Application of Wavelet Analysis in The Prediction of Telemetry Data

Xu, Jiangtao; Liu, Pingping

doi:10.21307/ijanmc-2019-044

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yan [19] used wavelet transform coefficients to eliminate the noise and gross error of ship navigation nonlinear trajectory data to complete the trajectory data preprocessing, and realized its nonlinear trajectory prediction through the design of the mathematical model of trajectory prediction. Xu et al [20] introduced the prediction technology based on wavelet analysis to solve the prediction problem of such data in view of the nonstationarity and periodicity of telemetry data and established a short-term prediction model of time series based on the Mallat algorithm. Based on this, a new prediction method is proposed in this paper, that is, first decompose the data into relatively simple component signals using wavelet decomposition technology, then establish prediction models according to the characteristics of each component signal, and finally synthesize the prediction results to establish its drilling trajectory prediction model.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot

Kang

Hua

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The horizontal drilling trajectory of the anti-collision drilling robot is taken as the research object and its drilling trajectory prediction model is established in this paper. The drilling trajectory is affected by many factors, such as the structural parameters of the auger, the drill bit, the drilling process parameters, and the geological characteristics of the coal seam, etc., which results in the existence of multi-level time-scale structure and localization characteristics of the drilling trajectory data in the time domain. The wavelet analysis method can decompose the relatively complex non-stationary time series problem into low-frequency decomposition signals representing trend items and high-frequency decomposition signals representing periodicity and randomness. It can not only predict the main trend of drilling trajectory changes but also effectively predict sudden load changes and short-term changes. Based on this, a new prediction method is proposed in this paper. Firstly, the data is decomposed into relatively simple component signals using wavelet decomposition technology, then the prediction models are established according to the characteristics of each component signal. Finally, the drilling trajectory prediction model is established by synthesizing the prediction results. The anti-impact drilling robot drilling test bed is set up and the drilling experiments are carried out to verify the effectiveness of the model, which can lay the foundation for the anti-impact drilling robot drilling deflection control.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot

Kang

Hua

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

Application of decision tree regression in navigation satellite telemetry data modeling

Zhang

Sun²,

Zhao³

2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

View full text Add to dashboard Cite

In order to understand the working state of on orbit satellites, it is necessary to analyze the telemetry data. The fast-changing telemetry data is an important data to express the navigation service status of navigation satellite. Its analysis and modeling are helpful to mine the deep information of navigation telemetry data. A modeling method of on orbit navigation satellite fast-changing telemetry data based on decision tree regression is proposed. The model is used to predict the power measurements at frequency points. The results show that R2 value is greater than 0.96, and the error of prediction value is small. A fast-changing telemetry data model with good effect is established, which provides a possible scheme for the application of artificial intelligence in the analysis of fast-changing telemetry data.

show abstract

Investigation and Analysis of Sea Surface Temperature and Precipitation of the Southern Caspian Sea Using Wavelet Analysis

Molavi-Arabshahi

Azizpour

Nikan

et al. 2022

Axioms

View full text Add to dashboard Cite

Most time series from real-world processes are stained with noise. Therefore, much attention should be paid to data noise removal techniques. In this study, we use the family of biorthogonal wavelet, high-pass, and low-pass filters, to investigate the power of the wavelet method in removing noise from time series data. Using the wavelet discrete transformation, the variability of precipitation and sea surface temperature is analyzed for a southern region of the Caspian Sea. At each stage of decomposition, the previous wave is decomposed into two waves. In this research, the SST and precipitation data are decomposed into several levels based on discrete wavelet transformation. In each level of decomposition, the previous wave is decomposed into two waves. This can be done many times and at each stage, reducing the amount of data. This method is reversible, and the original wave can be reconstructed using the decomposed values. In the study of discrete wavelet transforms, it was observed that the analysis based on wavelets leads to more accurate results. The reconstruction error in the proposed method is shown to be very small.

show abstract

Application of Wavelet Analysis in The Prediction of Telemetry Data

Cited by 3 publications

References 4 publications

Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot

Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot

Application of decision tree regression in navigation satellite telemetry data modeling

Investigation and Analysis of Sea Surface Temperature and Precipitation of the Southern Caspian Sea Using Wavelet Analysis

Contact Info

Product

Resources

About