Determine turntable coordinate system considering its non-orthogonality

Guo, Xiaoting; Tang, Jun; Li, Jie; Wang, Chenguang; Shen, Chong; Liu, Jun

doi:10.1063/1.5084799

Cited by 26 publications

(18 citation statements)

References 25 publications

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“…The comparison results between two methods and the original data are shown obviously by Allan variance method. [17][18][19][20][21] Specifically, by using C-SVM, the drift of temperature is reduced more evidently.…”

Section: Resultsmentioning

confidence: 99%

Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

Wang

Cao

2020

International Journal of Distributed Sensor Networks

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This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 [Formula: see text] to 0.02194 [Formula: see text], and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 [Formula: see text] to 0.0329 [Formula: see text], and the temperature drift trend and noise characteristics are improved clearly.

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Section: Resultsmentioning

confidence: 99%

Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

Wang

Cao

2020

International Journal of Distributed Sensor Networks

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“…In recent years, methods based on signal processing [3] or deep learning [4,5] are widely used to solve practical engineering problems [6][7][8][9]. Moreover, in the field of compound fault diagnosis of rotating machinery, with the continuous exploration of many researchers, novel intelligent fault diagnosis methods emerge in an endless flow.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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“…The conventional magnetometer calibration methods assume that a reference sensor, which can provide accurate heading information, is available [6][7][8]. A well-known example of such an approach is compass swinging [7].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Cao

2020

Sensors

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This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.

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Determine turntable coordinate system considering its non-orthogonality

Cited by 26 publications

References 25 publications

Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

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