Diagnostic Expert System Research Articles

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

AUTOMATED SYSTEMS FOR DIAGNOSING DISEASES: A REVIEW OF EXISTING TOOLS

Diagnostic expert systems are computer-based decision-support systems that are designed to assist healthcare professionals in diagnosing diseases. They are designed to provide a list of possible diagnoses based on a patient’s symptoms, medical history, and other relevant information. The basic operation of a diagnostic expert system involves capturing information from a patient, such as symptoms and medical history, and using that information to generate a list of potential diagnoses. The system then uses knowledge-based techniques, such as decision trees and rule-based systems, to narrow down the list of potential diagnoses to arrive at a final diagnosis. This research paper provides an overview of expert systems, including their history, architecture, knowledge representation. It also discusses the advantages and limitations of expert systems, as well as their applications in different fields. There are many research papers studied and research analysis done. There have been numerous research studies in the field of systems for diagnosing diseases, particularly in the area of medical expert systems and decision support systems.

APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems.

Traditional medicine of three regions of Russia and the use of information technologies to investigate it

The paper presents experience of investigating the traditional medicine for the three regions of Russia – Buryatia, Kalmykia and Tuva — that has been kind of folk tradition here for centuries. This is Tibetan medicine that is the cultural and historical heir of the Indian medical tradition using some elements of the Chinese medicine. The paper gives information on the problem of objectifying and automating the Tibetan diagnostic methods (inspection, palpation, questioning) using information technologies. This research has been carried out in two directions: objectification and automation of pulse diagnostics by the development of the Computer Pulse Diagnostic System and the development of the Expert Diagnostic System for questioning and inspection of a patient. On the basis of the assessments of the experts (specialists in pulse diagnostics) and mathematically detected diagnostic signs of pulse waves, statistical models of normal and pathological pulses (pulses of «hot» and «cold» diseases) have been developed. A catalogue of pulses is being compiled. This catalogue consists of classes of computer recorded pulse signals that correspond to Tibetan nosology. The expanded and differentiated catalog of pulses and the pulse wave pattern recognition program designed to compare measured pulses and reference pulses will make the basis for combining the diagnostic results by three diagnostic methods, which will lead to the creation of an Integral Computer Diagnostic System designed for diagnosing diseases and providing recommendations for health correction in accordance with the traditions of Tibetan medicine.

Diagnostics of inter-turn short circuits in phase stator windings of permanent magnet synchronous machines

BACKGROUND: Currently, electric buses are becoming more widespread in the segment of urban passenger transportation due to the absence of harmful emissions and low noise levels. Permanent magnet synchronous machines (PMSM) are widely used as a traction motor.
 One of the most promising methods of monitoring the technical condition of traction motors is diagnostics using monitoring tools installed on board of a vehicle. The advantages of such methods include the possibility of regular frequent condition checks (as opposed to much rarer diagnostics in stationary maintenance points) and a more rapid response to the development of identified malfunctions, which significantly reduces the cost of maintenance and repair of expensive equipment. The disadvantages include limitations associated with a shortage of layout space for the placement of diagnostic equipment, with the limited capabilities of on-board computer facilities for the transmission and storage of diagnostic information and with the complexity of computational procedures of built-in expert diagnostic systems.
 Concerning the types of operational failures of PMSMs, which are the most common and the most severe in terms of the cost of restoring operability, these are inter-turn short circuits in the stator windings, increased eccentricity of the air gap between the stator and the rotor, bearing damage, as well as damage of the mechanical gearbox coupled with the electric motor.
 AIM: Identification the diagnostic signs of inter-turn short circuits in the phase windings of a permanent magnet synchronous machine, helping to detect these malfunctions on board of route urban vehicles at an early stage.
 METHODS: A mathematical model of a permanent magnet synchronous machine with short-circuited turns of the phase winding, which makes it possible to study the behavior of the motor with a various number of short-circuited turns, has been developed.
 RESULTS: For the first time, the requirements for diagnostic features for the on-board diagnostic system of an urban electric bus, making it possible to detect inter-turn short circuits in the phase windings of traction electric motors at the early stages of the origin of a malfunction, are formulated.
 The main parameters of the process of digital registration of phase currents have been obtained, helping to do measurements and to transmit signals through the onboard information network of the electric bus.
 Using simulation modeling methods, it was found that the peak values of the linear spectrum of the total current vector are stable diagnostic signs that make it possible to detect inter-turn short circuits in the phase windings of traction motors at the early stages of the origin of the malfunction.
 CONCLUSION: The practical value of the study lies in the opportunity of using the proposed methods for diagnosing the technical condition of traction electric drive units in urban passenger electric transport.

Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique

Hypertension is a leading risk factor for cardiovascular disease (CVD) and the overlap of diabetes mellitus (DM) with hypertension can lead to severe complications. There is a need for early diagnosis and risk stratification to implement an overall risk management strategy. Presently, the conventional method is not suitable for large-scale screening. The primary aim of this study is to develop an automated diagnostic system that uses Photoplethysmogram (PPG) signals for the non-invasive diagnosis of hypertension and DM-II. The proposed model uses a statistical feature extracted by decomposing the PPG signal up to level 11 into a sub-band using Discrete wavelet transform (DWT), and a variety of classifiers are used for the classification of hypertension and detection of DM-II patients. Three feature selection techniques used are Spearman correlation, ReliefF and Minimum Redundancy Maximum Relevance (mRMR) to select 20 top features out of 130 features using correlation with systole blood pressure (SBP), diastole blood pressure (DBP) values and D-II. The highest accuracy attained by the Adaptive neural fuzzy system (ANFIS) for classification categories such as normal (NT) vs prehypertension(PHT), NT vs. hypertension type 1 (HT-I), NT vs hypertension type 2 (HT-II) in terms of F1 score are 92.%, 98.5%, 98.3% (SBP) and 83.1%, 95.6%, 86.8% (DBP),respectively. The accuracy achieved by the adaptive-network-based fuzzy inference system (ANFIS) for the classification of normal (non-diabetic) vs. diabetic patients is 99.1%. The hybrid learning algorithm-based classifier achieved higher accuracy for hypertension risk stratification as compared to the hard computing classifier, which requires parameter tuning and DWT decomposition is robust to the noisy signal, overcoming the limitation of the morphological feature-based model.

Implementation of the breadth-first search method on forward-chaining inferences to diagnose autism disorders in children

Autism is a type of mental disorder that can affect children as young as three years old, but can affect children from birth. This mental disorder is characterized by social relationships, culture, verbal and non-verbal communication, imagination, and a single attractive object. By using the Autism Disorder Diagnostic Expert System, we hope that most people will be able to attempt an autism diagnosis without an expert. This system works by answering all system questions. The "forward chaining" method is a way of drawing conclusions that starts with facts and tests hypotheses toward conclusions. To test the hypothesis, begin matching facts or statements with IF. The forward chaining method searches for a solution to a problem. The forward chaining inference and breadth first search methods were used to design this system, as well as Microsoft Access for database management systems and Visual Basic Language Programming. The expected outcome is that all users, particularly parents, will be able to easily assess the possibility of their child developing autism symptoms from an early age. The output of the system is whether there is a possibility of autism in a child based on the facts and symptoms given to the system.

Gastric Disease Diagnostic Expert System Application Using the Fuzzy Mamdani Method

The level of awareness among Indonesian society regarding the importance of maintaining gastric health is still very low, even though gastric diseases can significantly disrupt daily activities. In a medical examination, a doctor detects diseases in a patient's body based on their symptoms or complaints. The patient's action is to meet the doctor in person, and the doctor asks about the symptoms experienced by the patient. In the manual system, there is a drawback where patients have to visit the doctor for consultation or to have their diseases examined, and they also need to prepare the necessary fees for the examination. Such a manual system can be simplified with an information system where patients don't need to visit the doctor to diagnose their diseases. Therefore, the researcher will develop a gastric disease diagnostic expert system application using the fuzzy Mamdani method. The aim is to make it easier for patients/the public to identify the type of disease based on the symptoms experienced, as well as to provide information on solutions, actions, and medication for the disease. The methodology used in developing the gastric disease diagnostic expert system application involves four stages: fuzzification process, implication function, inference process (rules), and defuzzification. The research flow includes data collection through interviews and data sampling, data analysis, calculation using the fuzzy Mamdani method, implementation, and testing using a black box. The result of this research is a gastric disease diagnostic expert system application using the fuzzy Mamdani method with an accuracy of 65%. This application can help individuals to identify the type of disease based on the symptoms experienced without having to immediately consult a doctor, thus avoiding potential issues

Analysis and Application of the Certainty Factor Method in the Diagnostic Expert System for Broiler Chicken Diseases

An expert system is a system that uses human knowledge recorded in a computer to solve problems that normally require human expertise. Certainty Factor is a clinical parameter value given by MYCIN to indicate the level of trust. The certainty factor is also a way of combining belief and disbelief in a single number. Certainty factor introduces the concepts of belief/belief and disbelief/unbelief. In the system analysis stage, the author analyzes to find weaknesses in the system that is currently running in an organization or company with the aim that the system can be proposed for improvement. Implementing the program that has been designed requires a tool in the form of a computer or laptop, which requires three supporting components to operate the computer or laptop itself, such as hardware, software, and brainware. The expert system application has been running with the Certainty Factor method and to make a diagnosis on chickens the user can choose a value according to the Certainty Factor Interpretation that has been given and processed with the CF expert. Based on the tests carried out, the value of system accuracy with experts is almost the same . And the Inference engine works well, according to the pre-programmed rules. After diagnosing a chicken disease, the system will display how to treat and handle sick broiler chickens

Expert systems for diagnostics of infectious diseases in cattle

Expert systems designed to identify diseases in cattle may use a variety of sources of information such as clinical data, signs of disease, medical history, laboratory results, environmental exposure and other relevant factors. They can be developed based on rules, heuristics, or machine learning, using a large amount of data to train the model. The expert system may provide the veterinarian with a list of possible diagnoses, ranked by probability, based on the data provided. She may also recommend further investigations or tests for a more accurate diagnosis. The developed expert system for veterinary medicine provides a wide range of functions and capabilities to support the work of veterinarians and processing data on the incidence of cattle.

DESIGN OF AN ANDROID-BASED LUNG DISEASE SCREENING EXPERT SYSTEM AND PATIENT EARLY WARNING USING THE FORWARD CHAINING METHOD AT WALUYO JATI, KRAKSAAN HOSPITAL

This aims of study were design and create an android-based lung disease screening expert system and patient early warning using the forward chaining method at Waluyo Jati Kraksaan Hospital. This study used a mixed methods approach, namely descriptive qualitative and quantitative descriptive. In this descriptive qualitative research design used case study and the quantitative descriptive research design uses a survey. This research used the forward chaining method and model development using the Waterfall model. Based on the accuracy test table, it can be concluded that the Android-based lung disease diagnosis expert system uses the Forward chaining method. The ISO/IEC 9126 standard test uses two aspects, namely the usability aspect of 83.45% and the Functionality aspect of 85.1%. The certainly factor for the confidence value can be identified quite well. This is evidenced by the results of testing the system accuracy of 96.43%. An Android-based lung diagnostic expert system was built to provide identification conclusions in accordance with the thoughts of an expert, namely a lung specialist.

Diagnostic Expert System for Children with Mental Disabilities with the Certainty Factor Method

Mental retardation is a mental disability which is also known as Intellectual Disability or mental retardation. Intellectual Disability is characterized by significant limitations in intellectual functioning, significant limitations in adaptive behavior, including cognitive, social, and practical adaptive skills. SLB YPAC West Sumatra is one of the special schools that prioritizes quality services for people with disabilities to increase independence. These services need to be carried out to minimize obstacles or disturbances of repetitive, aggressive behavior and other balance disorders. At SLB YPAC West Sumatra, 100% still use experts for their services. With the problems that exist in SLB YPAC West Sumatra which still uses a manual system, an expert system is created that can diagnose children with mental retardation using the Certainty Factor method. By using data on types of mental retardation and some symptoms, it was found that mentally retarded people at SLB YPAC West Sumatra suffer from moderate mental retardation with a CF value of 0.33. This research is expected to help SLB YPAC West Sumatra and service to be faster and also help the performance of employees at SLB YPAC West Sumatra

Sistem Pakar Diagnosa Penyakit Pencernaan Menggunakan Metode Case Based Reasoning (CBR) Berbasis Web

The main factors causing digestive disorders are food that is not good in terms of hygiene and health, nutritional balance, improper diet, infection, and abnormalities in the digestive organs so that they will contract several digestive diseases such as appendicitis, ulcers, stomach ulcers, hepatitis. , diarrhea and constipation. Early treatment is needed before consulting specifically with a digestive disease specialist before the disease is severe. In this case, it is necessary to have a system that can replace specialist doctors in diagnosing early symptoms of digestive diseases considering the high cost of consulting a doctor. This diagnostic expert system is very much needed in diagnosing early symptoms of the causes of digestive diseases. The application of the system is equipped with a Case Based Reasoning that solves new cases based on reasoning from old cases. The implementation has resulted in gastric cancer of 67.01% according to the symptoms selected by the patient, then the system also provides treatment advice, and provides information on the handling or treatment of gastric cancer, so that with this application it can help doctors in diagnosing the disease quickly.

Sistem Pakar Diagnosa Penyakit Diabetes dengan Menggunakan Metode Bayes Berbasis Web

Diabetes Mellitus is one of the non-communicable diseases with the highest proportion in Indonesia and is the sixth highest cause of death in this country. Especially in Asahan District, the number of people with diabetes who died is increasing over time, so there needs to be effective treatment so that diabetes is no longer feared by the public because it is easy to treat. Many patients who experience diabetes are getting worse because they cannot detect the early symptoms of diabetes, which is still considered trivial. There is no information about diabetes and its symptoms, making it difficult to diagnose the disease. In the diagnosis of DM disease is limited to conventional diagnoses with doctors. it is necessary to build a system on a computer application to help diagnose DM. To determine the level of DM disease, an expert diagnostic system was made with the method used in this case is the Bayes method. This method is an approximation to an uncertainty that is measured by probability. Bayes' approach at the time of classification is to find the highest probability by inputting the required attributes and the probability of the disease and related symptoms. The results of the implementation of the system are the selection of symptoms according to the case of experiencing type 1 diabetes because it has a weight = 2 higher than the weight results of other diseases, the system provides the results of the process the system will provide information on what type of DM he is experiencing in order to get a solution with treatment

Penerapan Metode Metode Dempster Shafer Untuk Mendiagnosa Penyakit Ginjal Berbasis Web

Kidney disease is one of the non-communicable diseases with the highest proportion in Indonesia and is one of the causes of death in this country. Especially in Asahan District, the number of kidney patients who die is increasing over time, so there needs to be effective treatment so that kidney disease is no longer feared by the public because it is easy to treat. Many patients who experience kidney disease are getting worse because they cannot detect the early symptoms of kidney disease, which is still considered trivial. There is no information about kidney disease and its symptoms, making it difficult to diagnose the disease. In the diagnosis of this disease is limited to conventional diagnosis with doctors. it is necessary to build a system on a computer application to help diagnose kidney disease. To determine the level of kidney disease, an expert diagnostic system was made with the method used in this case is the Dempster Shaper method. This method is an approach method that is used is to measure the strength of evidence (facts) in supporting a set of propositions by providing a way to combine evidence from sources and bring or provide a level of confidence (represented through the trust function) which takes from all available evidence with input. required attributes and the likelihood of the disease and associated symptoms. The result of the implementation of the system is that the system provides an alternative to the symptoms experienced by the patient and the results of the process the system will provide information on kidney infection by 60% and also provide solutions with treatment

BANKRUPTCY DIAGNOSIS AS THE ELEMENT OF STRATEGIC MANAGEMENT OF THE ENTERPRISES MARKETING ACTIVITY

Abstract. The article is aimed at the forming approaches for the estimated probability of bankruptcy in the system of strategic management of marketing activity which serves as a tool for coordinating production and distribution of goods and services based on the customer needs, market characteristics and the development of practical measures to meet identified needs. It is proved that strategic management of marketing activity is the difficult task because it is necessary to achieve a dynamic balance between environmental factors and internal resources of the enterprise. The proposed analytical and applied support for the estimated probability of bankruptcy in the system of strategic management of marketing activity is based on the use of diagnostic expert systems that make it possible to formulate reasonable conclusions, to implement the necessary procedures aimed at the obtaining answers, to obtain the generalized results of analysis and to assess the probability of bankruptcy in a convenient form. It is substantiated the procedure for the estimated probability of bankruptcy in the context of strategic management of marketing activity providing for the formation of a set of indicators based on the selected criteria characterizing the state of the enterprise as a whole and the level of marketing activity. It is established the membership functions of the fuzzy value of the indicator to the previously introduced term-sets of the levels of the probability of bankruptcy. The results of calculating membership functions for each of the selected indicators made it possible to obtain the objective result of the estimated probability of the enterprise bankruptcy according to the selected system of indicators grouped by the activity areas in conditions of uncertainty and variability of the external environment for the current and future periods. The proposed approach for the estimated probability of the enterprise bankruptcy based on the regression mechanism of inference serves as the basis for the production of strategic management influences aimed at the stabilizing the financial condition of the enterprise by harmonizing production and sales activities in the context of marketing activity. Further areas of researches are the formation of a mechanism for strategic management of the enterprise marketing activity based on the implementation of the marketing concept in the context of the need to adapt to changing environmental conditions and consumer needs. Keywords: management, strategic management, marketing, marketing activity, bankruptcy, estimated probability of bankruptcy. JEL Classification G21, J31 Formulas: 0; fig.: 3; tabl.: 5; bibl.: 21.

Mathematical Models of Diagnostic Information Granules Generated by Scaling Intuitionistic Fuzzy Sets

The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with some scaling of the symptom-fault concept pair values. These value pairs can be considered as intuitionistic fuzzy sets for the space of diagnosed objects. In this article, for scaling intuitionistic fuzzy sets (n-ScIFS), the deductive theory is formulated. There the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs) are generalized to the n-ScIFS objects. The membership and non-membership values, as standard, can be described by the 1:1 scale or the quadratic function scale. However, any power scale n>2 can be used. In this paper, any n-Sc scaling functions retaining the IFSs properties are considered. The n-ScIFS theory introduces a conceptual apparatus analogous to the classical theory of Zadeh fuzzy sets and Yager PFSs, consistently striving, for the first time, to formulate the relational structure of n-ScIFSs as a model of a certain information granule system called here the diagnostic granule system. In addition, power- and linear-repeatable diagnostic granules are defined in the n-ScIFSs structure for serial or parallel diagnosis processes. The information granule base is determined and a diagnostic granule system model produced by this information granule base is shown. Certain algorithms have been given to establish the semantic language of diagnosis describing the system of diagnostic information granules.

ТЕХНОЛОГИИ КОНСТРУИРОВАНИЯ ЭКСПЕРТНЫХ СИСТЕМ ДЛЯ ЭЛЕКТРОЭНЕРГЕТИКИ

The technologies of construction diagnostic expert systems is considered. The strategy of the knowledge base structuring by diagnostic expert system is described

Diagnostic principles in maintenance systems

The features of the triangle of mutual influence of the basic diagnostic principles are analyzed depending on the known strategies of maintenance and repair, suggesting that these features be taken into account in diagnostic expert systems. Two options for the interaction of the diagnostic support of an object with its maintenance system are studied, specifically: the first is the influence of the set of possible defects in the diagnostic object and the existing maintenance system with a fixed maintenance strategy on the formation of system representations of this object; the second is the influence of the set of possible defects in the diagnostic object and the formed diagnostic model (system representations of the object) on the features of the designed maintenance system. It is proposed to modify the global algorithm for the functioning of the diagnostic expert system, taking into account various aspects of the relationship between the degradation representation of the diagnostic object and the existing maintenance strategies. The functional capabilities of two information platforms for organizing maintenance systems (Galaxy EAM and 1C:RCM Reliability Management) are assessed in relation to the triangle of mutual influence of basic diagnostic principles. The main conclusion is that the methodology for constructing diagnostic expert systems must necessarily be considered in the context of the triangle of mutual influence of basic diagnostic principles, otherwise it largely loses its pragmatic value.

Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

Integrating machine learning into one of the manufacturing processes, i.e., forging, is mainly concerned with making the system more intelligent by incorporating them to exhibit global understanding. Sometimes the engineer/operator can find the defects during or after the forging operation. So, the system will need some input to identify the different types of categorized defects. And also, according to that, we will develop the intelligent fault diagnosis process. We should calculate the statistical probability theory. Now, we implement the system which is the structure of the fault analysis system for the forging process. In the structure, we demonstrate the defect of the forged part, use the given imported probability to find the possible causes, and provide some remainders to reduce the fault. For enhancement of feature needs, this work includes more integration of AI with forging.