Diagnostic Expert System Research Articles

The Analysis of Water Supply Operating Conditions Systems by Means of Empirical Exponents

The stochastic character of water consumption by consumers and the technical condition of water supply systems are the main deterministic random factors influencing the observed changes in flow rate and pressure. The implementation of Supervisory Control and Data Acquisition (SCADA) systems resulted in the creation of dispersed data sets coming from the devices controlling the operation of the water supply system. Thanks to the use of metadata and advanced computer systems of analysis, data from various sources can be analyzed to detect the operating conditions of the water supply system. The aim of the research was to analyze an empirical exponent, determined on the basis of flow rate and pressure measurements for one of the District Metered Areas (DMAs). Modern supervised and unsupervised machine learning systems were implemented to classify the obtained results. The results of the research showed that on the basis of the established empirical exponent in the systems in which the pressure is reduced at night, it is possible to qualify the operating conditions of the water supply system in the DMA with accuracy of up to 90%. The conducted tests may be implemented as a component of expert diagnostic systems in water companies.

Digital diagnostic complex for power turbine units equipment

The study is devoted to the problem formulation for creation a concept of digital complex for power turbine equipment diagnostics using a reference turbine model formed by a set of computational models of thermal and gas-dynamic processes, by diagnostic expert systems (probabilistic type and decision tree type), by functional standard characteristics and statistical models. As part of the study it is proposed to develop diagnostic methods and algorithms for characteristic malfunctions causes for various subsystems of power equipment, which will allow them to be detected at an early stage and to prevent unplanned (emergency) equipment shutdown.

RANCANG BANGUN APLIKASI SISTEM DIAGNOSIS KERUSAKAN PADA MOBIL DAIHATSU XENIA 1.3 M/T DENGAN METODE FORWARD CHAINING

Cars have properties like humans. The car will show certain signs before it breaks. Corrective action is taken when the symptoms occur.The problem faced is that many car Users who do not know it or may know only a little and not everyone knows about it in repairing the car.The purpose of writing this thesis report is to help detect damage to Daihatsu Xenia 1.3 M / T cars as an alternative solution for vehicle Users in shortening decision making time.Research methods conducted by the author in conducting research at PT. ArmadaAuto Tara. Fleet includes direct observation of the current diagnostic system, interviews with the head of mechanics and mechanics and literature studies that are relevant to the problems faced by the author. This study uses the Forward Chaining method.The results of this study can be seen that with the application of an expert diagnostic system this car damage can help the User get information about the damage experienced in his car without having to come to the workshop or to the expert.

Application of Fault Diagnosis Expert System for Unmanned Vehicle Safety

In order to ensure the safety of the underwater unmanned vehicle (UUV) and reduce the risk of damage or loss during operation, the safety system will be equipped on the UUV. It can accurately locate the fault when the submersible encounters dangerous situations, such as equipment failure and cabin flooding, assess the dangerous situation and make emergency measures, so as to help the underwater unmanned submersible realize self-rescue. Fault diagnosis expert system is a knowledge-based fault diagnosis method, which is suitable for complex systems with incomplete knowledge. However, because the working environment of the underwater unmanned underwater vehicle is very complex and the fault types are diverse and change in real time, the traditional fault diagnosis expert system may not meet the response time requirements of the safety system due to the long response time required. Therefore, the finite state machine technology is applied to the diagnostic expert system to improve the response speed of the diagnostic expert system, and at the same time make the system more flexible and convenient to expand its functions. In addition, the model-based design method was applied to establish the model of the security system in stateflow, and the model verification was carried out. With the help of PLC Coder tool, the code of the target controller was generated and the algorithm was deployed.

DEVELOPMENT OF A METHOD FOR ASSESSING LEARNING OUTCOMES THROUGH AUTOMATED TESTING MANAGEMENT

Testing knowledge is an important part of the pedagogical process, as it helps to improve the quality of professional knowledge, contributes to the effectiveness and efficiency of training. This article discusses the use of several approaches to building a fuzzy diagnostic expert system for knowledge testing. The aim of the research is a comprehensive optimisation of the process of mass test information processing in order to obtain reporting data, consideration of clear and fuzzy tests, drawing the analogy between clear and fuzzy tests, proposal and justification of different schemes of summing up the testingandproposal of one's own prototype of automated expert testing system which covers all stages of the testing process. Generally speaking, testing, as an algorithmic problem, can be represented as a problem of building an expert system to evaluate knowledge of testees. Such systems are used in solving many problems related to effective management, scientific research, and the like, where it is necessary to get the result with a limited amount of reliable information. It is established that the classical theory of tests has a number of controversial assumptions, and the results of its application have serious practical drawbacks. Therefore, the main principle of the approach in the work is the separation of the creation of a bank of test tasks and the process of compiling tests (bank of test material) and testing.

Development of an Experimental System "Computer-Aided Diagnosis in Neurotology (CADINO)" for Vertigo.

Computer-based medical diagnosis expert systems, when used by clinicians, familiar with the limitations of the system, were found both accurate and educationally helpful. Vertigo is one of the most common complaints of the patients and the specialists who manage these patients are not easily available. Diagnostic expert system can help clinicians including general practitioners in providing reasonable diagnostic suggestions while dealing with patients with vertigo/dizziness when the specialist doctors are not available. To provide an understanding about the method of making an expert system "Computer-Aided Diagnosis in Neurotology (CADINO)" for dizzy patients and highlight its potential uses. CADINO, developed during this original research project, is an expert computer program which is capable of making diagnoses in patients with vertigo. CADINO includes more than 100 causes of vertigo. It was developed in Microsoft Office using hyperlinks. CADINO has the potential of making diagnoses in patients with vertigo. It differs from most other programs for computer-assisted diagnosis in the generality of its approach and knowledge base. Specific deficiencies of CADINO include its inability to explain its thinking. CADINO can broaden the clinicians' scope and awareness regarding the findings which are important for making the differential diagnoses in patients with vertigo/dizziness. Though the CADINO on its face value seems to improve patient safety and quality of care by enhancing knowledge and cognitive skills of the clinicians, yet it remains a research tool and will need clinical evaluation in terms of diagnostic accuracy.

FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

Android-Based Dental and Oral Health Diagnostic Expert System

Dental and oral health is one of the most crucial thing for human health. If it is taken cared properly then all foods will be absorbed optimaly and human body will have all the nutritions needed. Otherwise, if it is not taken cared properly then it will affects health condition of a human body. Lack of awareness of Indonesians about their dental and oral health had caused many of infection cases which leads to teeth structural damage. In 2013, Health Research and Development Agency had done a survey based on interview’s result. They found that 25,9 percent of people in Indonesia were having issues with their dental and oral health in the last 12 months. Among them, there was 68,8 percent people who had not taken an treatments. To resolve this problem, expert system with android platform is made to diagnose dental and oral diseases. The research methodology used is forward chaining method. Expert System Shell used is CLIPS which then adopted into Android application.

THE BULDING A KNOWLEDGE BASE AND ITS INTERNAL RULES FOR AN EXPERT SYSTEM FOR DIAGNOSTIES A GAS PUMPING UNIT

The article proposes the development of an expert diagnostic system, namely, the construction of a knowledge base and internal rules for assessing the condition of the gas pumping unit by the parameters of vibration. The choice of environment for the implementation of the expert system was carried out. On the basis of the performed researches of failures and defects of the gas pumping unit, the method of diagnosing those defects were proposed, and also the conditions to determine the normal state of the functioning of the installation were formulated. When developing the expert system, a general structure, the construction of a knowledge database, and the construction of internal rules for the diagnosis of the gas pumping unit were proposed. The rules of construction of the expert systems with variable values ​​were considered. The determination of the technical condition of the unit using vibration parameters is made both by the content of the relevant information at the time, and on the basis of analysis of its change over the time. An expert system was offered that provides an analysis of transitional states of control objects, also describes the knowledge base and the number of rules that are used for conclusions of the expert system.

Application and prospect of artificial intelligence in cancer diagnosis and treatment

Artificial intelligence is a man-made device that simulates intellectual work such as human thinking and judgment. There are many successful applications of artificial intelligence in the fields of medicine, healthcare, agriculture, education, finance, service, news and machinery. Intelligent computer hospital management systems, intelligent medical diagnostic expert systems and intelligent surgical robots have revolutionized the medical field. Therefore, the attentions which are paid to artificial intelligence in cancer diagnosis and cancer treatment are on the rise. Some breakthroughs have also been made in the application of imaging diagnosis, pathological diagnosis, oncology surgery, oncology radiotherapy, drug development and cancer screening. Artificial intelligence is gradually changing the traditional medical model, which is a future direction and trend of human medical development. Here, we delineate the concept and development of artificial intelligence, and its application and prospect in the diagnosis and treatment of malignant tumors.

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

The Survey of CNN-based Cancer Diagnosis System

This thesis summarizes the structure and image processing methods of miniature robot from the perspectives of miniature diagnostic robot, CSF (CNN-SVM-FCN) and expert systems, in which it highlights the key technologies and the latest research progress in various aspects, analyzes the advantages and disadvantages of each method, elaborates their respective implementation processes and system framework. It also further explores ways to promote their complementary advantages and makes a prediction of the possible opportunities brought by the combination of these three methods.

Development of Android-based Rabbit Disease Expert System

Many rabbit keepers or breeders are panics when their rabbit has an illness. This paper proposed an expert diagnostic system application for Android-based rabbit disease using the Naïve Bayes method to determine the illness and Certainty Factor for the trust value of the condition by combining the rate of the trust of users and experts due to diagnose the diseases of the rabbit.The testing was using 65 data learning and 160 data learning to test the naïve Bayes method. Furthermore, the certainty factor is using CF user 1 and its variation.The results obtained for 65 data learning is 53%, while 160 data learning is 73%. With the naïve Bayes method, it can be concluded that the more data learning, the better and more accurate the system. The results of conformity with the testing data obtained from the variative CF user value, namely 53% accordingly, 13% inappropriate, 33% near. The effect of compliance with the sample data collected from the CF value of user 1 is 53% appropriate, 7% inappropriate, 40% is near. With the certainty factor method, it can be concluded that differences in user input values affect the overall CF value.

Database of Expert Diagnostic System in the Field of Turbine Blades Non-Destructive Testing

Database of Expert Diagnostic System in the Field of Turbine Blades Non-Destructive Testing

Integration of Lean Six Sigma and Ergonomics: A Proposed Model Combining Mura Waste and a RULA Tool to Examine Assembly Workstations

Lean Six Sigma and Ergonomics have great importance in the implementation of continuous improvement. By combining Lean with Six Sigma, waste is eliminated and errors reduced by limiting activities that do not add value to processes, thus enhancing productivity levels. Increased productivity can also increase psychological and psychological stress on workers, however. Continuous improvement with the integration of Ergonomics principles in Lean Six Sigma enables productivity increases without compromising worker safety, generating healthy and comfortable work conditions within proper workstation layouts. Mura translates as unevenness or irregularity, and it occurs when there is an uneven process leading to inconsistencies. Mura can be applied to mechanical and human situations with regard to postural and biomechanical for desired tasks. Rapid Upper Limb Assessment (RULA) was used to assess postural movement associated with upper extremity Musculoskeletal Disorder tasks where the workers were standing or seated without moving about. The methodology of this research proposed a model based on the integration of Lean Six Sigma and Ergonomics by designing a diagnostic expert system combining tools focused on Mura time waste and a Rapid Upper Limb Assessment to identify the risk levels inherent in the postural movements of workers as part of the lean practice undertaken within the workstations in the assembly line.

Expert system diagnose disease dermatitis using web based certainty factor

Dermatitis is one of the skin disorders characterized by itching and clinically appearing a rash on the skin. Generally can occur repeatedly to a person in the form of skin inflammation (epidermis and dermis) in response to the influence of exogenous factors or endogenous factors. Dermatitis disease diagnostic expert system is a web application that allows users to diagnose the type of dermatitis disease they are experiencing. The purpose of making this application is to facilitate the user in diagnosing the type of dermatitis disease based on the symptoms that are being felt. In the use of expert system application of dermatitis diagnosis of this disease the user must register first to get a username and password that will be used to log on expert system and do diagnosis dermatitis disease. After diagnosis the user can know the type of dermatitis disease they are experiencing along with the description of the disease type and the solution. Based on testing, the accuracy of this application is 90%. With trial data that is 10 patients.

An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.

Проектирование диагностической экспертной системы на основе диагностических методов европейской и восточной медицины

Проектирование диагностической экспертной системы на основе диагностических методов европейской и восточной медицины

A telehealth system for automated diagnosis of asthma and chronical obstructive pulmonary disease.

This paper presents the development and real-time testing of an automated expert diagnostic telehealth system for the diagnosis of 2 respiratory diseases, asthma and Chronic Obstructive Pulmonary Disease (COPD). The system utilizes Android, Java, MATLAB, and PHP technologies and consists of a spirometer, mobile application, and expert diagnostic system. To evaluate the effectiveness of the system, a prospective study was carried out in 3 remote primary healthcare institutions, and one hospital in Bosnia and Herzegovina healthcare system. During 6 months, 780 patients were assessed and diagnosed with an accuracy of 97.32%. The presented approach is simple to use and offers specialized consultations for patients in remote, rural, and isolated communities, as well as old and less physically mobile patients. While improving the quality of care delivered to patients, it was also found to be very beneficial in terms of healthcare.

Measurements on partial discharge in on‐site operating power transformer: a case study

This study presents the case study of a substation in-service power transformer referred to the on-site partial discharge (PD) detection and evaluation methods. An original methodology for simultaneous application of three methods is proposed: electrical, acoustic and ultra-high frequency. Transformer is powered by the power grid and no external generator is required according to the proposed methodology. Furthermore, several possibilities of applying these concurrent measurements and benefits of such solution in terms of result interpretation, interference resistance and on-site measurement applicability are indicated. The proposed methodology allows for a fast, accurate and secure PD diagnostics in a power transformer under on-site operating conditions. In the described example, a failure as a surface PD in the vicinity of the first phase of high voltage winding output is detected. The research results provide the basis for further works on a diagnostic expert system, which exploits automatic detection and evaluation of the PD phenomenon in power transformers under normal operating conditions.