Comparative Study and Analysis of Pulse Rate Measurement by Vowel Speech and EVM

Paul, Ria; Shandilya, Rahul; Sharma, R. K.

doi:10.1007/978-3-030-00665-5_15

Cited by 1 publication

(1 citation statement)

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“…However, most samples are collected under ideal working conditions in the laboratory, which may lead to the contingency when verifying the feasibility of the deep learning method. In other words, this deep learning method can only diagnose the gearbox under the specific working conditions [39,40], which means that the reliably is very poor.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Section: Introductionmentioning

confidence: 99%