Kalman filter identification method for micro-vibration transfer paths of satellites

2021 ◽  
pp. 095441002110533
Author(s):  
Yan Shen ◽  
Yang Xu ◽  
Xiaowei Sheng ◽  
Xianbo Yin
Keyword(s):  
Kalman Filter ◽  
Correlation Coefficients ◽  
Vibration Signal ◽  
Satellite System ◽  
Solar Array ◽  
Target Point ◽  
Mechanical Equipment ◽  
Displacement Response ◽  
Transfer Path ◽  
Identification Method

Micro-vibrations on-board a satellite have degrading effects on the performance of certain payloads like observation cameras. The major sources of vibrations include momentum wheels, solar array drives, other rotary mechanical equipment, etc. These vibrations result in loss of the pointing precision and image quality of the payload through intricate transfer paths. To improve the accuracy of a satellite system with many vibration sources and complex transfer paths, it is necessary to determine the main transfer path of vibration. In this study, a path identification method is proposed and applied to the transfer system from the momentum wheel to the camera mount. First, the observer/Kalman filter identification (OKID) algorithm is used to acquire the state-space equation of each path subsystem. Then, the subsystem order is obtained based on the slope of the singular entropy increment. In the next phase, combined with the measured disturbance force of the momentum wheel, the displacement response of the target point is predicted. Finally, the dominant transfer path of vibration is achieved by calculating the vibration contribution of each path to the response point. The results indicate that the dominant transfer path is the axial path of the horizontal momentum wheel, which contributes to the vibration of the camera mount at most. Effective vibration reduction measures should be taken to this path to suppress the vibration signal. In comparing the identified displacement response with the finite element response of the camera mount under different noise conditions, the correlation coefficients are >0.85, which proves the accuracy and anti-noise capability of the identification method.

scholarly journals An investigation into high-speed train interior noise with operational transfer path analysis method

2021 ◽  
Author(s):  
Muxiao Li ◽  
Ziwei Zhu ◽  
Tiesong Deng ◽  
Xiaozhen Sheng
Keyword(s):  
Sound Source ◽  
Reference Point ◽  
Source Identification ◽  
High Speed ◽  
Interior Noise ◽  
Target Point ◽  
High Speed Train ◽  
Transfer Path ◽  
Sound Source Identification ◽  
Transfer Path Analysis

AbstractPassengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

scholarly journals Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 280
Author(s):  
Farzan Farhangian ◽  
Hamza Benzerrouk ◽  
Rene Landry
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Flight Test ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Measurement Unit ◽  
Starting Point ◽  
Leo Satellites ◽  
Positioning Method ◽  
Global Navigation Satellite

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

scholarly journals A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

2021 ◽  
Vol 2112 (1) ◽  
pp. 012020
Author(s):  
Xin Zhang ◽  
Qingmo Ja ◽  
SaiSai Ruan ◽  
Qin Hu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Real Life ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Signal Identification ◽  
Feature Vectors ◽  
Identification Method ◽  
Effective Manner ◽  
Empirical Wavelet Transform

Abstract As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

Comparison of Tropospheric Zenith Wet Delay from VLBI and GNSS Estimations

2021 ◽  
Author(s):  
Vicky Jia Liu ◽  
Maaria Nordman ◽  
Nataliya Zubko
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Elevation Angle ◽  
Correlation Coefficients ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Polar Regions ◽  
Long Baseline ◽  
Zenith Wet Delay ◽  
Space Geodetic Techniques

<p>Tropospheric delay is one of the major error sources for space geodetic techniques such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite System (GNSS). In this study, we compared the agreement of tropospheric zenith wet delay (ZWD) seasonal variations derived from VLBI and GNSS observations at 8 stations that are located at all around the globe. We have analysed time series of 8 years, starting in 2012 until end of 2019. Results show that VLBI_ZWD present clear seasonal variations which depend on the location of each station, in the tropics the variability is more pronounced than in mid-latitudes or polar regions. Furthermore, the VLBI_ZWD also shows a reasonably good agreement with seasonal fit model. When comparing zenith wet delays derived from co-located GNSS and VLBI stations at  cut-off elevation angle, they agree quite well, which is proved by the high correlation coefficients, varying from 0.6 up to 0.95. The biases between the techniques are in mm level and standard errors of the whole time series are in few centimetres.</p>

scholarly journals Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

2020 ◽  
Vol 10 (18) ◽  
pp. 6359 ◽  
Author(s):  
Shuangjie Liu ◽  
Jiaqi Xie ◽  
Changqing Shen ◽  
Xiaofeng Shang ◽  
Dong Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Vibration Signal ◽  
Deep Belief Network ◽  
Mechanical Equipment ◽  
Belief Network ◽  
Bearing Faults ◽  
Qualitative And Quantitative ◽  
Proposed Model ◽  
Different Types

Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

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