Anomaly Monitoring Method for Key Components of Satellite

نویسندگان

  • Jian Peng
  • Linjun Fan
  • Weidong Xiao
  • Jun Tang
چکیده

This paper presented a fault diagnosis method for key components of satellite, called Anomaly Monitoring Method (AMM), which is made up of state estimation based on Multivariate State Estimation Techniques (MSET) and anomaly detection based on Sequential Probability Ratio Test (SPRT). On the basis of analysis failure of lithium-ion batteries (LIBs), we divided the failure of LIBs into internal failure, external failure, and thermal runaway and selected electrolyte resistance (R(e)) and the charge transfer resistance (R(ct)) as the key parameters of state estimation. Then, through the actual in-orbit telemetry data of the key parameters of LIBs, we obtained the actual residual value (R(X)) and healthy residual value (R(L)) of LIBs based on the state estimation of MSET, and then, through the residual values (R(X) and R(L)) of LIBs, we detected the anomaly states based on the anomaly detection of SPRT. Lastly, we conducted an example of AMM for LIBs, and, according to the results of AMM, we validated the feasibility and effectiveness of AMM by comparing it with the results of threshold detective method (TDM).

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Fault Strike Detection Using Satellite Gravity Data Decomposition by Discrete Wavelets: A Case Study from Iran

Estimating the gravity anomaly causative bodies boundary can facilitate the gravity field interpretation. In this paper, 2D discrete wavelet transform (DWT) is employed as a method to delineate the boundary of the gravity anomaly sources. Hence, the GRACE’ satellite gravity data is decomposed using DWT. DWT decomposites a single approximation coefficients into four distinct components: the appr...

متن کامل

Evaluation Testing of Learning-based Telemetry Monitoring and Anomaly Detection System in SDS-4 Operation

Health monitoring and anomaly detection techniques for artificial satellites are very significant, as it is very hard to repair those space systems on orbit. Authors have proposed the framework of learning-based anomaly detection that applies statistical machine learning and data mining techniques to the satellite telemetry data to automatically obtain normal behavior models which can be used f...

متن کامل

Anomaly detection for satellite power subsystem with associated rules based on Kernel Principal Component Analysis

a r t i c l e i n f o The paper presents an implementable method of anomaly detection for satellite power system. Specifically, a data-driven anomaly detection method for sensor data integrated Kernel Principal Component Analysis (KPCA) and association rule mining is demonstrated. Establishing associated rules among sensor monitoring data sets, this approach analyses the structure of measure sp...

متن کامل

An efficient method for cloud detection based on the feature-level fusion of Landsat-8 OLI spectral bands in deep convolutional neural network

Cloud segmentation is a critical pre-processing step for any multi-spectral satellite image application. In particular, disaster-related applications e.g., flood monitoring or rapid damage mapping, which are highly time and data-critical, require methods that produce accurate cloud masks in a short time while being able to adapt to large variations in the target domain (induced by atmospheric c...

متن کامل

Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 2014  شماره 

صفحات  -

تاریخ انتشار 2014