IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen, Bingyan; Dong-li, Song; Cheng, Yao; Zhang, Weihua; Huang, Baoshan; Muhamedsalih, Yousif

doi:10.37965/jdmd.2022.65

Cited by 39 publications

(23 citation statements)

References 45 publications

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“…The results in the work 43 preliminarily demonstrate the effectiveness and advantages of employing the power function as the quasi-arithmetic mean generator in constructing transient quantitative indicators. Therefore, the generalization method used to generate PFGI1s can be reasonably applied to other sparsity measures.…”

Section: Power Function-based Gini Indices ⅰ ⅱ and ⅲmentioning

confidence: 75%

“…Using the two weight sequences of GI and employing the power function as the generator of quasi-arithmetic means, PFGI1s have been proposed as a generalization of GI for quantifying transient features in bearing fault diagnosis, as follows 43 :…”

Section: Definitions Of the Power Function-based Gini Indicesmentioning

confidence: 99%

“…Data sequences from Bernoulli distributions contain only numbers 0 and 1, and data sequences generated by Bernoulli distributions with different success probabilities can simulate different sparsity. 13,38,42,43 Thus, data sequences from Bernoulli distributions with different success probabilities are generated to investigate the sparse quantization capability of PFGI2s and PFGI3s. Traditional sparsity measures, GGIs and PFGI1s are used for comparison.…”

Section: Performance Analysis Via Numerical Simulationmentioning

confidence: 99%

“…More recently, to construct new indicators to improve the quantification performance of repetitive transient features under complex disturbances, Chen et al 43 proposed another single-parameter generalization of GI by adopting power function as the quasi-arithmetic mean generator, which is called the power function-based Gini indices Ⅰ (PFGI1s) in this paper. The results show that increasing the power exponent can improve the repetitive transient discriminability of PFGI1s under noise interference, which demonstrates the effectiveness of using the power function as the quasi-arithmetic mean generator.…”

Section: Introductionmentioning

confidence: 99%

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Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Chen

Zhang

et al. 2023

Structural Health Monitoring

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The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures.

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Section: Power Function-based Gini Indices ⅰ ⅱ and ⅲmentioning

confidence: 75%

Section: Definitions Of the Power Function-based Gini Indicesmentioning

confidence: 99%

Section: Performance Analysis Via Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Chen

Zhang

et al. 2023

Structural Health Monitoring

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“…Gear is a widely-used motion and power transmission component in mechanical equipment, and it is prone to failure due to the poor working condition. Moreover, pitting is the main failure mode of gear, which has been detected by vibration-based methods in the past years [1][2][3]. However, it is difficult for vibration-based methods to quantitatively detect gear pitting.…”

Section: Introductionmentioning

confidence: 99%

U-shaped Vision Transformer and Its Application in Gear Pitting Measurement

Wang

Qin

et al. 2022

JDMD

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Although convolutional neural networks (CNNs) have become the mainstream segmentation model, the locality of convolution makes them cannot well learn global and long-range semantic information. To further improve the performance of segmentation models, we propose u-shaped vision Transformer (UsViT), a model based on Transformer and convolution. Specifically, residual Transformer blocks are designed in the encoder of UsViT, which take advantages of residual network and Transformer backbone at the same time. What’s more, transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels, enhancing the capability of feature learning. In the decoder, for enhancing receptive filed, different dilation rates are introduced to each convolutional layer. In addition, residual connections are applied to make the information propagation smoother when training the model. We first verify the superiority of UsViT on Automatic Portrait Matting public dataset, which achieves 90.43% Acc、95.56% DSC and 94.66% IoU with relatively fewer parameters. Finally, UsViT is applied to gear pitting measurement in gear contact fatigue test, and the comparative results indicate that UsViT can improve the accuracy of pitting detection.

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