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“…This can be challenging, however. For example, typical observer‐based methods require observability of a state or signature residual associated with each type of fault to identify its cause . Other observer‐based methods assume that the fault symptoms can be described by linear functions of their magnitude .…”

Section: Introductionmentioning

confidence: 99%

Qualitative path estimation: A fast and reliable algorithm for qualitative trend analysis

Villez

2015

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com)Fault detection and identification is challenged by a lack of detailed understanding of process dynamics under anomalous circumstances as well as a lack of historical data concerning rare events in a typical process. Qualitative trend analysis (QTA) techniques provide a way out by focusing on a coarse-grained representation of time series data. Such qualitative representations are valid in a larger set of operating conditions and thus provide a robust way to handle the detection and identification of rare events. Unfortunately, available methods fail when faced with moderate noise levels or result in rather large computational efforts. For this reason, this article provides a novel method for QTA. This leads to dramatic improvements in computational efficiency compared to the previously established shape constrained splines method while the accuracy remains high. V C 2015 American Institute of Chemical Engineers AIChE J, 61: 1535-1546, 2015The BCBC shape is clearly identified as the most likely over a wide range of kernel half-widths.

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“…There has been tremendous interest in diagnosing the possible root causes of a fault situation. Different methods have been reported for fault diagnosis based on historical measurement data. In the field of multivariate SPM, the method of contribution plots has been widely used for years to isolate the root faulty variables based on the assumption that the out‐of‐control monitoring statistics are contributed significantly by those faulty variables.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

2014

AIChE Journal

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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

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Isolation Enhanced Principal Component Analysis

Cited by 30 publications

References 10 publications

Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

Qualitative path estimation: A fast and reliable algorithm for qualitative trend analysis

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

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