Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

Chen, Mingzhi; Shao, Haidong; Dou, Haoxuan; Li, Wei; Liu, Bin

doi:10.1109/tr.2022.3215243

Cited by 104 publications

(31 citation statements)

References 34 publications

(36 reference statements)

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“…As one of the most fundamental parts of rotational machinery, rolling bearings run under harsh operating conditions for a long time, and inevitably fail, causing enormous economic losses with even severe safety accidents. Therefore, it is crucially important to identify rolling bearing faults quickly and accurately [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Wang¹,

Jiang²,

Liu³

et al. 2023

Meas. Sci. Technol.

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Many recent studies have focused on imbalanced rolling bearing data for fault diagnosis. Complementing the imbalance dataset through data augmentation methods excellently solves this problem superior. In this paper, a patch variational autoencoding generative adversarial network (PVAEGAN) is proposed. Firstly, overlap sampling is designed to preprocess the input samples to alleviate noise interference. Secondly, the PVAEGAN is constructed, and the matrix discriminative output of the model allows it to focus on more features of the data during training. Thirdly, a stability-enhancing structure is designed for PVAEGAN to improve the stability of network parameter variations and inter-network stability for better model results. Furthermore, to verify the use of the multi-class comparison method, experiments are conducted. The results indicate that PVAEGAN can augment imbalanced datasets more effectively and with better robustness than other existing models.

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Section: Introductionmentioning

confidence: 99%

Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Wang¹,

Jiang²,

Liu³

et al. 2023

Meas. Sci. Technol.

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“…As the key rotating parts in mechanical equipment, gearboxes are widely used in various complex mechanical equipment (Chen et al, 2022; Wang et al, 2023; Zhang et al, 2023). Gears are prone to wear, fracture, and other failures due to long-term work in complex and terrible environments, cause the machine to malfunction and resulting in incalculable economic losses (Shi et al, 2022; Xiao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

Han

Du³

et al. 2023

Transactions of the Institute of Measurement and Control

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In recent years, transfer learning has been widely used in mechanical fault diagnosis with some achievements. However, most transfer learning methods do not perform well in diagnosis when the speed and load change simultaneously. Inspired by the adversarial learning mechanism, a transfer learning method named attention mechanism-guided domain adversarial network (AMDAN) is proposed in this paper. AMDAN regards the convolutional neural networks (CNNs) as the generator of the domain adversarial network to learn mutually invariant features and the domain classifier as the discriminator of the domain adversarial network. Attention mechanism is introduced to take into account the interchannel and intraspace feature fusion to improve the training efficiency. Then, multi-kernel maximum mean discrepancy (MK-MMD) is used to measure the distance of different feature spaces to achieve domain alignment. Finally, the superiority of AMDAN is verified by two sets of gear fault diagnosis experiments. The experimental results show that AMDAN has the highest classification accuracy and the strongest generalization ability compared with other methods.

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“…The demand for enhancing system reliability and availability is on the rise in the industrial sector. Any sudden failure may lead to costly downtime, damage to surrounding equipment and even danger to personnel [1][2][3][4][5]. During the operation of an induction motor (IM), the stator and rotor windings often fail under the action of heat, electricity and machinery, and the fault rate can reach 30% ∼ 40% of the total number of motor failures.…”

Section: Introductionmentioning

confidence: 99%

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Zhong

Wang

2023

Meas. Sci. Technol.

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To accurately and sensitively track the stator current of the induction motor (IM) and detect faults, stochastic resonance (SR) and Teager energy operator (TEO) are combined to detect the fault in the residual stator current of sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. Firstly, a new approach law is constructed to establish an SMO for better state tracking. Secondly, SR is used to absorb noise and amplify the detection residual of the SMO, and the output results are estimated by TEO in the time domain to achieve fault detection. Finally, the detection results of IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of stator current is processed by SR and TEO, and the effectiveness of fault detection is enhanced.

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Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

Abstract: Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples. IEEE Transactions on Reliability.

Cited by 104 publications

References 34 publications

Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

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