Batch process monitoring in the original measurement's space

Alvarez, Carlos R.; Brandolin, Adriana; Sánchez, Mabel

doi:10.1016/j.jprocont.2010.03.006

Cited by 17 publications

(22 citation statements)

References 31 publications

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“…This enables unrealistically tight control of the process around its temperature and pH set points, greatly facilitating SPM. Many authors already recognized this problem and manually added measurement noise to the simulation data (e.g., [2][3][4]6,14,32,35,37,50,79,87,93]). In addition, Pensim only simulates a limited set of process upsets.…”

Section: Practical Implementationmentioning

confidence: 99%

“…Hu and Yuan [41] generated 250 normal and 4 faulty Pensim batches for SPM by means of Tensor Locality Preserving Projections (TLPP) and also validated his procedure on 16 industrial batches. Alvarez et al [6] used 187 normal and 444 faulty (8 types of faults at different magnitudes) Pensim for batch monitoring in the original measurement space-the largest Pensim dataset encountered by the authors.…”

Section: Introductionmentioning

confidence: 99%

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An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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Close process monitoring (i.e., detection and identification of disturbances) is important to achieve high process efficiency and safety. The Tennessee Eastman process is an extensive benchmark dataset for fault detection and identification, but it is only representative for continuous processes because it does not contain the inherent nonstationarity that complicates monitoring of batch processes. Nevertheless, batch processes also play an important role in many types of industry. This paper therefore presents an extensive reference dataset for benchmarking data-driven methodologies for fault detection and identification in batch processes. The original Pensim model [10] is expanded with sensor noise. By changing the properties of the initial conditions and/or model parameters, four subsets of different complexity are generated, each containing 400 batches with normal operation. To correctly assess the fault detection and identification in batch processes, 15 faults are simulated with various amplitudes and onset times for a total of 22,200 faulty batches for each subset, or 90,400 batches in total. Analysis of the data indicates that the presented types of process faults and their various amplitudes in each of the four subsets present a suitable benchmark for fault detection and identification in batch processes. The dataset is freely available at http://cit.kuleuven.be/biotec/batchbenchmark.

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Section: Practical Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An extensive reference dataset for fault detection and identification in batch processes

Impe¹,

Gins²

2015

Chemometrics and Intelligent Laboratory Systems

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“…The detailed T 2 statistic decomposition process is not shown in here, details can be found in Alvarez et al (2007Alvarez et al ( , 2010.…”

Section: T 2 Statistic Decompositionmentioning

confidence: 99%

Statistics Decomposition and Monitoring in Original Variable Space

Jing

Zhou

Chen

2022

Intelligent Control and Learning Systems

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The traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.

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“…The original measurement's space approach firstly calculates the monitoring statistics of each batch and then estimates the control limits at each time step.…”

Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China.

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Batch process monitoring in the original measurement's space

Cited by 17 publications

References 31 publications

An extensive reference dataset for fault detection and identification in batch processes

An extensive reference dataset for fault detection and identification in batch processes

Statistics Decomposition and Monitoring in Original Variable Space

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

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