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  i1 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 TМj 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. 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