IEEE SSCS Switzerland Chapter Hosts Distinguished Lecture on Mixed-Signal Technologies for Ultrawideband Signal Processing Systems [Chapters]

Coustans, Mathieu; Bron, Michel; Jang, Taekwang; Pepe, Domenico; Manganaro, Gabriele

doi:10.1109/mssc.2020.3035986

Cited by 1 publication

(1 citation statement)

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“…The training time of the obvious model increases as the model is trained. It can be seen from the analysis of the results that the ratio of the number of training standards to the number of test standards selected in this study is 4000 : 2000, and the percentage of training in the total data set is 67%, between 50% and 70%, which is also a reasonable distribution [ 25 ].…”

Section: Results and Analysismentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Xiao

Wang

2022

Computational Intelligence and Neuroscience

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Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. The experimental verification of the proposed method is carried out based on the public data set, and the effects of three different signal transformation methods based on vibration signal through vibration gray map, short-time Fourier transform time-frequency map, and continuous wavelet transform time-frequency map on the accuracy of diagnosis model are compared and analyzed. A very accurate guarantee is received, close to 100%. The final experimental results demonstrate the effectiveness of the information on the accuracy of diagnostic testing and provide new ideas for the verification and testing of wind turbine wind energy. Compared with other machine learning algorithms, the real-time recognition of machine learning based on time-domain statistical features is lower than that of convolutional neural network methods. The effect of the scale of the trained model on the accuracy of the algorithm is discussed. A sample ratio of 50% and 70% was found to be appropriate.

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Section: Results and Analysismentioning

confidence: 99%