Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data

Dong, Xun; Gao, Hongli; Guo, Liang; Li, Kesi; Duan, Andongzhe

doi:10.1109/access.2020.2986419

Cited by 32 publications

(9 citation statements)

References 24 publications

(21 reference statements)

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“…The total In order to illustrate the performance of the proposed DBRN method, the experiments on other methods are conducted under imbalance ratio R im = 10:1. The resampling methods, cost-sensitive reweighting methods, and two-stage methods are taken into consideration, such as ENN, 20 LDAMSS, 32 ADASYN, 19 GOGAN, 31 DCACN, 33 DRS, and DRW. 34 The classifiers of all these methods are based on the CNN network, thus the network structures of the one-stage methods are the same as CNN in Table 1 and that of two-stage methods are the same as DBRN.…”

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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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%

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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“…CNN was also applied for an imbalanced classification problem, as demonstrated in Reference [99], applying the Deep Cost Adaptive CNN configuration to the vibration signal and obtaining a final MAE of 2.5 µm.…”

Section: Machine Condition Monitoringmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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“…Thus, we apply it for the classifier selection of the base classifiers. The ensemble weights of anomalous base classifiers will be set to zero and the ensemble weights of other base classifiers will be reserved, as shown in (15 ≥ −  ′ =  < −  (15) where g t is the G-mean score or ensemble weight of base classifier h t , Q 1 is the lower quartile and I is the distance between the upper and lower quartile. Then, the ensemble weights w t of the base classifiers are normalized after the classifier selection.…”

Section: Base Classifiers Selection and Ensemblementioning

confidence: 99%

“…Zhou et al [14] designed a global optimization generative adversarial network to solve the imbalanced classification problem in the intelligent fault diagnosis. Dong et al [15] proposed deep cost adaptive convolutional network for the imbalanced mechanical data classification. Wang et al [16] used Wasserstein generative adversarial network to generate simulated signals and train stacked autoencoders to classify the mechanical health conditions under imbalanced data.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

Jia

Zuo

et al. 2020

IEEE Access

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Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced data. In the proposed method, a convolutional neural network with the input of multi-sensor signals is used as the base classifier. Firstly, the mechanical imbalance dataset is first split into balanced training subsets through under-sampling strategy, and each subset is used to train a base classifier. Then the weight coefficients of each trained base classifier are calculated by G-mean score and anomalous base classifiers are screened using classifier selection. Finally, the base classifiers are integrated into EnCNN through weighted voting strategy. The proposed EnCNN is validated by the imbalanced dataset collected from a machinery fault test bench. By comparing with the related methods, the superiority of EnCNN is verified in intelligent fault diagnosis of machines under imbalanced data.

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Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data

Cited by 32 publications

References 24 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

Recent Advances on Machine Learning Applications in Machining Processes

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

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