Knowledge discovery from process operational data using PCA and fuzzy clustering

Sebzalli, Y.M.; Wang, X.Z.

doi:10.1016/s0952-1976(01)00032-x

Cited by 139 publications

(77 citation statements)

References 21 publications

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“…[28][29][30] For simplicity, the K-means method is used for model clustering in this article. In the past years, several phase division approaches have been developed, including the expert knowledge-based method, process analysis approaches, automatic recognition methods, optimization and pattern recognition schemes, and so forth, 25 among which the expert knowledge-based method is used here.…”

Section: Operation Mode Clustering and Phase Divisionmentioning

confidence: 99%

Bayesian inference and joint probability analysis for batch process monitoring

Song

2013

AIChE Journal

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A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes, a phase-based Bayesian inference strategy is introduced, which can efficiently combine the information of multiple operation modes together into a single model in each specific phase. Therefore, without any process knowledge, local monitoring results in different operation modes can be automatically integrated. Besides, the information of the operation mode can be obtained through joint probability analysis under the Bayesian monitoring framework. Potential extensions of the proposed method for fault diagnosis and identification are also discussed. A benchmark case study on the penicillin fermentation process is given to evaluate the feasibility and efficiency of the proposed method. It is demonstrated that the monitoring performance and the process comprehension have both been improved.

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Section: Operation Mode Clustering and Phase Divisionmentioning

confidence: 99%

Bayesian inference and joint probability analysis for batch process monitoring

Song

2013

AIChE Journal

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“…Sebzalli and Wang [71] applied principal component analysis and fuzzy c-means clustering to a refinery catalytic process to identify operational spaces and develop operational strategies for the manufacture of desired products and to minimize the loss of product during system changeover. Four operational zones were discovered, with three for product grade and the fourth region giving high probability of producing off-specification product.…”

Section: Clustering In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…This research paper takes a case study of fluid catalytic cracking process used in refinery industry. The authors analyzed the problem by collecting three hundred data from the process site and applying principal component analysis and fuzzy c means clustering algorithm in the datasets [5].…”

Section: Industrial Process Monitoring Fault Detection and Isolationmentioning

confidence: 99%

"Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation"

Jyoti¹,

Singh²

2011

IJCA

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In this paper proposes different conventional and fuzzy based clustering techniques for fault detection and isolation in process plant monitoring. Process plant monitoring is very important aspect to improve productiveness and efficiency of the product and plant. This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data. This paper also discusses the comparison for K means algorithm and fuzzy C means algorithm.

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Knowledge discovery from process operational data using PCA and fuzzy clustering

Cited by 139 publications

References 21 publications

Bayesian inference and joint probability analysis for batch process monitoring

Bayesian inference and joint probability analysis for batch process monitoring

Data mining in manufacturing: a review based on the kind of knowledge

"Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation"

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