Modeling and test of flywheel vibration isolation system for space telescope

Studied on the flywheel micro vibration isolator of a space telescope, the relationship between input and output (I-O) disturbance force and velocity vector is described by the characteristic transfer matrix in the subsystem of the flywheel vibration isolation. The elastic support coupling vibration transfer matrix of the vibration isolator is derived, and the vibration transfer characteristics of the vibration isolation system are studied. The dynamic model of the three degrees coupled vibration isolation system about the flywheel micro vibration excitation, multi elastic support and basic structure is established on the admittance method and partition subsystem. Model simulation and the flywheel vibration isolation system test results show that the two spectra are consistent basically in the frequency components, the form of vortex frequency curve and the change of amplitude, which indicates that the key factors of the vibration characteristics of the flywheel vibration isolation system are accurate and the theoretical analysis is correct. The sub structure analysis method effectively avoids the complexity of the solution of the state vector of the sub structure coupling interface. The elastic support coupled vibration transfer matrix can solve the problem of the sub structure integration and merging, and integrated modeling and analysis in active and passive support system

Optimal design of the main support structure of space camera aiming at the RMS value of random response

To explore the optimal design method for main support structure of micro satellite, this paper proposed a method targeting the random acceleration response RMS value of the space camera installation position when design the main support structure of LQ-video satellite in Jilin-1 group satellites. Camera main support structure optimization mathematical model was established, and the thickness and flexible beam position of the flexible beam support structure has been optimized in the establishment of the optimization mathematical model. When the flexible beam thickness is 2.5 mm, and the distance between it and the support structure mounting surface is 94.5 mm, the camera installation point acceleration response root mean square (RMS) value is minimal. Engineering analysis showed that the maximal random response RMS of the camera installation point is 19.6 grms and the maximal relative magnification is 0.93. The camera mechanics test showed that the maximal relative error of finite element analysis and experimental measurements is 4.0 % and the maximal relative magnification of the response is 1.2 which is less than the overall index 1.5. It proved that the optimization method is effective and feasible

Multi-objective topology optimization to reduce vibration of micro-satellite primary supporting structure

We applied multi-objective topology optimization to reduce vibration of the primary supporting structure of video satellites in the frequency domain. The optimal structure is obtained by the multi-objective topology optimization with stiffness and random vibration response as the targets. This is compared with the optimal structure obtained by single-objective topology optimization with stiffness as the target. The dynamic analysis results show that the root mean square values in all three spatial directions of the optimal structure by the multi-objective optimization are smaller than that of the single-objective optimization. The maximal declining value reaches 2.94 g, and the maximal declining degree is 30.6 %. The maximal declining response value on the top of the cylinder reaches 3.87 g with a degree of 33.0 %. The results demonstrate that the multi-objective optimization method significantly improves the vibration response of the base plate, which therefore suppressed the vibration of the satellite. An acceptance condition experiment is performed for the satellite with the optimal base plate from the multi-objective optimization. The dynamic analysis results match well with the experimental data, and verify the applicability of the multi-objective optimization

Sun sensor design and test of a micro satellite

According to the requirement of small satellite, this paper designed a digital sun sensor which diaphragm is a V-shaped cross-section structure. Using Position Sensitive Detector (PSD) as the light detector, we designed the V-shaped cross-section structure based on the pinhole imaging principle. The sun sensor realized the accurate calculation for two axis sun angle of the sun sensor. The mechanical test, thermal test and testing of the sun sensor are designed and carried out. The mechanical test and thermal test results verify the stability of the sun sensor. Testing result shows that the detection angle can reach (120°)×(120°), and the attitude determination accuracy is better than 6” in the entire viewing field. The mass, volume and power consumption of the sun sensor is 0.177 kg, 78 mm×77 mm×21 mm and 0.25 W. The sun sensor has low power consumption, large viewing angle and high precision characteristics, which realized the sun sensor the miniaturization and meet the requirements of the micro satellite. Its performance has been verified in orbit

Flywheel micro-vibration characters of a high resolution optical satellite

According to the pictures of a sub-meter resolution optical satellite which were acquired on orbit, there is a phenomenon of jitter in the process of taking pictures. As the main attitude control component of the satellite, the flywheel will produce the disturbance in its normal work, which has great influence on the high resolution optical satellite. This paper has respectively researched the flywheel components’ disturbance mechanism from four parts, including uneven rotator, rotator friction, bearing disturbance, foundation loose, and builds the mathematical model of disturbance to analyze the characteristics of disturbance. We have simulated and tested the flywheel components’ disturbance. The disturbance force of flywheel components is 2 N magnitude and the torque of disturbance is 1.5 N·m magnitude in time domain. The flywheel's infrastructure should be more inflexible especially around 90-100 Hz. For this target high resolution optical satellite, there should be effective damping measures around 48.6 Hz, 190.4 Hz and 285.4 Hz to decrease the flywheel disturbance to guarantee the high precision of the satellite. The result would offer guidance for system optimization design and vibration isolation compensation of the later type of improved satellite or other same type of satellites

Multi-objective topology optimization to reduce vibration of micro-satellite primary supporting structure

We applied multi-objective topology optimization to reduce vibration of the primary supporting structure of video satellites in the frequency domain. The optimal structure is obtained by the multi-objective topology optimization with stiffness and random vibration response as the targets. This is compared with the optimal structure obtained by single-objective topology optimization with stiffness as the target. The dynamic analysis results show that the root mean square values in all three spatial directions of the optimal structure by the multi-objective optimization are smaller than that of the single-objective optimization. The maximal declining value reaches 2.94 g, and the maximal declining degree is 30.6 %. The maximal declining response value on the top of the cylinder reaches 3.87 g with a degree of 33.0 %. The results demonstrate that the multi-objective optimization method significantly improves the vibration response of the base plate, which therefore suppressed the vibration of the satellite. An acceptance condition experiment is performed for the satellite with the optimal base plate from the multi-objective optimization. The dynamic analysis results match well with the experimental data, and verify the applicability of the multi-objective optimization

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK

In order to extract effective features of complex signals,a fault diagnosis method combining short-time Fourier transform and two-dimensional convolution neural network is proposed. First,a short-time Fourier transform is performed on the rotating mechanical vibration signal to obtain a time-frequency map. Then,the time-frequency map is input into a two-dimensional convolution neural network for identification,and a final classification result is obtained. The method is applied to the fault diagnosis of rolling bearing and gearbox,and has achieved good results in the data of the Case Western Reserve University and the PHM2009 dataset.The correct rate is better than the direct comparison of the original signal into CNN,which verifies the superiority of the method

A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing

Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment

A comprehensive study of the magnetic concentrating sensor for the damage detection of steel wire ropes

Magnetic flux leakage (MFL) method is an important way to detect the fault of steel wire ropes and the sensor for the collection of MFL plays a vital role in the damage detection. Among varied sensors based on MFL method, the magnetic concentrating sensor shows a lot of advantages in the detection of wire ropes. The use of the magnetic concentrator can assist the magnetic sensitive component to detect the MFL and reduce the number of the Hall components. The lift-off of the magnetic concentrating sensor can also be set to a feasible value which is easier to be ensured in the practical application. Although many researches on the magnetic concentrating sensor have been carried out, few of them have a comprehensive and thorough investigation, which should include the simulation analysis, the prototype design, the broken wire experiment and the comparison with other commonly used sensors. In this paper, a magnetic concentrating sensor is developed and compared with a Hall array sensor through both simulation and experiments. Firstly, the three-dimensional models of the magnetic concentrating sensor and the Hall array sensor are designed and their performance on collecting MFL is analyzed through finite element method (FEM). Secondly, the prototypes of the two kinds of sensors are designed according to the simulation results and their corresponding processing circuits are made. Finally, the effectiveness of the two sensors is evaluated by broken wire experiments with different rope diameters. The simulation and experimental results demonstrate that the magnetic concentrating sensor achieves a higher sensitivity and signal-to-noise ratio (SNR) than the Hall array sensor with less Hall components and simpler pre-processing circuit