Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information

Han, Song-Yu; Shao, Haidong; Cheng, Junsheng; Yang, Xianyi; Cai, Baoping

doi:10.1109/tmech.2022.3199985

Cited by 52 publications

(9 citation statements)

References 36 publications

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“…With the development of deep learning, many studies have been conducted to utilize the capability of deep learning to extract features directly from data. In the field of bearing fault detection, research utilizing deep learning has been actively conducted [ 40 , 41 , 42 ]. Tand et al studied a method of visualizing data for the analysis of low-speed rotating equipment [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

BiVi-GAN: Bivariate Vibration GAN

Jeong,

Jeung,

Lee

et al. 2024

Sensors

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In the domain of prognosis and health management (PHM) for rotating machinery, the criticality of ensuring equipment reliability cannot be overstated. With developments in artificial intelligence (AI) and deep learning, there have been numerous attempts to use those methodologies in PHM. However, there are challenges to applying them in practice because they require huge amounts of data. This study explores a novel approach to augment vibration data—a primary component in traditional PHM methodologies—using a specialized generative model. Recognizing the limitations of deep learning models, which often fail to capture the intrinsic physical characteristics vital for vibration analysis, we introduce the bivariate vibration generative adversarial networks (BiVi-GAN) model. BiVi-GAN incorporates elements of a physics-informed neural network (PINN), emphasizing the specific vibration characteristics of rotating machinery. We integrate two types of physical information into our model: order analysis and cross-wavelet transform, which are crucial for dissecting the vibration characteristics of such machinery. Experimental findings show the effectiveness of our proposed model. With the incorporation of physics information (PI) input and PI loss, the BiVi-GAN showed a 70% performance improvement in terms of JS divergence compared with the baseline biwavelet-GAN model. This study maintains the potential and efficacy of complementary domain-specific insights with data-driven AI models for more robust and accurate outcomes in PHM.

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Section: Related Workmentioning

confidence: 99%

BiVi-GAN: Bivariate Vibration GAN

Jeong,

Jeung,

Lee

et al. 2024

Sensors

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“…To exploit the merits of both CNN and transformer, Han et al [124] proposed a Convformer-NSE to extract the local and global information from raw vibration signals, in which two convolutional layers were added to process input data and a novel Senet (NSE) [125]was integrated with transformer to make full use of learning of channel and spatial adaptivity. Sun et al [126] designed a multi-stage hierarchical structure via convolutional tokenization for transformer to learn both local and global information from raw signals, and meantime, spatial-reduction attention and linear dimension reduction projections were introduced to transformer to reduce the resource consumption.…”

Section: Transformermentioning

confidence: 99%

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Liu,

Cui,

Cheng

2023

Meas. Sci. Technol.

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Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e., autoencoder (AE), deep Boltzmann machine (DBM), convolutional neural network (CNN), transformer, generative adversarial network (GAN), and graph neural network (GNN), and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.

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“…If the convolution kernel of a CNN is too large, the model's detection effect on local features will be reduced; in contrast, if the convolution kernel is too small, the network cannot obtain global information well [26]. Inspired by the Inception Network and SqueezeNet [27], we designed an MSCNN to extract fault feature information to solve this problem entirely.…”

Section: Design Of the Mscnn-elmmentioning

confidence: 99%

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

Zhang

Huang

et al. 2023

Machines

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Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the collected vibration signals were transformed into images using the continuous wavelet transform. Subsequently, an MSCNN was designed to extract all detailed features of the original images. The final feature maps were obtained by fusing multiple feature layers. The parameters in the network were randomly generated and remained unchanged, which could effectively accelerate the calculation. Finally, an extreme learning machine was used to classify faults based on the fused feature maps, and the potential relationship between the fault and labels was established. The effectiveness of the proposed method was confirmed. This method performs better in mechanical fault diagnosis and classification than existing methods.

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Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information

Cited by 52 publications

References 36 publications

BiVi-GAN: Bivariate Vibration GAN

BiVi-GAN: Bivariate Vibration GAN

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

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