Научная статья на тему 'Detection of hardware failures at intermagnet observatories: application of artificial intelligence techniques to geomagnetic records study'

Detection of hardware failures at intermagnet observatories: application of artificial intelligence techniques to geomagnetic records study Текст научной статьи по специальности «Медицинские технологии»

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Ключевые слова
TIME SERIES / PATTERN RECOGNITION / GEOMAGNETIC OBSERVATIONS

Аннотация научной статьи по медицинским технологиям, автор научной работы — Soloviev A. A., Bogoutdinov Sh R., Agayan S. M., Gvishiani A. D., Kihn E.

INTERMAGNET global network consists of 114 observatories around the world, which monitor the Earth''s magnetic field in real time. Data from geomagnetic observatories represent time series. In spite of high quality of measuring instruments all of them are subjected to external impact, which affects the quality of records. The present work is devoted to detection of "Spike", "Baseline jump" and "Baseline drift" failures on records. A search is implemented in the framework of a new geoinformatics approach entitled Discrete Mathematical Analysis (DMA) developed at the Geophysical Center of RAS. It is based on fuzzy logic methods and intended for study of multidimensional data sets and time series. Failures on records are treated as anomalies of particular shapes: e.g., jump/spike is an anomaly on a record leading/not leading to its baseline shift. Preliminarily anomalies on records are detected by FCARS algorithm (Fuzzy Comparison Algorithm for Recognition of Signals). A further search of spikes, baseline jumps and baseline drifts among them is done by additional testing, which uses DMA technique.

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Текст научной работы на тему «Detection of hardware failures at intermagnet observatories: application of artificial intelligence techniques to geomagnetic records study»

RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 11, ES2006, doi:10.2205/2009ES000387, 2009 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE

Electronic Geophysical Year: State of the Art and Results

3-6 June 2009 • Pereslavl-Zalessky, Russia

Detection of hardware failures at INTERMAGNET observatories: application of artificial intelligence techniques to geomagnetic records study

A. A. Soloviev,1 Sh. R. Bogoutdinov,1 S. M. Agayan,1 A. D. Gvishiani,1 and E. Kihn2 Received 14 November 2009; accepted 14 November 2009; published 17 December 2009.

INTERMAGNET global network consists of 114 observatories around the world, which monitor the Earth’s magnetic field in real time. Data from geomagnetic observatories represent time series. In spite of high quality of measuring instruments all of them are subjected to external impact, which affects the quality of records. The present work is devoted to detection of “Spike”, “Baseline jump” and “Baseline drift” failures on records. A search is implemented in the framework of a new geoinformatics approach entitled Discrete Mathematical Analysis (DMA) developed at the Geophysical Center of RAS. It is based on fuzzy logic methods and intended for study of multidimensional data sets and time series.

Failures on records are treated as anomalies of particular shapes: e.g., jump/spike is an anomaly on a record leading/not leading to its baseline shift. Preliminarily anomalies on records are detected by FCARS algorithm (Fuzzy Comparison Algorithm for Recognition of Signals). A further search of spikes, baseline jumps and baseline drifts among them is done by additional testing, which uses DMA technique. KEYWORDS: time series; pattern recognition; geomagnetic observations.

Citation: Soloviev, A. A., Sh. R. Bogoutdinov, S. M. Agayan, A. D. Gvishiani, and E. Kihn (2009), Detection of hardware failures at INTERMAGNET observatories: application of artificial intelligence techniques to geomagnetic records study, Russ. J. Earth. Sci., 11, ES2006, doi:10.2205/2009ES000387.

INTERMAGNET global network consists of 114 observatories around the world, which monitor the Earth’s magnetic field in real time. Data from geomagnetic observatories represent time series. Each observatory is equipped with instrumentation needed for digital registration of three components and module of geomagnetic field. Data are transmitted to INTERMAGNET data centers in France, Japan, USA, Canada and United Kingdom with a minimal delay [Kerridge, 2001].

In spite of high quality standard of measuring instruments all of them are subjected to external impact, which affects the quality of records. Figure 1 illustrates examples of the most common failures, which have to be eliminated prior to further use of obtained observations.

The present work is devoted to recognition of “Spike”,

1 Institution of the Russian Academy of Sciences Geophysical Center, Moscow, Russia

2National Geophysical Data Center, Boulder, Colorado, USA

Copyright 2009 by the Russian Journal of Earth Sciences. http://elpub.wdcb.ru/journals/rjes/doi/2009ES000387.html

“Baseline jump” and “Baseline drift” failures on records. Such failures are characterized by certain morphology; therefore the present work is a continuation of morphologic analysis of time series developed by the authors [Gvishiani, 2008]. We interpret magnetogram as a record y = {yk = y(kh)} given on a segment (registration period) T of discrete semiaxis R+ = {kh, h > 0, k = 1, 2,...}.

Spike. Informal logic which underlies such failure recognition can be formulated in the following way: “Spike is a vertically significant and horizontally insignificant (i.e. singular) disturbance on a record lying on its one side”.

Since a spike is vertically significant it should contain points with very large derivatives (Figure 2). We search them adaptively using fuzzy comparisons [Gvishiani, 2008; Kolmogorov, 1981] and collect into small compact groups. The point is that a real spike often differs from an ideal one, which consists of 3 points (beginning, peak and ending). Nevertheless, as a rule its length is < 10. We set the corresponding A, collect points with large derivatives (red) into small compact groups basing on A-coherence (i.e. distant from each other not more than by A) and fill possible

Figure 1. Examples of magnetometer hardware failures reflected on records.

gaps inside them with other points (green). As a result we Test:

get segments of green and red points with red ends.

We proceed with spike recognition among such segments basing on the second test for homogeneity.

S - set of nodes with large derivatives on a record y, where A - coherence parameter. S = V^i^So, - decomposition into components of A-coherence.

Component Sa contains a spike if

>

(y(Î) - lSa (Î))(y(Î) - rSa (t))fuzzy0

on Sa

^Sa (t) — regr (y| [min Sa —A,min Sa — 1] )

1 1 1 I 1

! . . 1 k L . P™

: ; III

! i i

1 1 ] 1 1 1 1 1 1 1 1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2007

Figure 2. 29 anomalies with large derivatives are recognized.

nT

-10

-30

-20

! ! (a) !

1 "X 'A : : :

! L..D \/Al i i

i i

! !

-10

0

10

27 Sep 2007 16:37

20

-20

-40

-60

-80

30

-30

(b)

-20

-10

0

10

20 Nov 2007 16:19

20

30

Figure 3. Anomaly on the right has passed the test Homogeneity test verifies same sign definiteness of differences.

rSa (t) = regrx(y|

[max Sa + 1, max Sa + A] )

Any red-green segment should neighbor from both sides with relatively calm segments (blue) with a length not less than A. According to them we build special linear regressions ls and rs, each of which we continue to initial red-green segment and consider as variants of background for it (Figure 3).

Homogeneity test verifies same sign definiteness of differences y(t) — ls (t) and y(t) — rs (t) on a current testing segment. Sign definiteness is understood in a special soft, fuzzy way. Segments, which have passed the test, are considered as spike carriers. As a result, 7 spikes were recognized on a magnetogram given on Figure 2.

anomaly first we recognize all anomalies on a record using FCARS [Gvishiani, 2008] algorithm (Figure 4).

The first step is local - recognition of anomalies, which contain jumps. Recognition is implemented using fuzzy bounds of a numeric set introduced by us: fuzzy inf and sup. We get corridors of anomaly from the left and from the right, which should be conformed with each other, i.e. lie at significantly different levels.

A y1 [a,b]

is anomaly recognized by FCARS, t e [a, b],

l inf y(t), l sup y(t)

Baseline jump. The following informal logic underlies recognition: “Jump is a disturbance on a record leading to its baseline shift”. Since jump is considered to be an are fuzzy bounds of anomaly A on [a,t],

nT

800

700

600

500

400

300

200

100

0

-100

i i ! ! ! !

III

i i i i i i i i i i i

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2007

Figure 4. 352 anomalies have been recognized by FCARS.

3 Feb 2007 04:48 15 Feb 2007 16:46 24 Aug 2007 17:02

Figure 5. Anomalies on the second and on the third pictures have passed the test.

r inf y (t), r sup y (t) are fuzzy bounds of anomaly A on [t,b].

Local test (Figure 5): anomaly A = y|[a,b] contains jump if 3 t e [a, b]:

(l inf y(t) < l sup y(t) < r inf y(t) < r sup y(t)) A

f7mes(A) = (r inf y(t) — l sup y(t)K 0 5

y (r sup y(t) — l inf y(t))

(jump up), or

(r inf y(t) < r sup y(t) < l inf y(t) < l sup y(t)) A

/■mes(A) = (1 inf y(t) - r sup y(t)) ^ o 5 1 (1 sup y(t) - r inf y(t))

Global test (Figure 6): local jump of anomaly A at point t is global if

1 inf y(t, A) < 1 sup y(t, A) < r inf y(t, A) < r sup y(t, A),

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Jmes(A) = (r inf y(t, A) - 1 sup y(^, A)) > 0.5 (r sup y(t, A) — 1 inf y(t, A))

r inf y(t, A) < r sup y(t, A) < 1 inf y(t, A) <

1 sup y(t, A),

Jmes(A) = (*inf y(t, A)— r sup y(^, A)) > 0.5

(1 sup y(t, A) — r inf y(t, A))

x . , , „ . . where Jmes(A) is a global measure of jumpiness.

(jump down), where jmes(A) is a local measure of jumpi- - ' i f

As a result of application of two algorithms given above

7 spikes and 2 jumps were recognized on preliminary data

magnetogram recorded at BOU observatory (Boulder, Col-

If an anomaly has passed the local test and contains a

jump it is tested for stability in a more global scale in the

.£ ui* orado, USA) in the time period of 01.01.2007-31.12.2007

same way using fuzzy bounds. A - parameter of global ob- , . , , , . .

servation; t - point of local jump A = y|[a,b].

l inf y(t, A), l sup y(t, A) - fuzzy bounds of record y on [a — A, t].

which corresponds completely with results of visual data control.

Baseline drift. Suppose that at time t algorithm

r inf y(t, A), r sup y(t, A) - fuzzy bounds of record y on FCARS [Gvishiani, 2008] has recognized an anomaly At =

[t, b + A].

y|[c(t),d(t)], d(t) < t on a record y|Tw, T(t) = {i e T : t < t}.

Figure 6. Anomaly on the right has passed the test.

or

152 153 154 155 156 157 Days

Figure 7. Example of drift recognition.

If anomaly At is “fresh” enough: t — d(t) < A then we wait for a time moment d(t)+A to determine whether At contains a jump J(Ai) = [a(t),b(t)j. Suppose that it does. But jump is accompanied with a record baseline shift. A question of the beginning (origin) of such shift arises.

We search it with a loop over f to leftward from the beginning of jump c(t). Thus, f < c(t) is proposed as the beginning of a shift ended with a jump y|[a(t),b(t)]. Further using FCARS algorithm we study docking between fragments y|[t_A,t] and At = y|(d(t),d(t)+A]. Docking takes place (point f is “good”) if rectification Fg(f A) of union y = y|[f-A,t]u(d(t),d(t)+A] at docking position [f,d(t) + h] is vertically background:

Fy(f A) < as

where as is a strong level of vertical anomality in FCARS algorithm. Point t* + h where t* is the closest to c(t) “good” point is considered as the beginning of a shift. Therefore, a shift itself represents a record fragment y| [t*+h,a(t)) (Figure 7).

In total, 30 INTERMAGNET magnetograms were processed; 100% of spikes, jumps and drifts recognized visually were recognized by the algorithms given above as well. All of the magnetograms processed represent preliminary data records. The list of the records processed with registration time periods indicated is given below:

AIA observatory:

1. F component, 01/01/2007-31/12/2007

2. X component, 01/01/2007-31/12/2007

3. Y component, 01/01/2007-31/12/2007

4. Z component, 01/01/2007-31/12/2007

BOU observatory:

5. F component, 01/01/2007-31/12/2007

6. X component, 01/01/2007-31/12/2007

7. Y component, 01/01/2007-31/12/2007

8. Y component, 22/04/2007-18/05/2007

9. Y component, 14/08/2007-09/09/2007

10. Z component, 01/01/2007-31/12/2007 GUA observatory:

11. F component, 01/01/2007-31/12/2007

12. X component, 01/01/2007-31/12/2007

13. Y component, 01/01/2007-31/12/2007

14. Z component, 01/01/2007-31/12/2007

NVS observatory:

15. F component, 01/01/2007-31/12/2007

16. X component, 01/01/2007-31/12/2007

17. Y component, 01/01/2007-31/12/2007

18. Z component, 01/01/2007-31/12/2007

PPT observatory:

19. F component, 01/01/2007-31/12/2007

20. X component, 01/01/2007-31/12/2007

21. Y component, 01/01/2007-31/12/2007

22. Z component, 01/01/2007-31/12/2007

SOD observatory:

23. F component, 01/01/2007-31/12/2007

24. X component, 01/01/2007-31/12/2007

25. Y component, 01/01/2007-31/12/2007

26. Z component, 01/01/2007-31/12/2007

TAM observatory:

27. F component, 01/01/2007-31/12/2007

28. X component, 01/01/2007-31/12/2007

29. Y component, 01/01/2007-31/12/2007

30. Z component, 01/01/2007-31/12/2007

The records contain a few number of baseline drifts therefore additionally 13 synthetic examples containing drifts of different shapes were generated. All of them were recognized by the corresponding algorithm.

Acknowledgment. The results presented in this paper rely on data collected at magnetic observatories. We thank the national institutes that support them and INTERMAGNET for promoting high standards of magnetic observatory practice. (http:// www.intermagnet.org)

References

Gvishiani, A. D., S. M. Agayan, Sh. R. Bogoutdinov (2008), Fuzzy recognition of anomalies in time series, Dokl. Earth Sci., 421(5), 838-842.

Kerridge, D. (2001), Intermagnet: worldwide near-real-time geomagnetic observatory data, Proceedings of the Workshop on Space Weather, BGS, Edinburgh, UK.

Kolmogorov, A. N., S. V. Fomin (1981), Elements of Func-

tion Theory and Functional Analysis, 5-th ed. (in Russian), Nauka, Moscow.

S. M. Agayan, Sh. R. Bogoutdinov, A. D. Gvishiani, and A. A. Soloviev, Institution of Russian Academy of Sciences Geophysical Center, 3 Molodezhnaya Str., 119296 Moscow, Russia. (a.soloviev@gcras.ru)

E. Kihn, National Geophysical Data Center (NOAA’s NGDC), Boulder, Colorado, USA

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