Fault classification for rolling element bearing in electric machines

Wadhwani, Sulochana; Gupta, Sumit; Kumar, Vinod

doi:10.4103/0377-2063.44230

Cited by 12 publications

(4 citation statements)

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“…Next, the machine learning-based fault diagnosis method become a focus. Nevertheless, these methods are not suitable for dealing with the problem of big data for their limited ability in feature extraction [6][7][8][9]. With the improvement of computing power in the graphics processing unit, deep learning-based fault diagnosis has been used in many related research.…”

Section: Introductionmentioning

confidence: 99%

Time graph sub-domain adaption adversarial for fault diagnosis

Sun,

Yin,

et al. 2024

Meas. Sci. Technol.

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Graph Neural Network (GNN)-based fault diagnosis has attracted widespread attention. However, the existing measure metrics of domain distribution discrepancy always is single, the weight of different domains is unknown, and the scale of GNN-based feature extractor is unknown. To address these issues, a Time Graph Sub-Domain Adaption Adversarial for Fault Diagnosis (TGSDAA) is proposed in this paper. Firstly, a multi-scale time connection layer is proposed to extract the feature of the signal. Specifically, an Encode layer is proposed to construct the undirected graph. Next, a multi-receptive field cluster-graph convolution neural network is proposed to extract features of the graph. Finally, a sub-domain alignment with adversarial adaption is proposed to align different domains and achieve fault diagnosis. Two different datasets are used to verify the effectiveness of TGSDAA. The experimental results show that the average diagnosis accuracy of TGSDAA can improve 4% than other methods.

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Section: Introductionmentioning

confidence: 99%

Time graph sub-domain adaption adversarial for fault diagnosis

Sun,

Yin,

et al. 2024

Meas. Sci. Technol.

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“…Many periodic impulse features are likely to disappear in the deep feature mapping, and the credibility of the diagnostic results is questionable because the diagnostic process is end-to-end and operates like a 'black box' [12]. On the other hand, the vibration signals of gearboxes measured in the industrial field are inevitably interfered with by different noise levels, seriously affecting the accuracy of mechanical fault diagnosis [13,14]. Therefore, the interpretability and noise robustness of 1D CNNs have become a challenge in machinery fault diagnosis [15,16].…”

Section: Introductionmentioning

confidence: 99%

MC-WDWCNN: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis

Zhou,

Zhang,

Jiang

et al. 2024

Meas. Sci. Technol.

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Deep learning-based methods have shown promising results in fault diagnosis, but research on interpretability and noise robustness still needs to be done. A multi-channel wide-kernel wavelet convolutional neural network is proposed to address these issues. Firstly, a first layer of multi-channel wide-kernel convolution is designed to fuse different weight information and suppress high-frequency noise. Secondly, a discrete wavelet transform block is designed to retain the low-frequency components of the discrete wavelet transform for signal denoising and feature dimension reduction. At the same time, Improved Balance Dynamic Adaptive Threshold is used to enhance the robustness of the model’s noise and the sparsity of features, making the model easier to optimize. Lastly, a power spectrum and normalized class activation mapping are designed to validate the post-hoc explanations of the model. The effectiveness and reliability of the Multi-Channel Wide Kernel Wavelet Convolutional Neural Network are verified through two gearbox datasets.

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“…Progress in the use of electrical systems for efficiency and the development of intelligent systems in the industrial sector entails an increasing role of electric motors. Although the purchase cost of grid-powered asynchronous motors is extremely low, it is nevertheless interesting to investigate their state of health during normal operating conditions [1][2][3][4][5][6][7][8][9][10][11][12]. This is because the anomalies reported by electric motors during their normal operation can evolve into serious failures that cause process disservices and numerous safety problems.…”

Section: Introductionmentioning

confidence: 99%

Online SFRA for Reliability of Power Systems: Characterization of a Batch of Healthy and Damaged Induction Motors for Predictive Maintenance

Bucci

Ciancetta

Fioravanti

et al. 2023

Sensors

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Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique’s performance. The results show that the online SFRA could be of interest for monitoring induction motors’ health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.

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Fault classification for rolling element bearing in electric machines

Cited by 12 publications

References 0 publications

Time graph sub-domain adaption adversarial for fault diagnosis

Time graph sub-domain adaption adversarial for fault diagnosis

MC-WDWCNN: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis

Online SFRA for Reliability of Power Systems: Characterization of a Batch of Healthy and Damaged Induction Motors for Predictive Maintenance

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