We present a novel time-delay chaotic system by adopting multiple electro-optic nonlinear loops. The dynamic characteristics of the proposed chaotic system are investigated by means of the bifurcation diagram and the permutation entropy. The security performance of the system is analyzed in detail by using autocorrelation function, delayed mutual information, and the permutation information approach. The simulation results show that the time-delay signature is suppressed under certain simple conditions. Moreover, a synchronization scheme on basis of the proposed system is analyzed. Both the security and the feasibility are verified. The proposed chaotic system can be used in secure communications and also has potential applications in random number generation or chaos computing.
Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non‐stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non‐linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO‐LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO‐LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.