Consistency Regularization Auto-Encoder Network for Semi-Supervised Process Fault Diagnosis

Ma, Yao; Shi, Hongbo; Tan, Shuai; Yang, Tao; Song, Bing

doi:10.1109/tim.2022.3184346

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…The component variable XMV(23−41) is not measurable in real time, and XMV(12) (agitator speed) is constant during simulations. 30 Therefore, the remaining 33 variables are used to build the monitoring model. The TE process contains 21 fault types, in which not every fault is the minor fault.…”

Section: Methodsmentioning

confidence: 99%

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Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

Shi

Tan

et al. 2023

Ind. Eng. Chem. Res.

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In the process of industrial production, the equipment is operating at normal conditions for most of the time, failures are generally rare. Therefore, the real-world data set collected from industrial processes always have skewed distributions; the data in normal conditions are more than that in fault conditions, which result in a class imbalance (CI) problem. Skewed data distributions will affect the ability of feature learning of the monitoring model. To overcome the CI problem, in this paper, a method named double branch rebalanced network (DBRN) is proposed. DBRN is a two-stage method, which learns two decision boundaries by designed convolutional neural network branches. First, in the resampling branch, a novel resampling method is proposed to rebalance the classes from the data-level. Second, a cost adaptive reweighting strategy is developed in the reweighting branch to rebalance the cost of each class. Finally, a fusing learning strategy is designed to fuse these two branches. To describe the proposed method, the experiment-based Tennessee Eastman (TE) data set is constructed with different imbalance ratios. The experiment results show that the proposed DBRN method achieves better performance in CI fault diagnosis.

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Section: Methodsmentioning

confidence: 99%

“…There are totally 41 measured variables and 12 control variables in the TE process. The component variable XMV(23–41) is not measurable in real time, and XMV(12) (agitator speed) is constant during simulations . Therefore, the remaining 33 variables are used to build the monitoring model.…”

Section: Methodsmentioning

confidence: 99%

Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

Shi

Tan

et al. 2023

Ind. Eng. Chem. Res.

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“…Take the L1 norm of the row vector in the weight matrix as the importance of the neurons. Thus, the importance of the jth output neurons is as the formula (6).…”

Section: The Basic Principles For Pruning Different Layersmentioning

confidence: 99%

“…They can complete the end-to-end process for the whole fault diagnosis task, without the need for the manual selection and extraction of the feature vectors. 1,3 According to the different types of the networks adopted in the algorithms, the current intelligent fault diagnosis can be divided into the following categories: the method based on the auto-encoder, [4][5][6] the method based on the convolutional neural network (CNN), [7][8][9] the method based on the ResNet, [10][11][12] the method based on the long short term memory [13][14][15] network, and the method based on the graph neural network. [16][17][18] The current methods mainly focus on how to improve the accuracy and the generalization of the fault diagnosis networks.…”

Section: Introductionmentioning

confidence: 99%

Iterative-AMC: a novel model compression and structure optimization method in mechanical system safety monitoring

Peng

et al. 2023

Structural Health Monitoring

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With the rapid development of artificial intelligence, various fault diagnosis methods based on the deep neural networks have made great advances in mechanical system safety monitoring. To get the high accuracy for the fault diagnosis, researchers tend to adopt the deep network layers and amount of neurons or kernels in each layer. This results in a large redundancy and the structure uncertainty of the fault diagnosis networks. Moreover, it is hard to deploy these networks on the embedded platforms because of the large scales of the network parameters. This brings huge challenges to the practical application of the intelligent diagnosis algorithms. To solve the above problems, an iterative automatic machine compression method, named Iterative-AMC, is proposed in this paper. The proposed method aims to automatically compress and optimize the structure of the large-scale neural networks. Experiments are carried out based on two test benches. With the proposed Iterative-AMC method, the problems of the parameter redundancy and the structure uncertainty can be addressed. The scale of the original network can be greatly compressed, and the compressed fault diagnosis network is successfully deployed on a small-scale FPGA chip.

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“…Wang et al proposed a stacked supervised auto-encoder to obtain deep features for fault classification and applied it to industrial process fault diagnosis [24]. To decrease the difficulty of label acquisition, Ma et al proposed a consistency regularization auto-encoder framework based on an encoder-decoder network to realize fault classification [25]. Huang et al proposed a distributed variational auto-encoder fault detection method.…”

Section: Introductionmentioning

confidence: 99%

Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

et al. 2022

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In the recent years, deep learning has been widely used in process monitoring due to its strong ability to extract features. However, with the increasing layers of the deep network, the compression of features by the deep model will lead to the loss of some valuable information and affect the model’s performance. To solve this problem, a fault detection method based on a discriminant enhanced stacked auto-encoder is proposed. An enhanced stacked auto-encoder network structure is designed, and the original data is added to each hidden layer in the model pre-training process to solve the problem of information loss in the feature extraction process. Then the self-encoding network is combined with spectral regression kernel discriminant analysis. The fault category information is introduced into the features to optimize the features and enhance the discrimination of the extracted features. The Euclidean distance is used for fault detection based on the extracted features. From the Tennessee Eastman process experiment, it can be found that the detection accuracy of this method is about 9.4% higher than that of the traditional stacked auto-encoder method.

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Consistency Regularization Auto-Encoder Network for Semi-Supervised Process Fault Diagnosis

Cited by 19 publications

References 26 publications

Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

Iterative-AMC: a novel model compression and structure optimization method in mechanical system safety monitoring

Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

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