A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

Tan, Xiaorong; Xu, Jiangning; Li, Fangneng; Wu, Miao; Chen, Ding; Liang, Yifeng

doi:10.1049/rsn2.12315

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…where a is the development coefficient representing the development trend of x (0) , and b is the grey action quantity reflecting the change relationship of x (0) [33]. Defining â = [a, b] T , which were estimated by the least squares method:…”

Section: Establishment Of Gm (11)mentioning

confidence: 99%

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Zou

Zhang²,

Chen

et al. 2023

Sustainability

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Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn.

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Section: Establishment Of Gm (11)mentioning

confidence: 99%

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Zou

Zhang²,

Chen

et al. 2023

Sustainability

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show abstract

“…There are many methods that can be applied to such nonlinear and unsmooth time series modeling. The effectiveness and robustness of traditional modeling and forecast methods such as fitting and extrapolation, the Grey System Model (GM) [18], and Auto Regressive and Moving Average (ARMA) [19] have been proven. With the recent developments in machine learning, the application of machine learning algorithms to GNSS signal processing has received increasing attention [20].…”

Section: Introductionmentioning

confidence: 99%

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method

Du,

Chen,

Yuan

et al. 2024

Sensors

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This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

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“…Relevant work has been published, for example, Li et al improved the generalization ability of the grey model using an improved particle swarm algorithm, achieving a 6-hour prediction error of better than 1.60 ns for the clocks of four global positioning system (GPS) satellites 25 . Tan et al proposed an improved grey model based on the new information theory 26 , which showed improved average prediction performance compared to the basic grey model. Literature 27 has also compared the grey and polynomial models.…”

mentioning

confidence: 99%

Real-time prediction of the approximate non- homogeneous white exponential law BDS clock bias sequences: modelling and strategy

Tan,

Xu,

et al. 2024

Preprint

Self Cite

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Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting precess. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's forecast accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP.

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A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

Cited by 6 publications

References 17 publications

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method

Real-time prediction of the approximate non- homogeneous white exponential law BDS clock bias sequences: modelling and strategy

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