Multiscale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation

Alawi, Ahmed; Zhang, Jie; Morris, Julian

doi:10.1021/op400337x

Cited by 13 publications

(9 citation statements)

References 58 publications

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“…14 reports the correct detection rates for the gradual DO sensor drift (fault 6), i.e., the percentage of sample points after the onset of the fault that are detected as such by MWPCA. Each block of 200 batches 2 The "two strains" case includes 25 repetitions of each strain.…”

Section: Illustrative Monitoring Resultsmentioning

confidence: 99%

“…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%

“…Recently, Multi-Scale PCA (MSPCA) for batch processes was proposed by Alawi et al [2] and tested on 40 normal and 3 faulty Pensim batches.…”

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: Illustrative Monitoring Resultsmentioning

confidence: 99%

Section: Practical Implementationmentioning

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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show abstract

“…A theoretical analysis of the properties underlying multivariate and multiscale statistical process control (MSSPC) can be found in [55]. Several other works report improvements or modifications made to the original base formulation [56][57][58][59][60] and a variety of applications of multiscale methods to process monitoring have been reported since then [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78], including for the more complex case of batch processes [79,80]. More recently, image-based monitoring methods [81,82] were developed as well as methods dedicated to supervision of slowly evolving degradation phenomena, closely related to prognosis of equipment health and reliability [78,83,84].…”

Section: Multiscale Methods For Process Monitoringmentioning

confidence: 99%

Multiscale and Multi-Granularity Process Analytics: A Review

Reis

2019

Processes

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As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.

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“…Block segmentation is an important step in the design of a distributed alarm system. − Process knowledge can be used to separate one system into blocks. In each subblock, latent variable space was built for independent alarm analysis .…”

Section: Introductionmentioning

confidence: 99%

Online Distributed Process Monitoring and Alarm Analysis Using Novel Canonical Variate Analysis with Multicorrelation Blocks and Enhanced Contribution Plot

Zhao

Zhu

et al. 2020

Ind. Eng. Chem. Res.

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Process alarm systems and process monitoring play a significant role in ensuring stability and safety in industrial production. The continual increase in the complexity and scale of industrial processes makes it difficult to monitor processes online and accurately track the root causes of alarms. To address these problems, online distributed process monitoring and alarm analysis using novel canonical variate analysis (CVA) with multicorrelation blocks (MCB-CVA) and enhanced contribution plot is developed in this paper. The proposed methodology is simple: first, process knowledge is used to divide the entire system into multicorrelation blocks to enhance fault detection performance; an improved contribution plot method based on principal component analysis is adopted as an alarm analysis strategy such that the distributed contribution can be used for online analysis; finally, a model of distributed alarm analysis is developed using the novel MCB-CVA and enhanced contribution plot. The performance of the proposed methodology is tested using case studies based on the Tennessee Eastman process (TEP). The simulation results show that the proposed MCB-CVA can achieve good online performance in not only fault detection but also root cause analysis, proving the practicability and effectiveness of the proposed MCB-CVA model.

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Multiscale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation

Cited by 13 publications

References 58 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

Multiscale and Multi-Granularity Process Analytics: A Review

Online Distributed Process Monitoring and Alarm Analysis Using Novel Canonical Variate Analysis with Multicorrelation Blocks and Enhanced Contribution Plot

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