Data Augmentation for Intelligent Mechanical Fault Diagnosis Based on Local Shared Multiple-Generator GAN

Guo, Qingwen; Li, Yibin; Liu, Yanjun; Gao, Shengyao; Yan, Shuicheng

doi:10.1109/jsen.2022.3163658

Cited by 24 publications

(7 citation statements)

References 27 publications

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“…Moreover, the authors designed a new loss function to improve diagnostic accuracy. Guo et al [24] established an enhanced deep feature GAN model to mine the deep features of real signals, effectively solving the problem of rolling bearing imbalanced fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions

Tang

Pang

2023

Meas. Sci. Technol.

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Recently, deep learning models based on convolutional neural networks (CNN) have achieved satisfactory results in rolling bearing fault diagnosis. However, the bearings usually work in variable loading conditions, and their feature distribution could vary with load. The important features cannot be effectively captured in the convolution process using the existing diagnosis models, resulting in poor generalization performance. In this paper, a novel deep learning model, named multiscale cascade recurrent dilation convolution network (MSC-RDCN), is proposed by introducing the dilated convolution and global average pooling layer. Firstly, a new multiscale cascade structure with different convolution kernel sizes is introduced to extract multiscale features contained in the vibration signal. Secondly, a recurrent dilation convolution strategy is designed in each branch of the multiscale cascade structure to extract abundant feature information. Finally, the global average pooling is employed to reduce redundant feature vectors and output them, while a classifier of multilayer perceptron (MLP) is used to automatically identify the fault types. The effectiveness of the proposed algorithm is evaluated by two experimental cases. The results show that the proposed method can successfully identify the labels of fault samples under unknown load conditions using the fault samples with labels under existing load conditions. Compared with other methods, this method exhibits excellent robustness and generalization performance for bearing fault diagnosis under cross-load conditions.

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Section: Introductionmentioning

confidence: 99%

Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions

Tang

Pang

2023

Meas. Sci. Technol.

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“…Recently, as machine-learning techniques have evolved, many intelligent health measurement models have been developed [3][4][5]. Generally, there are two main kinds of models: physics-based and data-driven models [6].…”

Section: Introductionmentioning

confidence: 99%

A working-condition-robust method for two-stage health measurement of rolling bearings based on energy entropy distribution and dynamic adversarial transfer network

Jiang

Xue

Zhang

et al. 2023

Meas. Sci. Technol.

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Accurate and robust health measurement for rolling bearings under variable working conditions has great significance to guarantee the safe and stable operation of rotating machinery. In this paper, a two-stage and working condition-robust health measurement method is proposed, systematically blending energy entropy theory, deep learning approach and transfer learning technology. In the first stage, a state boundary of energy entropy is systematically deduced based on the adaptive variational mode decomposition (VMD) – improved fruit fly optimization algorithm (IFOA) and statistical analysis principle to detect the abnormal states of bearings, where the IFOA is developed for searching the optimal parameters of VMD with high efficiency. In the second stage, if fault exists, a hybrid robust auto-encoder (HRAE) adopting the multi-layer and deep structure is constructed to strengthen the features extraction capacity and automatically capture valuable and robust fault features from original samples. Considering the insufficient labeled samples and significant data distribution discrepancy, a novel dynamic adversarial transfer network (DATN) is designed to extract the transferable and domain-invariant features between source and target datasets and achieve accurate fault identification. Specifically, a dynamic adversarial coefficient based on Wasserstein distance is provided in DATN to quantitatively evaluate the relative importance of marginal and conditional distributions. Extensive experiments on two rolling bearing datasets validate the more superior performance of the proposed method compared with other state-of-the-art identification models and transfer learning approaches.

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“…[ 10 ] Guo et al effectively reduced mode collapse of fault sample generation model by setting multiple GAN generators and sharing weights locally. [ 11 ] Luo et al designed a two‐stage GAN, which first extracts time series information with time‐series GAN, and then generates multimodal samples with auxiliary classifier GAN. Experiments show that the proposed algorithm can detect fault samples more accurately in imbalanced industrial time series.…”

Section: Introductionmentioning

confidence: 99%

A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks

Zou

et al. 2023

Can J Chem Eng

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As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions. The uneven number of samples will lead to the imbalance of datasets and make it a challenging task to assure the overall accuracy. To address the issue, an innovative imbalanced fault diagnostic approach based on area identification conditional generative adversarial networks (AICGAN) is proposed. First, considering the imbalance between normal data (majority data) and fault data (minority data), a hybrid data generation method combining over‐sampling and AICGAN generator is proposed, which effectively extends the limited minority data and overcomes the inclination to majority data to some extent. On one hand, the over‐sampling algorithm reduces the impact of dataset imbalance on the AICGAN training process by linear interpolation. On the other hand, the trainable generator can create samples similar to real samples by learning the generation principle so as to enrich the minority data information and reduce the sample stacking caused by linear synthesis. The two sample production methods complement each other. Combining the raw samples, over‐sampled samples, and samples generated by generator, a new dataset is constructed. Second, the new dataset is used to train the AICGAN discriminator. In addition, in order to generate samples with higher value, an auxiliary discrimination layer is added to the discriminator to control the pattern of generated samples. Third, the balanced dataset containing the linear synthesis samples and the samples generated by the trained generator are put into the classifier to obtain the fault diagnosis. The effectiveness of the proposed approach for fault diagnosis based on AICGAN is verified using the three‐phase flow facility (TFF) dataset and the Tennessee Eastman (TE) dataset. The experimental results demonstrate that the AICGAN‐based fault diagnosis method achieves high F1 scores on the imbalanced dataset.

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Data Augmentation for Intelligent Mechanical Fault Diagnosis Based on Local Shared Multiple-Generator GAN

Cited by 24 publications

References 27 publications

Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions

Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions

A working-condition-robust method for two-stage health measurement of rolling bearings based on energy entropy distribution and dynamic adversarial transfer network

A novel imbalanced fault diagnosis method based on area identification conditional generative adversarial networks

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