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.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis （FEA）. At last, the damage identification system is verified on a CFRP plate with 300mm × 300mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

HCPCF-Based In-Line Fiber Fabry-Perot Refractometer and High Sensitivity Signal Processing Method

An in-line fiber Fabry-Perot interferometer （FPI） based on the hollow-core photonic crystal fiber （HCPCF） for refractive index （RI） measurement is proposed in this paper. The FPI is formed by splicing both ends of a short section of the HCPCF to single mode fibers （SMFs） and cleaving the SMF pigtail to a proper length. The RI response of the sensor is analyzed theoretically and demonstrated experimentally. The results show that the FPI sensor has linear response to external RI and good repeatability. The sensitivity calculated from the maximum fringe contrast is -136 dB/RIU. A new spectrum differential integration （SDI） method for signal processing is also presented in this study. In this method, the RI is obtained from the integrated intensity of the absolute difference between the interference spectrum and its smoothed spectrum. The results show that the sensitivity obtained from the integrated intensity is about -1.34× 10^5 dB/RIU. Compared with the maximum fringe contrast method, the new SDI method can provide the higher sensitivity, better linearity, improved reliability, and accuracy, and it＇s also convenient for automatic and fast signal processing in real-time monitoring of RI.