Multirate Partial Least Squares for Process Monitoring

Cong, Ya; Ge, Zhiqiang

doi:10.1016/j.ifacol.2015.09.062

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…These techniques provide better performance than the single-variable approach. Among them, PCA [8][9][10][11][12][13][14] and partial least squares 15,16 are the most common techniques.…”

Section: Industrial Process Monitoring Techniquementioning

confidence: 99%

Principal component analysis for process monitoring in distributed system environment

Gao

Tian

Chen

2019

Concurrency and Computation

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In modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee-Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results.

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“…These techniques provide better performance than the single-variable approach. Among them, PCA [8][9][10][11][12][13][14] and partial least squares 15,16 are the most common techniques.…”

Section: Industrial Process Monitoring Techniquementioning

confidence: 99%

Principal component analysis for process monitoring in distributed system environment

Gao

Tian

Chen

2019

Concurrency and Computation

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“…The realization of this method requires some prior knowledge and a lot of parameters estimation. Cong et al proposed the multirate principal component analysis (MRPCA) method and multirate partial least squares (MRPLS) method, which replaced the original incomplete data matrix with the covariance matrix, and the complete covariance matrix was used to model . This method does not require processing of unsampled samples.…”

Section: Introductionmentioning

confidence: 99%

Grouping multi‐rate sampling fault detection method for penicillin fermentation process

2020

Can J Chem Eng

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For the multi-rate sampling problem of the penicillin fermentation process, some traditional methods cannot be solved due to the different lengths of samples. To solve this problem, a grouping multi-rate sampling fault detection method is proposed. The training sample set is divided into multiple groups according to different sampling rates of variables. Each group has the same sample length. We use the principle component analysis and k-nearest neighbour methods to build model for every group. Then the models of all groups are combined to determine the control limit. The PenSim2.0 software is used to generate a sample set of the penicillin fermentation process that contained multiple variables at different sampling rates. The simulation result shows the effectiveness of the proposed method. K E Y W O R D Sfault detection, group modelling, multi-rate sampling process, penicillin fermentation process

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“…In this setting, a common quick fix that has been adopted so far is to throw overboard some data under the assumption that processes are oversampled and nothing meaningful would be lost by discarding a part of it. Sub-sample and multirate schemes fall in this category [5][6][7][8][9][10][11][12][13][14][15][16]. However, an alternative approach is gaining importance: data aggregation.…”

Section: Introductionmentioning

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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Multirate Partial Least Squares for Process Monitoring

Cited by 13 publications

References 15 publications

Principal component analysis for process monitoring in distributed system environment

Principal component analysis for process monitoring in distributed system environment

Grouping multi‐rate sampling fault detection method for penicillin fermentation process

Multiscale and Multi-Granularity Process Analytics: A Review

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