Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

Yan, Yong; Chen, Honggang; Gao, Xiao qin

doi:10.1155/2021/3782446

Cited by 7 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a k and ω k are constants. In formula (13), a k ðtÞ and θ k ðtÞ are variables. Therefore, compared with Fourier transform, Hilbert transform can be applied to nonstationary signal processing.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…The filter can only suppress the noise in the specified frequency band well, but when the acoustic signal and the noise characteristic band are unknown or in the same frequency band, it is difficult to achieve the expected effect. Compared with filter, wavelet decomposition has a better denoising effect [13]. However, noise is a disordered signal, so it is very difficult to select wavelet basis function and threshold [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Denoising Method of Pipeline Leakage Signal Based on VMD and Hilbert Transform

et al. 2023

View full text Add to dashboard Cite

In order to solve the problem of low localization accuracy of water supply pipeline leakage detection using acoustic emission signals, a noise reduction algorithm based on variational mode decomposition (VMD) and Hilbert transform is proposed in this paper. Firstly, the leakage signal is decomposed into several intrinsic mode functions (IMF) using VMD, and the number of decomposition layers is determined, and the IMF component is transformed by Hilbert to get the marginal spectrum, and the noise are preliminarily filtered according to the marginal spectrum characteristics and the cross-correlation coefficient. Secondly, the signal denoising is realized by using filtering technology and cross-correlation coefficient. Finally, the time delay estimation of the denoised signal is estimated and the location of the leakage point is calculated based on the positioning principle. In order to verify the performance of the algorithm, a water supply pipeline experiment platform for leakage simulation and leak location experiment was conducted. The cross-correlation algorithm, VMD-correlation coefficient algorithm, and the proposed algorithm are analyzed through simulation and experiment. The results show that compared with the other two algorithms, the proposed algorithm can effectively remove the noise in the leakage signal and improve the accuracy of leakage location.

show abstract

Section: Journal Of Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Denoising Method of Pipeline Leakage Signal Based on VMD and Hilbert Transform

et al. 2023

View full text Add to dashboard Cite

show abstract

“…AM can be exploited to enhance the learning capacity and interpretability of DL networks [6]. In the field of fault diagnosis, DL networks based on AM are becoming more and more popular [7,8]. Huang et al [9] proposed a shallow multi-scale convolutional neural network (CNN) with AM for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Research on bearing fault diagnosis method based on transformer neural network

Yang

Cen

Liu

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Attention mechanism (AM) has been widely used for fault diagnosis and health identification in industrial equipment. The existing researches have only used AM in combination with deep networks, or to replace certain components of these deep networks. This reliance on deep networks severely limits the feature extraction capability of AM. In this paper, a bearing fault diagnosis method based on signal Transformer neural network (SiT) with pure AM is proposed. Firstly, the raw one-dimensional vibration time-series signal is segmented and a new segmented learning strategy is introduced. Secondly, linear encoding and position encoding are performed on the segmented subsequences. Finally, the encoded subsequence is fed to the Transformer for feature extraction to achieve fault identification. The validity of the proposed method is verified using Case Western Reserve University (CWRU) dataset and self-priming centrifugal pump bearing dataset. Compared with other existing methods, the proposed method still achieves the highest average diagnostic accuracy without any data preprocessing. The results demonstrate that the proposed SiT based on pure AM can extract features and identify faults from the raw vibration signal, and has superior diagnostic performance.

show abstract

“…These are designed to be used with specific software and proprietary data formats from the manufacturer. Secondly, there are portable vibration analysers [13,14]. Not only do they use specific software and proprietary data formats, but they are also expensive devices not suitable for continuous monitoring due to the need to carry out in-place analysis by specialized staff.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Industrial IoT System for Wireless Monitoring of Electric Motors Condition

et al. 2022

View full text Add to dashboard Cite

Condition monitoring of industrial equipment has become a critical aspect in Industry 4.0. This paper shows the design, implementation and testing of a low-cost Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system can be used to detect operating anomalies and paves the way for building predictive maintenance models. The system is built using low-cost hardware components (wireless multi-sensor modules and single-board computers as gateways), open-source software and open cloud services, where all the relevant information is stored. The module collects real-time vibration data from electric motors. Vibration analyses in the temporal and frequency domains were carried out in both modules and gateways to compare their capabilities. This approach is also a springboard to using edge/fog computing to save cloud resources. A system prototype has been tested in the laboratory and in an industrial dairy plant. The results show that the proposed system can be used for continuous monitoring of any rotatory machine with similar accuracy to professional monitoring devices but at a significantly lower cost.

show abstract

Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

Cited by 7 publications

References 24 publications

Denoising Method of Pipeline Leakage Signal Based on VMD and Hilbert Transform

Denoising Method of Pipeline Leakage Signal Based on VMD and Hilbert Transform

Research on bearing fault diagnosis method based on transformer neural network

Low-Cost Industrial IoT System for Wireless Monitoring of Electric Motors Condition

Contact Info

Product

Resources

About