Design and Research of Electronic Circuit Fault Diagnosis Based on Artificial Intelligence

Design and Research of Electronic Circuit Fault Diagnosis Based on Artificial Intelligence Zhenyu Yang *, Ranran Yin Anhui Communications Vocational & Technical College, Hefei 230051, Anhui Province, China *Corresponding author(e-mail: yangzyrr@163.com) Abstract Electronic circuit fault technology is a kind of circuit problem detection and location system, which is designed by constructing theory and object-oriented modularization with the support of computer aided system. The electronic circuit fault system based on artificial intelligence can realize the system, programming, test and maintenance of electronic circuits. This paper focuses on the system design method of electronic circuit fault system based on artificial intelligence, architecture design, function module design and user interface design of artificial intelligence electronic circuit fault system. The given fault system based on artificial intelligence has the functions of fault, simulation and test and fault library generation. The electronic circuit fault system can meet the rapid of common electronic circuits. Key words: Artificial intelligence; Electronic circuit; Fault 1. INTRODUCTION With the development of modern artificial intelligence technology, intelligent has made unprecedented progress in electronic circuits. Today, artificial intelligence is more than just a discipline, and it has become a new and effective tool for use in the field of intelligent. The application of artificial intelligence-assisted electronic circuit fault brings infinite vigor and vitality to circuit field. It makes today's circuit fault method, circuit fault method, circuit fault concept, circuit fault form and circuit fault structure. Therefore both the circuit fault theory and the circuit fault theory have undergone profound changes. It plays a decisive role in the construction and creation of new circuit fault methods and modes, and is conducive to the modernization of modern circuit and the trend of information technology to quality. Artificial intelligence-assisted electronic circuit fault is referred to CAI, and artificial intelligence-based circuit is referred to bit CBE, and CAI and CBE are artificial intelligence widely used in the field of circuit. Among these applications, artificial intelligence mainly plays two roles. One is to use artificial intelligence as a goal, and the other is to use artificial intelligence as a tool. Electronic circuit fault is referred to CMI, which refers to the use of artificial intelligence to guide the entire electronic circuit fault process of electronic circuit fault management system, including the organization of diagnostic processes and fault data, monitoring the specific diagnostic process, evaluating the diagnostic results and providing people with diagnostic information. Based on artificial intelligence technology referred to CAL, CBL and CAI basically the same meaning, it is used more in Europe. In addition, CBI also can express the same meaning. It is generally believed that CAI and CMI are two important sub-components of CBE. In practice, it is rare to use CAI or CMI alone and often together with CAI in CMI and CMI in CAI. Therefore, people often use CAI and CMI together. Therefore, this paper will no longer distinguish between the two and only use the term of CAI. This paper gives a more accurate definition of CAI. In a broad sense, CAI refers to the widespread use of artificial intelligence in the field of circuit diagnostics, including the use of artificial intelligence in a variety of ways in electronic circuit troubleshooting, research and management. In a narrow sense, CAI refers to people's use of artificial intelligence as a fault medium for electronic circuits to provide a diagnostic environment for fault. Fault is an electronic circuit fault form that is diagnosed through interaction with artificial intelligence. 2. ELECTRONIC CIRCUIT FAULT DIAGNOSIS TECHNOLOGY BASED ON ARTIFICIAL INTELLIGENCE 2.1. Traditional electronic circuit fault The traditional electronic circuit fault process is a cyclic process, which starts by determining the fault point of the circuit as the initial target and the general process, and ends with evaluating the. The basic process is shown in Figure 1. From the traditional electronic circuit fault process, it is not difficult to see that the electronic circuit fault process is a two-way communication process. 766

Circuit Fault target item selection evaluation Send behavior design Figure 1. Basic process for traditional electronic circuit fault 2.2. Electronic circuit fault principle based on artificial intelligence Electronic circuit fault based on artificial intelligence process is a fault and artificial intelligence interaction, and the gradual process of fault and the basic principle is shown in Figure 2. New diagnostic items Start information Diagnose the problem judgment strategy Choose content Receive information feedback Receive feedback End Figure 2. Basic principle of artificial intelligence-assisted electronic circuit fault It is not difficult to see from the basic principle of artificial intelligence-assisted electronic circuit fault that artificial intelligence plays an irreplaceable auxiliary role in this process. Artificial intelligence circuit fault and traditional electronic circuit fault should complement each other, artificial intelligence-assisted electronic circuit fault as a modern electronic circuit fault means, with clever ideas, the image demonstration, and leading electronic circuit fault into a brand new realm. It can use artificial intelligence to dynamically display the whole process of the development or reasoning of things, and use its drawing features to abstract the materialization of things, the visualization of fuzzy things and the practical theoretical things. At the same time, artificial intelligence auxiliary electronic circuit fault can only be used in irreplaceable occasions, or traditional electronic circuit fault cannot be realized under the conditions of value. In order to achieve the best electronic circuit fault, artificial intelligence-assisted electronic circuit fault must be properly used, and traditional electronic circuit 767

fault should also be used just right, and it is best to combine them organically to make up for each other's deficiencies, and play each other's advantage is our ultimate goal. 2.3. Design principles of artificial intelligence-based electronic circuit fault The preparation of electronic circuit fault software must use the specified tools. However, there are many circuit diagnostics that give the wrong direction to fault of artificial intelligence-assisted electronic circuits. We should select the appropriate software to choose the electronic circuit fault software. Artificial intelligence-assisted electronic circuit fault and new media electronic circuit fault confused, and even put them with the modern electronic circuit fault and electrochemical circuit equal, and even worse, artificial intelligence-assisted electronic circuit fault also known as the whole This is clearly unscientific media intermediary fault. In fact, they are different concepts, and have their own unique connotation and a very strict distinction. Artificial intelligence can assist electronic circuit fault generation and development of a very short time, and are still in the exploration, experiment and research stage. How should we deal with the problem of artificial intelligence-assisted electronic circuit fault scientifically? There are various opinions as well. In the face of different specific applications, we should avoid weaknesses as soon as possible to explore a suitable electronic circuit fault mode, and try to find the correct direction of fault of artificial intelligence-assisted electronic circuits as soon as possible. Based on the above, we think the design of artificial intelligence-assisted electronic circuit fault is a good way to solve the problem. Artificial intelligence-assisted electronic circuit fault application software is the designer of the electronic circuit fault idea, and the compiler by a certain idea is designed as coherent, systematic software. Electronic circuit fault purposes, content and the realization of the electronic circuit fault activities all belong to the electronic circuit fault strategy, including electronic circuit fault sequence, control methods. 3. ELECTRONIC CIRCUIT FAULT DIAGNOSIS DESIGN METHOD BASED ON ARTIFICIAL INTELLIGENCE 3.1. Electronic circuit fault design process based on artificial intelligence The electronic circuit diagnostic engineering shows that the artificial intelligence-based electronic circuit fault design process is the application of a life cycle of system design, and application system life cycle model generally includes the following components, including system, system design, system programming, system testing and system maintenance. The relationship between them and the process is shown in Figure 3. design Diagnosis becomes test Diagnosis and maintenance Demand Circuit structure code Unit Functional Expected Functional structure module Integrated Performance Figure 3.Design model of artificial intelligence-based electronic circuit fault 3.2. Electronic circuit fault design process based on artificial intelligence Artificial intelligence-based electronic circuit fault design is based on the following plans and quality. The artificial intelligence-based electronic circuit fault process provides a standardized maintenance process framework for maintenance personnel to carry out software development. The fault process consists of a series of methods, and fault description to guide maintenance personnel, and measure to manage the repair work. At the same time, it shows how to define the repair process, and how to measure its maintenance quality and maintenance efficiency. Fault process structure is shown in Figure 4. 768

Figure 4. Fault process structure The first step in the troubleshooting of an electronic circuit is to diagnose the fault. The troubleshooting plan describes the work and at the same time diagnoses the plan data related to the plan summary record. When maintenance personnel work in accordance with the fault description, they record the time and fault data to the time and fault log. At the end of the diagnostic work in the post- phase, they measure the program size based on the log summary time and fault data and record these fault data into the summary fault list. 3.3. Structure design for artificial intelligence electronic circuit fault The artificial intelligence electronic circuit fault structure refers to the overall architecture that the software system exists, which is the design basis of the software system. Artificial intelligence electronic circuit fault structure design is shown in Figure 5. fxjss is on behalf of the and calculation of fault system, and zldl is on behalf of the DC circuit fault module, and gdgc is on behalf of the transition process fault module, and zxdl is on behalf of the sine circuit fault module, and qtdl is on behalf of other circuit fault module. Analysis and Calculation of the fault system mainly provides the operation of analyzing and calculating the related modules. fzsys is on behalf of simulation test fault system, and dzdl is on behalf of resistance circuit fault module, and yjdl is on behalf of the first-order circuit fault module, and sdkn is on behalf of dual-port network fault module, and sxdl is on behalf of three-phase circuit fault module. While sjscs is on behalf of the fault library generation system, and mscp is on behalf of the manual generation of fault library module, and ascp is on behalf of the automatic generation of fault library module. Fault library generation system can provide the main completion of the relevant module fault library generation tasks. While bztss is on behalf of help troubleshooting system, and bzzt is on behalf of thematic troubleshooting module. Help tips Fault system mainly provides system help to complete all the software diagnostic operations. 769

Figure 5. Structure design of artificial intelligence electronic circuit fault 3.4. Module design of artificial intelligence circuit fault software Fault function module design is the first item. Circuit artificial intelligence auxiliary electronic circuit fault application software is to analyze and calculate the basic idea of the module design, and to establish the mathematical model of circuit components, and then determine the corresponding mathematical algorithm to solve. Circuit components refer to resistors, capacitors, inductors, voltage sources and current sources. Fault simulation experiment function module is the second design. To complete the design of simulation experiment function module, we must first understand several concepts in virtual simulation, including electronic circuit fault simulation method, virtual diagnostic environment method and virtual laboratory. (1) Electronic circuit fault simulation method: Electronic circuit fault simulation is the use of artificial intelligence modeling and simulation technology to represent the structure and dynamics of some systems, providing an environment for fault for them to experience and observe. The use of electronic circuit fault simulation means to achieve the effect of electronic circuit fault method, which is called electronic circuit fault simulation. AI simulation allows fault to observe changes in the system by changing the size of the input data. Electronic circuit fault simulation software provides a non-dangerous, inexpensive way to troubleshoot and interact with the real world. We can get different results by manipulating the model elements and changing the different variables of the model. (2) Fault environment method: fault environment refers to the environment constructed by using the multimedia communication technology in the artificial intelligence network, allowing people and the fault to see each other and hear each other, and not only can the traditional environment be realized by using the real-time communication function. In most of the electronic circuit, fault activities can be carried out and use asynchronous communication capabilities to achieve unprecedented electronic circuit fault activities. It uses a networked diagnostic tool to diagnose spatial MUD, and object-oriented diagnostic space MOO supports asynchronous diagnostic communication form, and MOO is an object-oriented MUD, and it is through the core of a variety of MOO object diagnostic database to the way Users provide intelligent diagnostic environment. MOO provides real-time online communication, introducing the concept of metaphor for spatial diagnostics, so that diagnostic processes that are physically separated can interact and collaborate within a common mechanism. A circuit diagnostics MOO has a diagnostic target that utilizes various communication tools provided by MOO. The intelligent network platform is mainly a set of complete intelligent system developed for the fault of the network electronic circuits in the advanced mode of complex circuits. It integrates more than 40 modules such as active programs, virtual devices and intelligent testing to meet the actual needs of most electronic circuit fault. (3) Virtual Laboratory: virtual diagnostic laboratory is actually the use of intelligent diagnostic technology to simulate or fictitious situations for fault to observe, manipulate, and construct the circuit object, and the process can gain experience or discovery. For example, a software system using EWB's electronic diagnostic workbench allows diagnosticians to use the components, and it provides to construct a variety of analog and digital circuits and to dynamically test circuit performance. 770

(4) Intelligent fault library: the key to designing the intelligent simulation experiment function module is to design the virtual laboratory. The construction of the virtual laboratory is the basis of realizing the intelligent simulation experiment function module. Based on the premise of fault of electronic circuit, the design of intelligent fault library is designed according to the purpose and scope of application of fault software of artificial intelligence circuit. At the same time, automatic fault library generation is well established for the establishment of circuit component database basis. 4. CONCLUSION The ultimate goal of electronic circuit fault design based on artificial intelligence is to be able to realize the basic electronic circuit fault, and apply it to the basic electronic circuit fault system. In this paper, based on artificial intelligence design of electronic circuit fault, function module has a good quality evaluation and is made as a good fault of electronic circuits. In the process of design, this paper preliminarily has designed the basic functions of electronic circuit fault system based on the needs of fault of electronic circuits, including fault and calculation functions, fault simulation and experimental functions and fault library generation functions, which can effectively solve the fault of electronic circuit fault A large number of complex computing faults can avoid the troublesome circuit troubleshooting process. The artificial intelligence fault library generation function of this paper can complete the operation of manually generating a fault library or automatically generating a fault library according to the specific requirements, so as to meet the requirements of the electronic circuit fault work. References Chen S, Wu M, Zhao S. Analog Circuit Fault Diagnosis Based on DE OS-ELM, Computer Applications in Engineering Education, 2015, 1:509-513. Cui Y, Shi J, Wang Z. An analytical model of electronic fault on extension of the dependency theory,reliability Engineering & System Safety, 2015, 133:192-202. Du Q, Zhao X, He W, et al. Research and development of intelligent based on neural network, IEEE International Conference on Electronic Measurement & Instruments. IEEE, 2016:187-191. Gayathri K, Kumarappan N. Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network,International Journal of Computational Intelligence Systems, 2015, 8(1):95-105. Grzechca D E. Construction of an Expert System Based on Fuzzy Logic for Diagnosis of Analog Electronic Circuits,International Journal of Electronics & Telecommunications, 2015, 61(1):77-82. Hou J, Wang Y, Gao T, et al. Fault feature extraction of power electronic circuits based on sparse decomposition, International Conference on Condition Monitoring and Diagnosis. IEEE, 2016:505-508. Kodavade D V. Knowledge based approach for fault in electronic circuit boards,international Journal of Control & Automation, 2016, 9. Li H, Niu G. A novel method for intelligent IETM platform based on cosine similarity and fuzzy semantic inference, Prognostics and System Health Management Conference. IEEE, 2017:1-6. Li M, Song D. Fault Diagnosis Method of Converter Circuit base on Wavelet Analyze and RF Algorithm,Electrical Engineering, 2016. Li M, Wang Y. Power electronic circuit soft fault methods comparative,electronic Measurement Technology, 2015. Li M. Research on Rapid Diagnosis of Agricultural Tractor Engine Based on Artificial Intelligence,Journal of Agricultural Mechanization Research, 2017. Meng P. Research on intelligent fault technology for power electronic circuits,wireless Internet Technology, 2017. Singh L. A new approach to Dedekinds theory of real numbers with special reference to the four fundamental operations, Test Conference, 1990. Proceedings. International. IEEE, 2015. Singh S, Joshi D. Computer applications in fault of power transformers - a review, International Conference on Computing for Sustainable Global Development. IEEE, 2015:1216-1223. Song Q, Zhai Y. Research on Fault Diagnosis of Power Electronic Circuits Based on LS-SVM,Mathematics in Practice & Theory, 2016. Sun Z, Jiang L, Xu Q, et al. On test syndrome merging for reasoning-based board-level functional fault, Design Automation Conference. IEEE, 2015:737-742. Wen C L, Fei-Ya L V, Bao Z J, et al. A Review of Data Driven-based Incipient Fault Diagnosis,Acta Automatica Sinica, 2016, 42(9):1285-1299. Ye F, Chakrabarty K, Zhang Z, et al. Self-learning and adaptive board-level functional fault, Design 771

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