Test for random in electrical signals time series of CO_2 short circuit transition welding process by the method of surrogate data

This paper introduced the basic theory and algorithm of the surrogate data method, which proposed a rigorous way to detect the random and seemingly stochastic characteristics in a system. The Gaussian data and the Rossler data were used to show the availability and effectivity of this method. According to the analysis by this method based on the short-circuiting current signals under the conditions of the same voltage and different wire feed speeds, it is demonstrated that the electrical signals time series exhibit apparently randomness when the welding parameters do not match. However, the electrical signals time series are deterministic when a match is found. The stability of short-circuiting transfer process could be judged exactly by the method of surrogate data.

A bearing fault diagnosis method based on a convolutional spiking neural network with spa tial-tempor al fea ture-extr action capability

Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.

Analysis of Non-stationary Signals Based on Nonlinear Chaotic Theories

In the paper, two nonlinear parameter estimation methods based on chaotic theory, surrogate data method and Lyapunov exponents, are used to distinguish the difference of non-stationary signals. After brief introducting of the corresponding algorithms, two typical different non-stationary signals with different nonlinear constraining boundaries are taken to be compared by using the above two methods respectively. The obtained results demonstrate that the apparently similar signals are distinguished effectively in a quantitative way by applying above nonlinear chaotic analyses.