wavelet packet
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Feng He ◽  
Qing Ye

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Yuxing Li ◽  
Feiyue Ning ◽  
Xinru Jiang ◽  
Yingmin Yi

The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

Measurement ◽  
2022 ◽  
pp. 110726
Xiaolong Liu ◽  
Jian Han ◽  
Hanwen Xu ◽  
Xinbiao Xiao ◽  
Zefeng Wen ◽  

2022 ◽  
pp. 1246-1262
Suraj Kumar Nayak ◽  
Ashirbad Pradhan ◽  
Salman Siddique Khan ◽  
Shikshya Nayak ◽  
Soumanti Das ◽  

This chapter is aimed at identifying the variation in the cardiac electrophysiology due to the abuse of the cannabis products (bhang) in a non-invasive manner. ECG signals were acquired from 25 Indian women working in the paddy fields. Amongst them, 10 women regularly abused bhang and the rest 15 women never abused bhang. The ECG signals were preprocessed and subjected to wavelet packet decomposition (WPD) up to the level 3 using db04 wavelet. Ninety-six statistical features were extracted from the wavelet packet coefficients and analyzed using linear and non-linear statistical methods. The results suggested a variation in the cardiac electrophysiology due to the abuse of bhang. Artificial neural networks (ANNs), namely, radial basis function (RBF) and multilayer perceptron (MLP) were able to classify the ECG signals with an accuracy of ≥95%. This supported the hypothesis that abuse of bhang may alter the cardiac electrophysiology. The results of the study may be used to increase awareness among people to avoid the abuse of cannabis products.

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