A Time Series Transformer based method for the rotating machinery fault diagnosis

Jin, Yuhong; Hou, Lei; Chen, Yushu

doi:10.1016/j.neucom.2022.04.111

Cited by 96 publications

(22 citation statements)

References 45 publications

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“…The attention mechanism now has been adopted in various deep learning architectures, such as CNNs and RNNs. Transformer architecture [ 126 ] abandons all the recurrent and convolutional structures, and only contains multi-head self-attention (MSA), multi-layer perceptron (MLP), and a basic fully connected layer [ 127 ] to capture the long-term dependencies between elements in a sequence without considering their distance, which can consider the global information comprehensively.…”

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

“…Ref. [ 127 ] proposes a time-series transformer which utilizes raw vibration signals for the rotating machinery fault diagnosis, and it tries to capture translation invariance and long-term dependencies with a new time-series tokenizer. Different from [ 127 ], Ref.…”

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

“…[ 127 ] proposes a time-series transformer which utilizes raw vibration signals for the rotating machinery fault diagnosis, and it tries to capture translation invariance and long-term dependencies with a new time-series tokenizer. Different from [ 127 ], Ref. [ 128 ] designs a time-frequency transformer with a fresh tokenizer and encoder module to extract effective abstractions from the time–frequency representation of vibration signals.…”

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

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Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

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Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

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Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

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“…frontiersin.org connection layer to predict the unknown capacity data value by the auto-regressive method (Jin et al, 2022). The decoder uses the attention mechanism to connect with the encoder, and "pays attention" to the most useful part of the input capacity data value before prediction, in which the padding mask part will be input for the mask to avoid gaining future values during training.…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network

Zhao

2022

Front. Energy Res.

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It is imperative to accurately predict the remaining useful life (RUL) of lithium-ion batteries to ensure the reliability and safety of related industries and facilities. In view of the noise sequence embedded in the measured aging data of lithium-ion batteries and the strong nonlinear characteristics of the aging process, this study proposes a method for predicting lithium-ion batteries’ RUL based on the wavelet threshold denoising and transformer model. To specify, firstly, the wavelet threshold denoising method is adopted to preprocess the measured discharging capacity data of lithium-ion batteries to eliminate some noise signals. Second, based on the denoised data, the transformer model output’s full connection layer is applied to replace the decoder layer for establishing the RUL prediction model of lithium-ion batteries. Finally, the discharging capacity of each charging–discharging cycle is predicted iteratively, and then the RUL of lithium-ion batteries can be calculated eventually. Two groups of lithium-ion batteries’ aging data from the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the laboratory at Anqing Normal University (AQNU) are employed to verify the proposed method, individually. The experimental results demonstrate that this method can overcome the impacts of data measurement noise, effectively predict the RUL of lithium-ion batteries, and present a sound generalization ability and high accuracy.

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“…The transformer failure can cause a breakdown of the power system, which makes transformer fault diagnosis a hot research topic. The traditional methods for diagnosing transformer faults include the three-ratio [1] method and the four-ratio [2] method. However, ratio methods often have problems such as incomplete coding, which can lead to diagnosis failures.…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis based on Chaotic Particle Swarm Optimization RBF Neural Network

Ge,

Lei,

Sun

2023

J. Phys.: Conf. Ser.

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This article uses logistic chaotic mapping to improve the particle swarm algorithm parameters and construct the chaotic particle swarm optimization (CPSO) algorithm. Then, the CPSO algorithm is used to optimize the width, weight, and center values of the Radial Basis Function Neural Networks (RBFNN) to improve the RBFNN model used to diagnose transformer fault types. Results show that the CPSO-RBFNN model has a small mean square error and high accuracy in diagnosing transformer faults.

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A Time Series Transformer based method for the rotating machinery fault diagnosis

Cited by 96 publications

References 45 publications

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network

Transformer fault diagnosis based on Chaotic Particle Swarm Optimization RBF Neural Network

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