Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

Sui, Shunqi; Wang, Kaiyun; Chen, Shiqian

doi:10.1088/1361-6501/acfd4b

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…More than 80% of the existing literature on bearing fault diagnosis employs vibration signal-analysis methods. Tis method usually uses manual approaches such as the fast Fourier transform (FFT) [10], wavelet transform (WT) [11], and empirical mode decomposition (EMD) [12] to extract signal features and then uses a support vector machine (SVM) [13], K-nearest neighbor (KNN) [14], and BP neural network (BPNN) [15] to obtain diagnostic results. However, these feature extraction methods rely on expert experience and knowledge, which can easily introduce artifcial errors and have poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

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Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods.

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