Fault Diagnosis Expert System Research Articles

Sistema Especialista para Correção de Panes em Aeronaves

The article discusses the implementation of an Expert System (ES) for aircraft fault diagnosis, highlighting the complexity of modern systems and the need for qualified mechanics. The ES uses heuristics and a knowledge base to diagnose issues, allowing mechanics to follow a structured methodology during maintenance. Through inference rules, the ES not only improves efficiency in identifying faults but also elevates the professional level of mechanics by providing continuous and well-founded support. The adoption of this system can lead to more effective and safer maintenance of the Brazilian Air Force aircraft.

Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.

Application of Modern Multimedia and Sensing Technology in Fault Detection and Diagnosis of Hydraulic Agricultural Machinery

In order to solve the problem of fault diagnosis of agricultural machinery hydraulic system, the authors propose an application of modern multimedia and sensing technology in fault detection and diagnosis of hydraulic agricultural machinery. By analyzing the component faults and system faults of the hydraulic system of agricultural machinery, an expert system for fault diagnosis is constructed, and a knowledge base and inference engine suitable for fault diagnosis of the hydraulic system of agricultural machinery are designed. In order to further improve the diagnostic accuracy of the fault diagnosis expert system, a fault diagnosis based on sparse coding is designed, and the sparse coding fault diagnosis results are integrated with the expert system to improve the diagnostic accuracy. The experimental results show that after sparse coding fusion, the fault diagnosis accuracy can be improved to more than 91%. In conclusion, the model meets the requirements of fault diagnosis and puts forward a new idea for the fault diagnosis of agricultural machinery hydraulic system. Make the agricultural machinery and equipment industry develop healthily.

Fault Diagnosis Using Bond Graphs in an Expert System

A fault diagnosis method using bond graphs in an expert system is proposed for a reactor coolant system. Firstly, the time causality graph and the variable relationship graph are derived from the bond graph. Secondly, the fault signature matrix is obtained by combining the change relationship of fault parameters. Finally, the fault signature matrix is used as the rule of the inference engine design in the expert system for fault diagnosis. In this paper, the key equipment of the reactor coolant system is used to verify the fault diagnosis method of the bond graph expert system, and the path reasoning relationship between alarms is obtained, which can accurately obtain the deep knowledge required by the operators. A new idea for fault diagnosis in a nuclear power plant’s expert system is provided by this method.

Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

Research on the Fault Diagnosis Method of Mine Fan Based on Sound Signal Analysis

The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis.

Design of a Fault Diagnosis Equipment

In the process of daily maintenance and repair of a certain type of equipment, there are problems of long fault diagnosis time and untimely repair. A set of fault diagnosis equipment has been developed for this purpose. The device adopts C8051F120 microprocessor and consists of interface circuit board, excitation signal board, single chip computer board, signal conditioning board, fault diagnosis expert system, etc. It can detect the main performance parameters of the tested equipment, and combined with the fault diagnosis expert system, it can realize the function of performance detection, fault diagnosis location and data storage record, and effectively improve the efficiency of fault diagnosis of the tested equipment.

Research on Ship Electronic Power System Fault Diagnosis Based on Expert System

Aiming at the difficulty in locating the fault point quickly and accurately after the ship electronic power system fault. In this paper, fault diagnosis expert system is used to diagnose the fault occurred to ensure stability of power supply of ship power system. The possible faults are divided into four categories on the base of analysis of ship electrical system fault characteristics. Frame structure is used to describe the connections between the various parts of the ship power system in knowledge presentation. Expert system uses production rules to describe the specific fault and combine the ideal of fuzzy inference to dealing with the uncertainty in fault diagnosis at the same time. The expert system designed in this paper can find fault points and provide solutions quickly and accurately and improves the reliability of the ship power system by test.

Application Research of Artificial Intelligence Technology in Power System Alarm Processing Method

The paper first introduces the principles of artificial intelligence and expert systems, and makes a detailed analysis of the application of expert systems in power systems. Secondly, the paper uses VC++ language to establish an expert system for judging power system fault areas and fault types. The system can identify power system bus faults, line faults, circuit breakers and relay malfunctions and refusal faults. Finally, the system simulation test shows that the fault diagnosis expert system established in this paper has certain theoretical and practical significance.

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.

Bayesian network approach to fault diagnosis of a hydroelectric generation system

AbstractThis study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic‐mechanical‐electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time‐based maintenance to transform to the condition‐based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy‐Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.

Research on fault diagnosis of B737 aircraft fuel system based on improved BP neural network

In this paper, an improved BP neural network algorithm is applied to the fault diagnosis of aircraft fuel system. The simulation results show that the algorithm has the characteristics of fast diagnosis speed and low misdiagnosis rate, and lays a foundation for the development of aircraft fuel fault diagnosis expert system based on neural network.

A Fault Diagnosis Expert System for Commercial Bus Manufacturing process

Bus manufacturing is one of the important assets in automotive industries as well as mode of public transportation. The design process is difficult and long throughout time. Moreover, there are many manuals, rules and regulation according to different standard which make the standardization and design process to be difficult and time consuming. Hence, this project describes the use of an expert system shell for commercial bus design. In bus manufacturing field, design of commercial bus is heavily depending on human experts. With the help of expert system, process of design commercial bus will be shortened up to 56.5% compared to conventional way. The developed system can be used as a training module for inexperienced personnel. In this research work, the fault diagnosis system was developed by using Kappa-PC expert system shell. It is supported by object orientated technology for the MS window environment. Lastly, the developed system will be validated with a case study to verify the capability of the developed system.

Design of an expert system for the prediction and comprehensive diagnosis of CNC machining fault

Unlike the earlier research works which only focused on fault diagnosis, in this paper a smart fault preventive step is taken in avoiding some category of failures which may be predicted at the initial stage of CNC operation before a fault is diagnosed. Besides this, the domain of fault diagnosis has also been expanded by incorporating human error which was not part of the earlier system. This is the first phase of the study and a comprehensive fault diagnosis expert system algorithm for CNC machine is going to be designed, which can later on be transformed into a knowledge-based intelligent computer applications program. This will be accomplished by consulting experts on various aspects of CNC operations.

A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines

This paper proposes a new belief rule-based (BRB) expert system for fault diagnosis of marine diesel engines. The expert system is the first of its kind that consists of multiple concurrently activated BRB subsystems, in which each subsystem has its distinctive outputs and uses the evidential reasoning approach for inference. This novel modeling approach can be applied to identify fault modes that may co-exist. In essence, the group of BRB subsystems is used to model the nonlinear relationships between the fault features and the fault modes in marine diesel engines. The initial BRB expert system can be established by using expert experience and then optimized by using the data samples accumulated during the operation of marine diesel engines. Due to limitations in knowledge and data collected, ignorance is also considered in some BRB subsystems. The proposed BRB expert system is applied to abnormal wear detection for a kind of marine diesel engine. The performance of the BRB expert system is investigated in comparison with that of artificial neural network (ANN) models, support vector machine (SVM) models, and binary logistic regression model with fivefold cross-validation. The results show that the BRB expert system can be used for fault diagnosis of marine diesel engines in a probabilistic manner, which outperforms the ANN models, SVM models, and the binary logistic regression model in terms of accuracy and stability, and can effectively identify concurrent faults.

A Fault Diagnosis Expert System for Building Chillers

Building air conditioning is a demanding trend nowadays. It gives maximum comfort working and leisure environment to the occupant as well as it reduces the indoor temperature. This project describes the use of an expert system shell to develop a fault diagnosis system for building chillers. For the building chiller, services and maintenances of the machine are heavily depending on human expertise, which is costly and critical. Hence, the main goal of the developed system is to diagnose the problems of building chillers. With the developed system, the diagnosis process for building chillers is standardized, precise and faster compared to the normal way. The developed system is useful for the inexperienced staff as a training module as well. The constraint values for this developed expert system are based on building chiller design data and expert’s experiences. A case study was also conducted to verify the capability of the developed system.

Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring.

Fault Diagnosis System of Electrical System Based on Classified Hierarchical Model

In order to promote the maintenance and support capability of modern military engineering equipment under the complicated field circumstance, the fault diagnosis expert system is proposed based on neural network. The fault diagnosis characteristics of whole machine as well as the failure mode and mechanism of the electronic system are investigated in detail. Then the structure of classified hierarchical diagnosis model is studied, and the knowledge base of the fault diagnosis system is also been accomplished. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work is applied in military engineering fields. Hence it effectively enhances the fault maintenance, diagnosis and support capabilities of the military troops.

Rule - based Fault Diagnosis Expert System for Wind Turbine

Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.

Implementation of an integrated real-time process surveillance and diagnostic system for nuclear power plants

This paper presents two parts, real-time process surveillance unit and fault diagnosis unit, which are separated from each other. However, these units are connected by trigger-rules in a real-time expert system. The design structure that has been adopted is capable of inspecting errors and revising the model.Multilevel Flow Model (MFM), which is a method for functional modeling, is introduced briefly and illustrated with a reactor coolant system. Utilizing functional modeling method to represent system knowledge, this modeling method is especially useful when the domain experts are not available.Considering issues such as loop modeling and mutually exclusive events inevitably exist between the observation points, a novel modeling technology called observation points’ protection was used to build a generic fault model and preserve the statuses of observation points during reasoning within an expert system. This paper also presents minimal candidate and domains of interpretation, which are especially useful for finding the fundamental root cause when multiple faults occur.The process surveillance and diagnostic system is implemented on the platform of G2, which is an environment for developing real-time expert systems. The emulation test was conducted and it has been proven that the fault diagnosis expert system can identify the faults correctly and in a timely manner.