Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

Due to their robust learning and expression ability for complex features,the deep learning(DL)model plays a vital role in bearing fault diagnosis.However,since there are fewer labeled samples in fault diagnosis,the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields,which limits the diagnostic performance.To solve this problem,a novel transfer residual Swin Transformer(RST)is proposed for rolling bearings in this paper.RST has 24 residual self-attention layers,which use the hierarchical design and the shifted window-based residual self-attention.Combined with transfer learning techniques,the transfer RST model uses pre-trained parameters from ImageNet.A new end-to-end method for fault diagnosis based on deep transfer RST is proposed.Firstly,wavelet transform transforms the vibration signal into a wavelet time-frequency diagram.The signal’s time-frequency domain representation can be represented simultaneously.Secondly,the wavelet time-frequency diagram is the input of the RST model to obtain the fault type.Finally,our method is verified on public and self-built datasets.Experimental results show the superior performance of our method by comparing it with a shallow neural network.

Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

Fatigue crack prediction is a critical aspect of prognostics and health management research.The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth,and it is widely used to predict the state of fatigue crack.This paper proposes a method of lamb wavebased early fatigue microcrack prediction with the aid of particle filters.With this method,which the changes in signal characteristics under different fatigue crack lengths are analyzed,and the state-and observation-equations of crack extension are established.Furthermore,an experiment is conducted to verify the feasibility of the proposed method.The Root Mean Square Error(RMSE)of the three different resampling methods are compared.The results show the system resampling method has the highest prediction accuracy.Furthermore,the factors affected by the accuracy of the prediction are discussed.

Reconstruction Technology of Flexible Structure Shape Based on FBG Sensor Array and Deep Learning Algorithm

A structural displacement field reconstruction method is proposed to aim at the problems of deformation mon-itoring and displacement field reconstruction of flexible plate-like structures in the aerospace field.This method combines the deep neural network model of the cross-layer connection structure with the fiber grating sensor network.This paper first introduces the principle of strain detection of fiber grating sensor,studies the mapping relationship between strain and displacement,and proposes a strain-displacement conversion model based on an improved neural network.Then the intelligent structure deformation monitoring system is built.By controlling the stepping distance of the motor to produce different deformations of the plate structure,the strain information and real displacement information are obtained based on the high-density fiber grating sensor network and the dial indicator array.Finally,based on the deformation prediction model obtained by training,the displacement field reconstruction of the structure under different deformation states is realized.Experimental results show that the mean absolute error of the deformation of the measuring points obtained by this method is less than 0.032 mm.This method is feasible in theory and practice and can be applied to the deformation monitoring of aerospace vehicle structures.

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.

One Novel Type of Miniaturization FBG Rotation Angle Sensor With High Measurement Precision and Temperature Self-Compensation

In order to achieve rotation angle measurement, one novel type of miniaturization fiber Bragg grating （FBG） rotation angle sensor with high measurement precision and temperature self-compensation is proposed and studied in this paper. The FBG rotation angle sensor mainly contains two core sensitivity elements （FBG1 and FBG2）, triangular cantilever beam, and rotation angle transfer element. In theory, the proposed sensor can achieve temperature self-compensation by complementation of the two core sensitivity elements （FBG1 and FBG2）, and it has a boundless angel measurement range with 2π rad period duo to the function of the rotation angle transfer element. Based on introducing the joint working processes, the theory calculation model of the FBG rotation angel sensor is established, and the calibration experiment on one prototype is also carried out to obtain its measurement performance. After experimental data analyses, the measurement precision of the FBG rotation angle sensor prototype is 0.2° with excellent linearity, and the temperature sensitivities of FBG1 and FBG2 are 10 pro2℃ and 10.1 pm/℃, correspondingly. All these experimental results confirm that the FBG rotation angle sensor can achieve large-range angle measurement with high precision and temperature self-compensation.

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.

Development of High Temperature Acoustic Emission Sensing System Using Fiber Bragg Grating

In some applications in structural health monitoring （SHM）, the acoustic emission （AE） detection technology is used in the high temperature environment. In this paper, a high-temperature-resistant AE sensing system is developed based on the fiber Bragg grating （FBG） sensor. A novel high temperature FBG AE sensor is designed With a high signal-to-noise ratio （SNR） compared with the traditional FBG AE sensor. The output responses of the designed sensors with different sensing fiber lengths also are investigated both theoretically and experimentally. Excellent AE detection results are obtained using the proposed FBG AE sensing system over a temperature range from 25℃ to 200℃. The experimental results indicate that this FBG AE sensing system can well meet the application requirement in AE detecting areas at high temperature.