Statistical process monitoring based on dissimilarity of process data

Kano, Manabu; Hasebe, Shinji; Hashimoto, Iori; Ohno, Hiromu

doi:10.1002/aic.690480610

Cited by 146 publications

(92 citation statements)

References 20 publications

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“…The conclusion made by such a method may be incorrect if the current faulty condition is not included in the pre-defined scenarios. Research has also proposed different indices of similarity measures for process monitoring and fault diagnosis [20][21][22]. On the other hand, Fisher discriminant analysis (FDA), is widely used for pattern classification [23].…”

Section: Introductionsupporting

confidence: 42%

Multiple sensor fault diagnosis for dynamic processes

Jeng

2010

ISA Transactions

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Section: Introductionsupporting

confidence: 42%

Multiple sensor fault diagnosis for dynamic processes

Jeng

2010

ISA Transactions

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“…In the context of statistical process monitoring, Raich and Cinar [11] apply a similarity index to discriminate among different faults using angles and distances between clusters. Kano et al [65] use a dissimilarity measure to discriminate between the normal and faulty clusters. By using Fisher discriminant analysis, Chiang et al [66] achieve maximum separation between the normal and faulty clusters.…”

Section: Fault Identification and Diagnosismentioning

confidence: 47%

Statistical process monitoring: basics and beyond

Qin

2003

Journal of Chemometrics

1,340

144

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This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction-and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contributionand reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation.

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“…Many fault diagnosis approaches were developed, e.g. discriminant analysis [26], pattern matching using dissimilarity factors [27], contribution plot [28] etc. Of them, contribution plot method was broadly used, as it is free of a prior process knowledge [29].…”

mentioning

confidence: 44%