Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods,making it challenging to ensure the fault diagnosis accuracy and reliability.A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator(ESGMD-CC)and artificial fish swarm algorithm(AFSA)optimized extreme learning machine(ELM)is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis.Firstly,SGMD decomposes the raw vibration signal into multiple Symplectic geometry components(SGCs).Secondly,the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components.Additionally,the calculus operator is performed to strengthen weak fault features and make them easier to extract,and the singular value decomposition(SVD)weighted by power spectrum entropy(PSE)can be utilized as the sample feature representation.Finally,AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification.The superior performance of the proposed method has been validated by various experiments.

Development and engineering application of integrated safety monitoring systems for China’s high-speed trains

With the improvement of the running speed of China’s high-speed trains,the demands for running status monitoring and security assurance of High-speed Electric Multiple Units(EMU)have increased significantly.However,the current safety monitoring systems are independent,which is not conducive to the comprehensive monitoring and information sharing of the whole vehicle.The temperature monitoring of running gear is insensitive to early failures.How to develop a train operation safety monitoring system with strong engineering implementation and high integration is a key problem to be solved.For the monitoring of running stationarity,frame stability and running gear health of China’s high-speed trains,an integrated safety monitoring system framework is designed,and the logic and algorithm for the diagnosis of stationarity,stability and health states of rotating parts are constructed.Monitoring software which fused the temperature,high and low frequency vibration data is developed,and the design and installation of the vibration temperature composite sensors are completed.The research results have realized the integration and comprehensive processing of multiple monitoring systems,completed the improvement from single component and vehicle-level safety monitoring to multi-system,train-level and interactive monitoring.In the process of real vehicle application,the developed monitoring system acquires the vehicle operation status data accurately and in real time.The constructed diagnosis algorithm and logic evaluates the vehicle operation status accurately and in a timely manner,and avoids the progression from fault to accident.The research results show that the integrated safety monitoring system can provide technical support for train operation safety.