航天器遙測時(shí)間序列數(shù)據(jù)挖掘研究
發(fā)布時(shí)間：2018-12-18 09:15
【摘要】：航天器作為航天事業(yè)的主要載體,是人類進(jìn)行空間探索的基礎(chǔ)。保證在軌航天器的正常工作,關(guān)系到整個航天工程的順利執(zhí)行。航天器遙測數(shù)據(jù)與地面模擬仿真實(shí)驗(yàn)得到的數(shù)據(jù)相比,更能反映航天器的真實(shí)工作狀態(tài),也更具可靠性,是航天器性能監(jiān)測和實(shí)時(shí)狀態(tài)分析的主要依據(jù)。有效利用在軌遙測數(shù)據(jù)并提取有效信息,不僅能為航天器管理決策提供支持,更能對航天器的設(shè)計(jì)改進(jìn)起到參考作用。本文以某航天器2011年至2015年期間近200萬行遙測數(shù)據(jù)為基礎(chǔ),針對遙測參數(shù)的特點(diǎn),設(shè)計(jì)并實(shí)現(xiàn)遙測時(shí)間序列的特征表示、相似性度量以及中心序列計(jì)算這三個方面的算法。本文的主要工作和創(chuàng)新點(diǎn)如下:(1)針對航天器遙測數(shù)據(jù)參數(shù)眾多,類型復(fù)雜的特點(diǎn),設(shè)計(jì)了一種基于全局信息熵的自適應(yīng)分段線性表示方法GIE-APLA。該方法彌補(bǔ)PLA方法在計(jì)算效率方面的不足,采用信息熵來度量當(dāng)前數(shù)據(jù)段的波動,以達(dá)到線性時(shí)間內(nèi)自適應(yīng)劃分的目的。在劃分所得的子序列段中采用線性回歸擬合原始序列,以保證特征表示的精度。實(shí)驗(yàn)結(jié)果表明,該算法在保證壓縮率的前提下,對原始序列有較高的表示精度,為后續(xù)研究奠定了基礎(chǔ)。(2)針對現(xiàn)有時(shí)間序列相似性度量方法的不足,提出了一種基于自適應(yīng)線段的動態(tài)時(shí)間規(guī)整算法ASDTW。該算法針對DTW算法計(jì)算開銷過大的問題,首先采用GIE-APLA算法將原始序列表示為序列段的形式,并根據(jù)其幾何特征定義序列段之間的距離,在動態(tài)匹配階段使用序列段作為基本匹配單元改善傳統(tǒng)逐點(diǎn)匹配策略所導(dǎo)致計(jì)算開銷過大的問題。實(shí)驗(yàn)結(jié)果表明,ASDTW算法保證度量精度的前提下,解決了DTW算法逐點(diǎn)匹配造成計(jì)算開銷過大的問題。(3)針對現(xiàn)有中心序列算法計(jì)算開銷過大和對合并順序敏感的問題,提出了一種基于序列段的中心序列算法SSB。該算法首先通過層次聚類對序列集進(jìn)行相似性的劃分,以減少不同形態(tài)序列之間的影響;然后在各序列子集中以迭代的方式求解中心序列�？紤]到迭代和動態(tài)匹配所造成的計(jì)算開銷,在每次迭代過程中,使用序列段的匹配來減少計(jì)算規(guī)模,并通過定義序列段的質(zhì)心來減少合并順序?qū)Y(jié)果的影響。實(shí)驗(yàn)表明,SSB算法所得中心序列在表征能力上優(yōu)于目前的NLAAF算法,與DBA算法相比性能持平;在計(jì)算效率上要優(yōu)于上述兩種算法。
[Abstract]:As the main carrier of spaceflight, spacecraft is the basis of human space exploration. Ensuring the normal operation of the orbiting spacecraft is related to the smooth implementation of the whole space project. Compared with the data obtained from the ground simulation experiment, the telemetry data can reflect the real working state of the spacecraft and be more reliable. It is the main basis for the performance monitoring and real-time state analysis of the spacecraft. The effective use of in-orbit telemetry data and the extraction of effective information can not only provide support for spacecraft management decision, but also play a reference role in spacecraft design improvement. Based on nearly 2 million lines of telemetry data from 2011 to 2015, this paper designs and implements three algorithms of telemetry time series, such as feature representation, similarity measurement and center sequence calculation, according to the characteristics of telemetry parameters. The main work and innovations of this paper are as follows: (1) aiming at the characteristics of many parameters and complex types of spacecraft telemetry data, an adaptive piecewise linear representation method based on global information entropy (GIE-APLA.) is designed. This method makes up for the deficiency of PLA method in computing efficiency, and uses information entropy to measure the fluctuation of current data segment, so as to achieve the purpose of adaptive partitioning in linear time. In order to ensure the accuracy of feature representation, linear regression is used to fit the original sequence in the subsequence segment. The experimental results show that the algorithm has a high precision for the original sequence under the premise of ensuring compression ratio, which lays the foundation for further research. (2) aiming at the shortcomings of the existing methods of measuring the similarity of time series, An adaptive line segment based dynamic time warping algorithm (ASDTW.) is proposed. In order to solve the problem of excessive computational overhead of DTW algorithm, the algorithm first uses GIE-APLA algorithm to represent the original sequence as the form of sequence segment, and defines the distance between sequence segments according to its geometric characteristics. In the dynamic matching phase, the use of sequence segments as the basic matching unit to improve the traditional point-by-point matching strategy leads to the problem of excessive computational overhead. The experimental results show that the ASDTW algorithm solves the problem that the point by point matching of the DTW algorithm leads to too much computing overhead. (3) the existing central sequence algorithm is too expensive and sensitive to the merging order. This paper presents a central sequence algorithm SSB. based on sequence segments. Firstly, the similarity of sequence sets is partitioned by hierarchical clustering to reduce the influence between different morphological sequences, and then the central sequence is solved iteratively in each sequence subset. Considering the computational overhead caused by iteration and dynamic matching, the matching of sequence segments is used to reduce the computational scale during each iteration, and the effect of merging sequence on the result is reduced by defining the centroid of sequence segments. The experimental results show that the center sequence obtained by the SSB algorithm is superior to the current NLAAF algorithm in representation ability and is equal to that of the DBA algorithm, and its computational efficiency is better than the above two algorithms.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V557;TP311.13
本文編號：2385632
[Abstract]:As the main carrier of spaceflight, spacecraft is the basis of human space exploration. Ensuring the normal operation of the orbiting spacecraft is related to the smooth implementation of the whole space project. Compared with the data obtained from the ground simulation experiment, the telemetry data can reflect the real working state of the spacecraft and be more reliable. It is the main basis for the performance monitoring and real-time state analysis of the spacecraft. The effective use of in-orbit telemetry data and the extraction of effective information can not only provide support for spacecraft management decision, but also play a reference role in spacecraft design improvement. Based on nearly 2 million lines of telemetry data from 2011 to 2015, this paper designs and implements three algorithms of telemetry time series, such as feature representation, similarity measurement and center sequence calculation, according to the characteristics of telemetry parameters. The main work and innovations of this paper are as follows: (1) aiming at the characteristics of many parameters and complex types of spacecraft telemetry data, an adaptive piecewise linear representation method based on global information entropy (GIE-APLA.) is designed. This method makes up for the deficiency of PLA method in computing efficiency, and uses information entropy to measure the fluctuation of current data segment, so as to achieve the purpose of adaptive partitioning in linear time. In order to ensure the accuracy of feature representation, linear regression is used to fit the original sequence in the subsequence segment. The experimental results show that the algorithm has a high precision for the original sequence under the premise of ensuring compression ratio, which lays the foundation for further research. (2) aiming at the shortcomings of the existing methods of measuring the similarity of time series, An adaptive line segment based dynamic time warping algorithm (ASDTW.) is proposed. In order to solve the problem of excessive computational overhead of DTW algorithm, the algorithm first uses GIE-APLA algorithm to represent the original sequence as the form of sequence segment, and defines the distance between sequence segments according to its geometric characteristics. In the dynamic matching phase, the use of sequence segments as the basic matching unit to improve the traditional point-by-point matching strategy leads to the problem of excessive computational overhead. The experimental results show that the ASDTW algorithm solves the problem that the point by point matching of the DTW algorithm leads to too much computing overhead. (3) the existing central sequence algorithm is too expensive and sensitive to the merging order. This paper presents a central sequence algorithm SSB. based on sequence segments. Firstly, the similarity of sequence sets is partitioned by hierarchical clustering to reduce the influence between different morphological sequences, and then the central sequence is solved iteratively in each sequence subset. Considering the computational overhead caused by iteration and dynamic matching, the matching of sequence segments is used to reduce the computational scale during each iteration, and the effect of merging sequence on the result is reduced by defining the centroid of sequence segments. The experimental results show that the center sequence obtained by the SSB algorithm is superior to the current NLAAF algorithm in representation ability and is equal to that of the DBA algorithm, and its computational efficiency is better than the above two algorithms.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V557;TP311.13
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