Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Lv, Zhaomin; Yan, Xuefeng; Jiang, Qingchao; Li, Ning

doi:10.1002/cem.3134

Cited by 15 publications

(8 citation statements)

References 32 publications

(74 reference statements)

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“…For any test vector

, the squared distance to the center

, denoted as

, can be used as a monitoring index for judging if the test vector belongs to an anomaly point. Its expression is given by [ 15 ]:

…”

Section: Svdd Methodsmentioning

confidence: 99%

“…In order to address the multimodal process monitoring issue, Li et al [14] designed a weighted SVDD method according to the local density ratio. For monitoring the batch process with time-varying dynamic characteristic, Lv et al [15] presented one improved SVDD algorithm by integrating the just-in-time learning strategy. Considering the non-Gaussian property of industrial process data, Dong et al [16] designed the independent component analysis (ICA) based improved SVDD method, which performs SVDD modeling on the non-Gaussian components for sensor fault detection.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Deng¹,

Zhang²

2020

Sensors

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As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

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“…For any test vector

, the squared distance to the center

, denoted as

, can be used as a monitoring index for judging if the test vector belongs to an anomaly point. Its expression is given by [ 15 ]:

…”

Section: Svdd Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Deng¹,

Zhang²

2020

Sensors

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“…In recent years, with the rapid development of artificial intelligence technology [9,10], image processing methods based on deep learning have become a research hotspot [11][12][13]. Deep learning networks have achieved important research results in image classification [14,15].…”

Section: Introductionmentioning

confidence: 99%

Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

Chen

et al. 2021

Complexity

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There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.

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“…In recent years, artificial intelligence technology has made great progress [8][9][10][11][12][13][14][15][16][17], and many technologies based on computer vision have been applied into the detection task of railway fasteners [3,[18][19][20][21]. However, if the entire image obtained from the line is detected, not only will it fail to meet the real-time requirements of fastener detection due to the long recognition time but also the complex background in the entire railway line image will inevitably cause the detection accuracy decline.…”

Section: Introductionmentioning

confidence: 99%

Rail Fastener Positioning Based on Double Template Matching

Qiu

Chen

2020

Complexity

Self Cite

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For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

show abstract

Just‐in‐time learning–multiple subspace support vector data description used for non‐Gaussian dynamic batch process monitoring

Cited by 15 publications

References 32 publications

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

Rail Fastener Positioning Based on Double Template Matching

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