Study of the long-term performance prediction methods using the spacecraft telemetry data

Fang, Hongzheng; Xing, Yi; Luo, Kai; Han, Liming

doi:10.1109/phm.2012.6228775

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…First the statistical based models are divided into two subgroups, namely, on directly observed state processes (online) and the indirectly observed state processes (offline). In the online statistical based models there are the regression-based models, namely, coefficient regression, auto-regressive (AR) linear, auto-regressive moving average (ARMA) (Chen & Pecht 2012), non-parametric regression method (Fang et al 2012), the Wiener process (Wei et al 2013), Gamma processes (Xu & Wang 2012) and Markovian-based models (Tobon-Mejia et al 2012, Tsui et al 2014.…”

Section: Phm Metricsmentioning

confidence: 99%

“…Concerning the machine learning methods, Table 2, further disseminated in RUL estimations are Artificial neural network (ANN) (Lau et al 2012, Fang et al 2012, Neuro-fuzzy (NF), (Chen & Pecht 2012, Lau et al 2012, Medjaher et al 2012, Support Vector Machine (SVM) (Vasan et al 2013), Hidden Markov Model (HMM), (Lau et al 2012, Medjaher et al 2012 and Dynamic Bayesian Networks (Si et al 2012, Medjaher et al 2012.…”

Section: Phm Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Prognostics Health Management: Perspectives in engineering systems reliability prognostics

Esteves¹,

Nunes²

2015

Safety and Reliability of Complex Engineered Systems

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The Prognostic Health Management (PHM) has been asserting itself as the most promising methodology to enhance the effective reliability and availability of a product or system during its life-cycle conditions by detecting current and approaching failures, thus, providing mitigation of the system risks with reduced logistics and support costs. However, PHM is at an early stage of development, it also expresses some concerns about possible shortcomings of its methods, tools, metrics and standardization. These factors have been severely restricting the applicability of PHM and its adoption by the industry.This paper presents a comprehensive literature review about the PHM main general weaknesses. Exploring the research opportunities present in some recent publications, are discussed and outlined the general guidelines for finding the answer to these issues.

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Section: Phm Metricsmentioning

confidence: 99%

Section: Phm Metricsmentioning

confidence: 99%

Prognostics Health Management: Perspectives in engineering systems reliability prognostics

Esteves¹,

Nunes²

2015

Safety and Reliability of Complex Engineered Systems

View full text Add to dashboard Cite

show abstract

“…Attenuation factor is the comprehensive of particle radiation, ultraviolet irradiation, micrometeoroid collision, and alternating between cold and hot, and so on [10]. It can be captured through a series of simulations about SA on the ground, like particle radiation experiment or through analyzing remote sensing data from on-orbit satellite [4]. Based on the method of analyzing onorbit data to estimate the empirical model, the empirical model of SA output power [2,5,11] can be described as follows:…”

Section: Empirical Modelmentioning

confidence: 99%

“…Prediction models about the attenuation of SA output power can be divided into three categories, which are the data-driven prediction method, prediction method based on model, and dynamic prediction method based on model and data. Among them, data-driven prediction methods, such as the Autoregressive Moving Average model (ARIMA), neural network, and other methods of pattern recognition, have been proved applicative to both diagnostics and prognostics in many fields [3,4], but those methods make it hard to establish a general empirical model about SA output power varying with time. Main tasks of prediction methods based on model are to analyze the influencing factors of SA output power attenuation and their effect on power and further to establish an empirical model about SA output power varying with time, through a lot of simulation and historical measurement data.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring and Predicting Output Power Losses of Solar Arrays Based on Particle Filtering

Fang

Chen

et al. 2013

Mathematical Problems in Engineering

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Solar arrays are the main source of energy to the on-orbit satellite, whose output power largely determines the life cycle of on-orbit satellites. Monitoring and further forecasting the output power of solar arrays by using the real-time observational data are very important for the study of satellite design and on-orbit satellite control. In this paper, we firstly describe the dynamical model of output power with summarizing the influencing factors of attenuation for solar arrays and elaborating the evolution trend of influencing factors which change with time. Based on the empirical model, a particle filtering algorithm is formulated to predict the output power of solar arrays and update the model parameters, simultaneously. Finally, using eight-year observational data of voltage and current from a synchronous on-orbit satellite, an experiment is carried out to illustrate the reliability and accuracy of the particle filtering method. Comparative results with classical curve fitting also are presented with statistical root mean square error and mean relative error analysis.

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“…Zhao introduced a method of eliminating the outlier of telemetry data which has more reliability and practicability [3]. Losik and Fang proposed several prediction methods of telemetry parameters but did not focus on the analysis of the credibility of telemetry data [4] [5].…”

Section: Introductionmentioning

confidence: 99%

The Study of the Spacecraft Telemetry Data Credibility Calculation Method based on the Probability Statistics and Fuzzy Algorithm

Fang¹,

Ma²,

Han³

et al. 2012

Proceedings of 2012 National Conference on Information Technology and Computer Science

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The credibility of the spacecraft telemetry data can be used to characterize the confidence degree of telemetry data and improve the accuracy of the spacecraft condition monitoring and diagnosis system. This paper proposed the telemetry data credibility calculation methods based on the probability statistics and fuzzy algorithm, the process and performance of the algorithms were studied and analyzed. Besides, the data credibility calculation software is realized and applied. The experiment result shows the proposed methods can be effectively applied to the monitoring and diagnosing the spacecraft telemetry data, and has strong practical significance in the field of the spacecraft engineering project.

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Study of the long-term performance prediction methods using the spacecraft telemetry data

Cited by 8 publications

References 2 publications

Prognostics Health Management: Perspectives in engineering systems reliability prognostics

Prognostics Health Management: Perspectives in engineering systems reliability prognostics

Intelligent Monitoring and Predicting Output Power Losses of Solar Arrays Based on Particle Filtering

The Study of the Spacecraft Telemetry Data Credibility Calculation Method based on the Probability Statistics and Fuzzy Algorithm

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