Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

Tan, Lei; Li, Peng; Miao, Aimin; Chen, Yong

doi:10.1177/0020294019838580

Cited by 10 publications

(9 citation statements)

References 45 publications

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“…In order to monitor the processes with multiple operating conditions, the multiple mode NPE method has been developed. [16,17] Tan et al [18] proposed an adaptive neighbourhood preserving embedding (ANPE) algorithm that combined nearly linear dependency conditions with neighbourhood preserving embedding to achieve online fault detection. Zhu et al [19] proposed an advanced fault diagnosis method based on discriminative neighbourhood preserving embedding of Mahalanobis distance (DNPE-M), which addressed the problems of classification accuracy and data overlap in process monitoring.…”

Section: Introductionmentioning

confidence: 99%

Siamese DeNPE network framework for fault detection of batch process

Liu,

Zhao,

Mou

et al. 2023

Can J Chem Eng

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In batch processes, it is crucial to ensure safe production by fault detection. However, the long batch duration, limited runs, and strong nonlinearity of the data pose challenges. Incipient faults with small amplitudes further complicate the detection process. To achieve safe production, motivated by deep learning strategies, we propose a new fault detection method of batch process called Siamese deep neighbourhood preserving embedding network (SDeNPE). First, the DeNPE network is constructed by means of NPE and kernel functions, which utilizes the different types of kernel functions in the kernel mapping layer to extract diverse deep nonlinear features and overcome strong nonlinearity in the process data. Then, the Siamese network is used to obtain the different features between the data and improve the recognition of incipient faults. In addition, the deep extraction and Siamese network allow for batches of training data reduction without diminishing the performance of fault detection. Finally, we utilize monitoring statistics to complete the fault detection process. Two batch process cases involving the penicillin fermentation process and the semiconductor etching process demonstrate the superior fault detection performance of the proposed SDeNPE over the other comparison methods.

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Section: Introductionmentioning

confidence: 99%

Siamese DeNPE network framework for fault detection of batch process

Liu,

Zhao,

Mou

et al. 2023

Can J Chem Eng

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“…The major difference among these methods is located in a way; they integrate the objective function of PCA and LPP. Similarly, Chen et al (2019), Cui et al (2021), Miao et al (2015), and Tan et al (2019) combine PCA with NPE. These new manifold learning methods showed significant improvement in fault detection performance.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

Shah

Ahmed

2022

Transactions of the Institute of Measurement and Control

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Manifold learning is widely adopted for the fault detection of industrial processes. However, the quality of low-dimensional embedding coordinates can be adversely affected by ill-constructed graph Laplacian. An improved locality preserving projection (ILPP) scheme is proposed. ILPP is built on a geometrically inspired Laplacian, and the Riemannian metric is used to find the suitable bandwidth parameter. The proposed approach combines the advantages of ILPP in preserving manifold data structures and those of support vector data description (SVDD) in handling complex process data distributions. Case studies on helix data, hot strip mill, and Pensim benchmark processes demonstrate the utility and feasibility of the proposed approach. The average fault detection rate for proposed ILPP is 99%, which is higher than locality preserving projection (LPP; 87.8%), local tangent space alignment (LTSA; 74.9%), and principal component analysis (PCA; 90.6%).

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“…Tan et al proposed an adaptive NPE algorithm. [ 18 ] This method combines approximately linear correlation conditions with NPE, and realizes online fault detection of the process by adaptively updating the model.…”

Section: Introductionmentioning

confidence: 99%

The chemical process monitoring method based on temporal extended orthogonal neighbourhood preserving embedding (TONPE)

et al. 2022

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Due to the high dimensionality, non‐linearity and dynamic characteristics of chemical process data, a fault monitoring method based on temporal extension orthogonal neighbourhood preserving embedding (TONPE) is proposed. In order to make up for the shortcomings of the orthogonal neighbourhood preserving embedding (ONPE) algorithm, an information extraction strategy based on spatio‐temporal structure is developed. First, a local neighbourhood set with spatio‐temporal characteristics is established, and a weight matrix with spatio‐temporal is reconstructed for each time point through the nearest neighbour in space and time. Then, a projection matrix with orthogonal constraints is obtained to establish a monitoring model. The TONPE algorithm can fully capture the local dynamic changes of high‐dimensional data by extracting two different manifold features, so that the low‐dimensional space has better performance capabilities. The simulation results of the continuous stirred tank reactor process and the Tennessee Eastman process verify the effectiveness of the TONPE algorithm in chemical process monitoring.

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Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

Cited by 10 publications

References 45 publications

Siamese DeNPE network framework for fault detection of batch process

Siamese DeNPE network framework for fault detection of batch process

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

The chemical process monitoring method based on temporal extended orthogonal neighbourhood preserving embedding (TONPE)

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