Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

Cavitation recognition of axial piston pumps in noisy environment based on Grad-CAM visualization technique

The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.

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Vibration isolation system(VIS)is usually applied to improve the pointing capability and resolution of a space optical payload.A novel vibration isolator is introduced.The equivalent stiffness model of the isolator is derived by analyzing its static characteristics.A VIS based on the bipod configuration is developed.Its dynamic model is derived and verified using the finite element method and experiments.Because this isolator contains viscoelastic material’which makes the frequency responsive analysis more difficult’an efficiency analysis approach based on the complex stiffness of viscoelastic material is proposed to calculate the transmissibility of the isolator.Finally’a prototype of the vibration isolator is manufactured and experimental studies are carried out.The experimental results show that the analytical results are in good correspondence with the experimental data and the isolator can effectively attenuate the vibrations for the optical payloads.Keywords：isolator;viscoelastic material；space optical

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston pump design

The power density of axial piston pumps can benefit greatly from increased rotational speeds.However,the maximum rotational speed of axial piston machines is limited by the cavitation phenomenon for a given volumetric displacement.This paper presents a scaling law derived from an analytical cavitation model to estimate the speed limitations for the same series of axial piston pumps.The cavitation model is experimentally verified using a high-speed axial piston pump,and the scaling law is validated with open specification data in product brochures.Results show that the speed limitation is approximately proportional to the square root of the inlet pressure and inversely proportional to the cube root of volumetric displacement.Furthermore,a characteristic constant is defined based on the presented scaling law.This constant can represent the comprehensive capacity of axial piston pumps free from cavitation.