Non-revisiting genetic cost-sensitive sparse autoencoder for imbalanced fault diagnosis

Peng, Peng; Zhang, Wenjia; Zhang, Yi; Wang, Hongwei; Zhang, Heming

doi:10.1016/j.asoc.2021.108138

Cited by 21 publications

(4 citation statements)

References 42 publications

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“…AE has been widely studied as a common unsupervised network [26]. Due to their unique structure, AEs can simply implement feature extraction from data.…”

Section: Stacked Autoencoder (Sae)mentioning

confidence: 99%

A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

Zeng¹,

Li²,

Li³

et al. 2022

Meas. Sci. Technol.

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Aiming at the problem of low diagnostic accuracy of fault diagnosis models due to changes in actual operating conditions, a novel fault diagnosis method based on transfer learning considering nearest neighbor feature constraints (NNFCTL) is proposed. Firstly, nearest neighbor samples are considered to measure data features, as well as a nearest neighbor feature constraint strategy is designed to improve the feature extraction performance of the network. Secondly, a multiple alignment strategy of nearest neighbor samples is proposed to enhance the domain adaptation performance of the network model utilizing multiple alignments. Then, a loss function dynamic weighting strategy is used to improve the convergence of the loss function during model training. Finally, the experimental verification is carried out on the public data set of Western Reserve University and the private data set. The experimental results show that the proposed method exhibits superior transfer performance with reliability and stability compared with the existing methods.

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“…AE has been widely studied as a common unsupervised network [26]. Due to their unique structure, AEs can simply implement feature extraction from data.…”

Section: Stacked Autoencoder (Sae)mentioning

confidence: 99%

A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

Zeng¹,

Li²,

Li³

et al. 2022

Meas. Sci. Technol.

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“…The cost-sensitive method, one of the most popular algorithm-level methods, focuses on the cost of misclassification of different classes, which modify the classifier by assigning different cost weights for different classes. Peng et al propose a nonrevisiting genetic cost-sensitive sparse autoencoder (NrGCS-SAE) which combines a cost-sensitive and sparse autoencoder to solve the imbalanced fault diagnosis. Wu et al proposed a deep adversarial transfer learning model for imbalanced bearing fault diagnosis (deep Imba-DA), which used a cost-sensitive deep classifier to solve the imbalance problem.…”

Section: Introductionmentioning

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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“…However, in a complex system, a combination of multiple independent faults may occur simultaneously at different locations and lead to multi-point failures [ 30 ]. (2) Under the real-world operating conditions of the complex system, the dataset is collected under non-realistic conditions, i.e., noisy and unbalanced data [ 31 , 32 ]. Most of the conducted studies on FDD models development, however, are developed using noise-free experimental data under normal conditions.…”

Section: Introductionmentioning

confidence: 99%

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

Abboush

Knieke

Rausch

2023

Sensors

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Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.

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Non-revisiting genetic cost-sensitive sparse autoencoder for imbalanced fault diagnosis

Cited by 21 publications

References 42 publications

A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

A transfer-learning fault diagnosis method considering nearest neighbor feature constraints

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

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

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