=Paper=
{{Paper
|id=Vol-2590/short18
|storemode=property
|title=Method of Control and Diagnosis Integrated Systems and Communication Systems Based on Thermal Processes
|pdfUrl=https://ceur-ws.org/Vol-2590/short18.pdf
|volume=Vol-2590
|authors=Vladimir Goydenko,Vladimir Goncharenko,Nina Zhuravleva
|dblpUrl=https://dblp.org/rec/conf/micsecs/GoydenkoGZ19
}}
==Method of Control and Diagnosis Integrated Systems and Communication Systems Based on Thermal Processes==
https://ceur-ws.org/Vol-2590/short18.pdf
Method of Сontrol and Diagnosis Integrated Systems and
Communication Systems Based on Thermal Processes
Vladimir Goydenko1[0000-0002-4983-0078], Vladimir Goncharenko2,3[0000-0002-1667-1197],
Nina Zhuravleva3[0000-0002-6561-3153]
1 Military academy of telecommunications named after Marshal of the Soviet Union
S. M. Budyonny, St. Petersburg, Russia
lglvl@ya.ru
2 Institute of Control Sciences. V.A. Trapeznikova, RAS, Moscow, Russia
vladimirgonch@mail.ru
3 Moscow Aviation Institute – National Research University, Moscow, Russia
fvo@mai.ru
Abstract. The significant number of failures in modern software and hardware
communication systems of large integrated systems are associated with thermal
conditions changes. The effective method of control and diagnosis software and
hardware communication systems is thermal control of electronic modules ele-
ments. The system of control should allow detect hidden defects in software and
hardware communication systems, substitute nature modeling defects to pro-
gram simulation for decrease time of creating state base. The subject of investi-
gation is control of technical condition software and hardware communication
systems in working state real time. The purpose of investigation is increasing
efficiency of technical control in working state. For development of thermal
control instruments are used methods: to modelling thermal processes in elec-
tronic modules of software and hardware communication system is used finite
difference method, to processing thermogram is used wavelet-analysis. For ex-
perimental verification obtained results is choosed electronic module of modern
hardware and software systems, for which created state base by developed
model and compared results of recognition technical condition with developed
methodic and without. As a result of investigation were developed thermal
model of hardware and soft-ware communication systems, the methodic of
recognition abnormal states electronic modules based on wavelet-transforms
and the algorithm creating state base soft-ware and hardware communication
systems. Based on the obtained results is develop technical proposals that will
improve efficiency of determining the technical condition and realize the possi-
bility of preventing failures.
Keywords. Aerospace systems Large integrated systems Software hardware
communication complex Wavelet analysis.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
The reported study was partially supported by RFBR, research project
no. 16-29-04326 ofi_m.
2 V. Goydenko et al.
1 Introduction
During the operation of aerospace organizational and technical complexes [1], includ-
ing autonomous objects, such as robotic systems, autonomous space and underwater
vehicles, automated communication centers and radio centers, the urgency of creating
a method for their diagnosis and non-destructive testing of technical status is increas-
ing [1-5]. Knowledge of the actual technical condition is necessary when making
managerial decisions at the organizational and technical level to ensure the function-
ing of critical infrastructure facilities. The decision of control and diagnosis task is
based on measuring thermal values electronic modules elements surfaces.
In result of control technical condition electronic modules (EM) of software and
hardware communication systems (SHCS) should recognized type of state (perfect
state, up state, down state, fault state) [1].
The nondestructive control on registration electromagnetic infrared radiation is ef-
fective and perspective method of control SHCS EM. Therefore, control task decision
is based on monitoring changing elements temperatures. The using of infrared radia-
tion is based on next factors:
from 70% to 80% energy in radio elements transforms in heat radiation;
a series of experiments shown the thermal control one of the most informative type
of control;
main reasons defects progress is deviation of radio elements heat conditions.
When thermal control of the technical condition of EM of SHCS of large integrat-
ed systems, the current state will be characterized by a matrix of temperature which
obtained by teplovision sensors. In investigation are used termograph testo 875-2 is
providing thermogram by size 120×160, it is allowing for measuring temperature of
controlled elements (Fig. 1).
Fig. 1. Thermogram image of EM SHCS in grayscale
3 V. Goydenko et al.
Since the boards are in different positions, it is necessary to segment the EM SHCS
in thermal images by geometric transformation and standardize the images of the
actual aspect ratio by affine transformation.
The resulting rectangular images are called EM SHCS thermograms [6]. The wave-
let transform method is an effective way of reducing the feature space and infor-
mation compression [7].
The analysis of different approaches to solving the problem of recognition of the
type of anomalous state of the thermal regime of SHCS is based on the wavelet trans-
form and shows that the problem is solved in the following formulation.
The initial data for solving the problem are:
the standard thermal behavior of SHCS information (internal parameters values for
different external conditions);
information characterizing the main types of SHCS states in the analysis of thermal
SHCS modes (tolerance intervals in different modes);
the studied measurement information obtained in the analysis of thermal conditions
of SHCS (different types of defects and failures);
frequency of obtaining temperature values of EM SHCS elements;
a set of orthogonal basis wavelet functions: daubechies, symlets, coiflets [7].
It is necessary to determine the type of anomalous state from the library of anomalous
states based on the results of evaluation of SHCS thermal behavior. The anomalous
state is understood as a deviation from the nominal mode of operation associated with
changes in external and internal factors.1
2 Using wavelet transforms to form a state base
Application of the wavelet transform in this paper is considered from the standpoint
of its use as a tool with which it is possible to obtain a feature space for subsequent
recognition. The choice of the discrete wavelet transform (DWT) for solving recogni-
tion and classification problems is due to the universality of the mathematical appa-
ratus of wavelet analysis, its ability to adapt to the signal form, the similarity of the
studied signals with basic functions (wavelets) [7].
The choice of the analyzing wavelet is largely determined by the information to be
extracted from the signal. Taking into account the characteristic features of different
wavelets in time and in frequency space, it is possible to identify in the analyzed sig-
nals certain properties and features that are invisible in the presence of strong noises.
When analyzing any signal, it is necessary, first of all, to choose the appropriate
basis, i.e. a system of functions that will play the role of “functional coordinates”.
However, the choice of the analyzing wavelet is not defined in advance. It should be
chosen according to the task to be solved. Simplicity of operation with wavelet and
presentation of the results (minimization of the used parameters) plays an important
role.
An unsuccessful choice of a specific form of wavelet can lead to the impossibility
of solving the problem or a high error, and, consequently, to an incorrect definition of
4 V. Goydenko et al.
the type of technical condition of the SHCS. Select the type of basis function from the
number used bases in the General case depends on the degree of adequacy of the
functions and selections. Quantitatively, the degree of optimal choice can be deter-
mined by the entropy criterion [7].
As a criterion for choosing the optimal decomposition basis, we take the Shannon
entropy criterion, which quantitatively characterizes the reliability of the transmitted
signal and is used to calculate the amount of information. The entropy determined by
Shannon's formula gives a criterion of how many effective components are needed to
represent a signal in a certain basis [3].
3 Modeling thermal processes in different technical states
Modern SHCS are multimode and multifunctional equipment, and the deviant of
tolerance level temperature in their can realized by changing functional mode (inner
and outer factors) and do not depend of technical condition.
To implement the pattern recognition capabilities of the technical condition of the
SHCS at the exit of the temperature values outside the tolerance intervals creates a
base state of many of the alleged effects of external factors and possible defects.
While conventionally, there are abnormal state (“pre-failure” and “failure”) [3, 6].
The base of anomalous states is a set of wavelet coefficients of SHCS thermograms
obtained by modeling, each of which corresponds to an anomalous state or defect.
The modeling of heat condition is realized stage-by-stage transition from up level
hierarchy with racks group and construction to down level with simplest elements
which inseparable elements [3].
First created heat processes models or macro model of studied construction.
The graphs nodes is geometric
centers of construction elements
Fig. 2. Length of thermal flow length between pair sides definition
The model construction is beginning at the finding graphs nodes. The next, nodes
associates between themselves to definition heat relation (Fig.2).
5 V. Goydenko et al.
5 2 2 5 5
2
1 9 6
1 6
1 6
3 4 3 3 4 4
4
2 2 5 5
8 8
7 1 8 6
4 4
1 6
8 8 3 3 4 4
4
Fig. 3. Topologic model of SHCS in case
The Fig. 3 are indicated by numbers: 1 – left side case, 2 – up side case, 3 – for-
ward side case, 4 – down side case, 5 – backward side case, 6 – right side case, 7 –
electronic module, 8 – air inside, 9 – air outside.
Between sides are defined conditions of heat exchange size characterized heat flow
cross-sectional area, length way of heat flow, thermal conductivity of material.
Heat exchange with the environment is characterized by natural convection from a
flat surface into the environment and radiation from an undeveloped surface. The heat
exchange of the board with the air inside the case is determined by the conditions of
radiation and convection from a flat undeveloped surface. The parameters are setting
surface length, surface width (height), surface orientation, environmental pressure.
The heat exchange of the board with the air inside the case is determined by the
conditions of radiation and convection from a flat undeveloped surface. Since the EM
is solve this problem, the equation of similarity and heat transfer equation, the method
of nodal potentials for the formation of a mathematical model of heat processes in the
form of a system of ordinary differential equations or a system of nonlinear algebraic
equations are used [4]. Located on the lower wall of the housing, the conductive heat
exchange of the EM with the lower wall of the housing is specified.
Based on the topological model (Fig. 3), a system of equations is formed and cal-
culated:
6 V. Goydenko et al.
T1 T2 T1 T3 T1 T5 T1 T4
P1 (T1 );
R12 R13 R15 R14
T2 T1 T2 T5 T2 T3 T2 T6
P2 (T2 );
R21 R25 R23 R26
T T T T T T T T
3 1 3 4 3 2 3 6 P3 (T3 );
RT31 R34 R32 R36
T4 T3 T4 T6 T4 T1 T4 T5 P (T );
R43 R46 R41 R45
4 4
T5 T2 T5 T6 T5 T1 T5 T4
P5 (T5 ); (1)
R52 R56 R51 R54
T T T T T T T T
6 5 6 4 6 2 6 3 P6 (T6 );
R65 R64 R62 R63
T T T T T T
4 7 4 2 7 8 2 7 rad 8 P7 (T7 );
R74 R78 R78
7
T T 7
T
8 i 8 i P (T ); T
i1 R8rad i 1 R8i
conv 8 8
7
i
T9 Ti T9 Ti P (T ).
7
rad conv 9 9
i 1 R9i i 1 R9 i
where Pi (Ti ) – heat power of element i, Rij – heat resistance between i and j ele-
ments.
To solve this problem, the critical equations of similarity theory and heat transfer
equation, the method of nodal potentials for the formation of a mathematical model of
thermal processes in the form of a system of ordinary differential equations or a sys-
tem of nonlinear algebraic equations are used [12].
Unlike other types of models, topological models of thermal processes allow us to
set boundary conditions of various kinds [13] and their combinations in terms of vol-
umes and surfaces of the SHCS structure using the appropriate graph components
(branches, sources of a given temperature and (or) sources with preset thermal power).
Any thermogram of the state base is formed as follows: a change is made in the
mathematical model of the SHCS, which corresponds to a defect or abnormal state,
then a thermogram is obtained that reflects this state. Therefore, the resulting thermo-
gram of such a modified model will correspond to the state of the SHCS, in which
there is coincident defect, in this element. After that, the wavelet transform is per-
formed and the resulting wavelet coefficients are preserved. In this way, the wavelet
coefficients for all defects inherent in this SHCS are obtained. Modeling of thermal
processes SHCS performed using computer-aided design, feeding the input model
SHCS, and the output, receiving a thermogram or temperature values of the elements
[4]. Then the wavelet transform of the obtained thermogram is performed to preserve
the wavelet coefficients. To creation state base disigned next sequence of steps:
Step 1. Taking into account the operating conditions and the impact of external fac-
tors make a list of parameters for different states.
Step 2. Parameters for states with different types of defects are determined on the
basis of failure statistics.
7 V. Goydenko et al.
Step 3. Based on the generated parameters simulation of thermal conditions of
SHCS is performed.
Step 4. Wavelet transform of the simulated thermogram of the technical state, re-
duction of the characteristic space.
Step 5. The obtained wavelet coefficients for the simulated anomalous state are
stored in the state base.
Step 6. If all data according to the list of abnormal States and defects is stored, then
proceed to the step 8.
Step 7. Conclusion of information that the modeling of anomalous states is com-
pleted.
Thus, looking through the list of anomalous states (step 3-7) peculiar to this SHCS,
we obtain a set of wavelet coefficients for each state [5-10]. The element of the list of
states (2) q Fj consists of: a) the serial number of the element in the SHCS, b) what
parameters reflect the defect and how to change them.
Q F (q F 1 qF j q F n ), (2)
where QF – many defects of the controlled SHCS (list of defects), q F j – a specific
defect of a given SHCS element.
The set of wavelet coefficients of thermograms С(Rм) SHCS is resulted (3), each of
which corresponds to one of the anomalous states
C(RМ ) (C(RМ
1
), C(RМn ), C( RМnorm )), (3)
where C(RМn ) – wavelet coefficients of the thermogram obtained in the simulation of
thermal processes SHCS, which corresponds to the anomalous state with the parame-
ters q Fj .
When using the state base to recognize the state of SHCS, the wavelet coefficients
from the state base are compared with the wavelet coefficients of the currently ob-
tained SHCS thermogram.
In the general case, the database state SHCS produced by the experimental studies
(by conducting a production test of the control object (prototype products) experimen-
tally) at the factory. Select one sample with the closest indicators to the ideal or sev-
eral samples. Next, a defect from the list of defects QF (3) is introduced into the sam-
ple and temperatures are measured in a stationary mode.
Eliminate the defect, bringing the sample to its original state, then carry out the
same with other defects of QF (2) etc.
The result is an expression (4), not simulated but experimental temperature. Such
acquisition of the state base is very difficult. Therefore, the proposed method using
modeling and wavelet transform is beneficial, given the fact that modern computers
can easily cope with the problem of modeling and calculation of wavelet coefficients.
TМ (TМ1 , TМn , TМnorm ), (4)
8 V. Goydenko et al.
where TМn – consists of n sets. Each j-th set TМj corresponds to a manufacturing de-
fect q Fj , where j 1, n 1 .
TМj (TМ1
j j
, TМ2 , , TМj k ), (5)
j
where TМ2 – the temperature in the element 2 obtained from the simulated images
RМj , which corresponds defect with number j in list (1).
Generally, the thermal control of electronic modules of software and hardware
communication systems of large integrated systems. Recognizing of state carried out
by comparison of сcurrent state wavelet coefficients and states signatures wavelet coef-
ficients of state base (Fig. 4). The graph is built for state base are including thermo-
grams of electronic modules be size 160×120 mm and their wavelet-coefficients.
20000
База состояний из теплограмм
Volume of state base, Kbyte
18000 With wavelet-transform
16000
База состояний
Without из вейвлет
wavelet-transform
14000
коэффициентов
12000
10000
8000
6000
4000
2000
0
1 100 500 1000
Number of states signatures
Fig. 4. Volume of state base with wavelet-transform and without wavelet transform.
4 Conclusion
Thus, the results of investigation are shown that using designed method provides at
first increase the reliability of the identification results because in existing system of
control is add thermal control, at second increase sensitivity to the detection of emer-
gency situations, because appear facility of detecting gradual failure, thirdly increase
sensitivity to the detection of emergency situations, also increase state recognition
speed and decrease volume of state base.
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