Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes

Zheng, Ying; Wang, Yang; Yan, Haolan; Wang, Yan; Yang, Weidong; Tao, Bo

doi:10.1002/cjce.23778

Cited by 18 publications

(6 citation statements)

References 34 publications

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“…. , s T N ] T ∈ R N×M is obtained according to equation (2), and it is normalized as S. In the procedure of SVDD modeling, S is firstly mapped from the original space to a higher feature space by a nonlinear transformation function Φ(s i ). For conventional SVDD, all the training data has the same impact on the model construction, which makes it insensible to outliers and data density.…”

Section: Mlrdr Weighted Svddmentioning

confidence: 99%

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SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

et al. 2021

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Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).

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Section: Mlrdr Weighted Svddmentioning

confidence: 99%

“…e operation conditions of industrial processes will inevitably change with diverse customer requirements, setpoints variation, and different intrinsic features, which leads to multiple modes [1][2][3]. Because of the complexity of multimodal process, it is difficult to obtain satisfactory monitoring results.…”

Section: Introductionmentioning

confidence: 99%

SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

et al. 2021

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“…[30] A k-nearest neighbours (kNN)-based DPC method was proposed and applied to multimode process monitoring. [31] Most related DPC methods adopted in the process fault detection are used for condition identification and division. In the perspective of data distribution, due to the unique nature of cluster centres selection and clustering manner, DPC can be regarded as a data distribution information acquisition process.…”

Section: Introductionmentioning

confidence: 99%

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Liu

et al. 2022

Can J Chem Eng

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Recently, stacked autoencoder (SAE)‐based deep learning has been widely employed for industrial process fault detection, which can extract representative low‐dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self‐reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC‐SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC‐SAE can describe the distribution characteristics of process data, and more informative high‐level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T‐squared (T2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC‐SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed‐batch fermentation penicillin (FBFP) process.

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“…Due to various market demands, drifting disturbance and fluctuating system settings, industrial manufacturing processes are inevitably operated under different working conditions, which are called multimode processes [1,2]. In such processes, there are several stable working points with different statistical characteristics, hence their data do not obey Gaussian distributions, which is the premise of the traditional statistical process monitoring schemes [3].…”

Section: Introductionmentioning

confidence: 99%

Sparse representation and modified density peak clustering-based state identification for multimode processes

Wang¹,

Chen²,

Zheng³

et al. 2022

Meas. Sci. Technol.

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Industrial processes with high dimensional data are generally operated with mixed normal/faulty states in different modes which are difficult to be automatically and accurately identified. In this paper, a state identification framework is proposed for multimode processes. First, a key variable selection approach is presented based on sparse representation to eliminate redundant variables. Then, the modified density peak clustering (MDPC) is proposed to identify different states, in which a distance measurement with a time factor is constructed to select out all the possible cluster centers; and the sum of squared error(SSE) based approach is developed to determine the optimal cluster centers automatically. Further, considering that the mode attributes may be mixed with the fault attributes, a two-step “coarse-to-fine identification” strategy is designed to precisely identify the mode and the faults in each mode. Finally, three cases including a numerical simulation, Tennessee Eastman (TE) benchmark process and an actual semiconductor manufacturing process are given to show the feasibility of the proposed method.

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Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes

Cited by 18 publications

References 34 publications

SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Sparse representation and modified density peak clustering-based state identification for multimode processes

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