Signal-to-Signal Translation for Fault Diagnosis of Bearings and Gears With Few Fault Samples

Zhao, Baining; Niu, Zhewen; Liang, Qiheng; Xin, Yanli; Qian, Tong Hui; Tang, Wenhu

doi:10.1109/tim.2021.3123433

Cited by 15 publications

(5 citation statements)

References 25 publications

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“…To verify the effectiveness of the proposed method, a comparison between the experimental results and other papers is shown in Table 6 . As can be seen from Table 6 , the accuracy obtained by using the model multi-label recurrent translation adversarial network (MCTAN) [ 35 ] based on the multimodal neural network was about 68%. The accuracy obtained by using the multimodal neural-network-based model [ 36 ] was 97.83%.…”

Section: Experiments and Analysismentioning

confidence: 99%

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Huo

Kang

Wang

et al. 2022

Entropy

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The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

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Section: Experiments and Analysismentioning

confidence: 99%

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Huo

Kang

Wang

et al. 2022

Entropy

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“…In addition, the digital twin technology and transfer learning theory have been applied to fault detection [11,12] and health management [13], to solve the challenges of limited actual fault data and insufficient accuracy of results. It can be seen that these studies mainly focus on mechanical parts with simple structures [14][15][16][17], such as bearings, while there is no relevant research report on complex mechanical equipment. In general, in order to save the costs of collecting fault samples, there are two acceptable approaches: one is the sample generation technique [18][19][20], the other is the transfer learning method [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Yang,

Lin,

Ruan

2024

Meas. Sci. Technol.

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Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network (DSRCNN), is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB's life cycle, and facilitate the application of intelligent fault diagnosis system.

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“…Liu et al 22 proposed a rolling BFD method, in which the matrix profile was adopted to mine impulses from vibration signals, and then convolutional neural network (CNN) was employed to extract the diagnosis features to realize intelligent bearing fault severity identification. Zhao et al 23 proposed a multilabel cycle translating adversarial network based method to solve the problem of insufficient machine fault data, in which the signal‐to‐signal translation was adopted to derive auxiliary fault data, and then deep learning based model was trained with the auxiliary data to realize BFD and gear FD. Huo et al 24 proposed a rolling BFD method, in which the bearing fault features was extracted by the linear superposition network, and then the enhanced transfer learning method was trained with the target domain data.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of power equipment based on variational autoencoder and semi‐supervised learning

Ye,

Li,

Zhang

et al. 2024

Concurrency and Computation

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SummaryThe issue of fault diagnosis in power equipment is receiving increasing attention from scholars. Due to the important role played by bearings in power equipment, bearing faults have become the main factor causing the shutdown of wind turbines units. Therefore, this paper takes bearing equipment as an example for research. In order to solve the problem of insufficient and unbalanced fault sample data of wind turbines bearings, a fault diagnosis (FD) method based on variational autoencoder and semi‐supervised learning is proposed in this paper. Firstly, based on Label Propagation‐random forests (LP‐RFs) and a small number of labeled fault samples, a semi‐supervised learning algorithm is proposed to label the original data samples. Secondly, a small number of training samples are preprocessed by the variational autoencoder to reduce the imbalance of the fault samples. Then, the RFs‐based method is adopted to train the processed fault samples to obtain a mature FD classifier. Finally, the proposed method is applied to FD for bearings, and the results show that the proposed method can realize bearings fault diagnosis (BFD). And meanwhile, the proposed method can also be applied for fault diagnosis in power transmission and transformation systems.

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Signal-to-Signal Translation for Fault Diagnosis of Bearings and Gears With Few Fault Samples

Cited by 15 publications

References 25 publications

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Fault diagnosis of power equipment based on variational autoencoder and semi‐supervised learning

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