Fault Diagnosis with Fuzzy Expert System

2011 ◽  
Vol 48-49 ◽  
pp. 519-522 ◽  
Author(s):  
Yang Lan Ou
Keyword(s):  
Expert System ◽  
Numerical Models ◽  
Energy Systems ◽  
Sensor Data ◽  
Fuzzy Expert System ◽  
Sensor Fault ◽  
Knowledge Based ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge based on fuzzy expert system.

Fuzzy Expert Systems for the Diagnosis of Component and Sensor Faults in Complex Energy Systems

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Andrea Toffolo
Keyword(s):  
Expert Systems ◽  
Numerical Models ◽  
Energy Systems ◽  
Combined Cycle ◽  
Sensor Data ◽  
Complex Energy ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Expected Values ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, since more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. This comparison simply determines if the differences between measured and expected values are “negative,” “zero,” or “positive” in fuzzy logic terms. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge base of a fuzzy expert system. The proposed diagnostic methodology is tested on the gas section of a real combined-cycle cogeneration plant, and the effect of measurement noise is also discussed.

Fuzzy Expert Systems for the Diagnosis of Component and Sensor Faults in Complex Energy Systems

2007 ◽  
Author(s):  
Andrea Toffolo
Keyword(s):  
Expert Systems ◽  
Numerical Models ◽  
Energy Systems ◽  
Combined Cycle ◽  
Sensor Data ◽  
Complex Energy ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Expected Values ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, since more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared to the expected values of the same quantities that are calculated using numerical models of local subsystems. This comparison simply determines if the differences between measured and expected values are “negative”, “zero” or “positive” in fuzzy logic terms. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge base of a fuzzy expert system. The proposed diagnostic methodology is tested on the gas section of a real combined-cycle cogeneration plant and the effect of measurement noise is also discussed.

scholarly journals Toward efficient modeling of fuzzy expert systems: a survey 

2012 ◽  
Vol 52 (No. 10) ◽  
pp. 456-460 ◽  
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Expert System ◽  
Conceptual Modeling ◽  
Maintenance Cost ◽  
Fuzzy Expert System ◽  
Knowledge Modeling ◽  
Modeling Tools ◽  
Knowledge Based ◽  
Application Systems ◽  
Fuzzy Expert Systems ◽  
Efficient Modeling

Efficient modeling of the artificial intelligence tools has become a necessity in order to cut down the development and maintenance cost associated with building application systems in the business, industrial and agriculture sectors that are frequently amendable to sudden unexpected environmental and economic conditions changes. This can be accomplished through developing an efficient modeling language which exploits the beneficial features of the emerging object-oriented technology. This research is aimed at reviewing the recent scientific aspects of the research concerning conceptual modeling of fuzzy knowledge-based system, which exhibits a large extent of applicability in last few decades due to its capability to deal with vagueness, uncertainty and subjectivity, those are inherent in real world problems. The most recent researches and applications of fuzzy expert system are surveyed. The existing knowledge modeling techniques are reviewed and the prominent ones are pinpointed. This paper is intended to identify the main and common bottlenecks of the existing knowledge modeling tools to overcome it in developing a reliable conceptual model of fuzzy expert system. 

scholarly journals Fuzzy Expert System Generalised Model for Medical Applications

2019 ◽  
Vol 24 (2) ◽  
pp. 128-133
Author(s):  
Osée Muhindo Masivi
Keyword(s):  
Expert System ◽  
Exponential Growth ◽  
Fuzzy Systems ◽  
Medical Application ◽  
Medical Applications ◽  
Fuzzy Expert System ◽  
Fuzzy Variables ◽  
The Past ◽  
Proposed Model ◽  
Fuzzy Expert Systems

Abstract Over the past two decades an exponential growth of medical fuzzy expert systems has been observed. These systems address specific forms of medical and health problems resulting in differentiated models which are application dependent and may lack adaptability. This research proposes a generalized model encompassing major features in specialized existing fuzzy systems. Generalization modelling by design in which the major components of differentiated the system were identified and used as the components of the general model. The prototype shows that the proposed model allows medical experts to define fuzzy variables (rules base) for any medical application and users to enter symptoms (facts base) and ask their medical conditions from the designed generalised core inference engine. Further research may include adding more composition conditions, more combining techniques and more tests in several environments in order to check its precision, sensitivity and specificity.

scholarly journals Fuzzy Expert System for Prediction of Prostate Cancer

2020 ◽  
Vol 16 (01) ◽  
pp. 163-176
Author(s):  
Juthika Mahanta ◽  
Subhasis Panda
Keyword(s):  
Prostate Cancer ◽  
Expert System ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Fuzzy Expert System ◽  
Knowledge Based ◽  
Free Psa ◽  
Clinical Dataset ◽  
Positive Biopsy

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.

Application of Fuzzy Expert System for Analyzing Agriculture Data

2020 ◽  
pp. 16-32
Author(s):  
Jharna Majumdar ◽  
Shilpa Ankalaki ◽  
Sabari Prabaaker
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Fruits And Vegetables ◽  
Meat Products ◽  
Fuzzy Expert System ◽  
Indian Economy ◽  
Fuzzy Expert Systems

Agriculture plays a major role in the Indian economy. India is rich in production of crops like rice, cotton, wheat, soybean, sugar; fruits and vegetables like onion, tomatoes, potatoes; dairy; and meat products. India is ranked first worldwide for the production of banana, jute, mango, cardamom, and ranked second worldwide for the production of rice, tomato, potato, and milk. India's agriculture contributed 4759.48 INR billion to the GDP during the first three months of 2018, and it has been reduced drastically during the second three months of 2018 (i.e., it has been reduced to 4197.47 INR billion). The average GDP is 4057.73 INR billion from 2011 until 2018; the agricultural contribution to GDP reached its highest level, that is 5666.82 INR billion, in the last three months of 2017. This chapter explores the application of fuzzy expert systems for analyzing agricultural data.

Assessing the Migration and Transformation of Pesticides in the Subsurface : The Role of Expert Systems

1990 ◽  
Vol 25 (3) ◽  
pp. 293-324 ◽  
Author(s):  
A.S. Crowe ◽  
J.P. Mutch
Keyword(s):  
Expert System ◽  
Numerical Models ◽  
Simulation Models ◽  
Management Tool ◽  
Transport And Transformation ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Regulatory Evaluation ◽  
Agricultural Regions

Abstract The expert system described here is designed to aid regulatory personnel in their assessment of the potential for pesticides to contaminate groundwater. The expert system, known as EXPRES (EXpert system for Pesticide Regulatory Evaluation Simulations), consists of existing simulation models coupled with a knowledge-based system. The numerical models are used to simulate the transport and transformation of pesticides in the unsaturated zone. The knowledge-based system guides the user through the choice of all the necessary information for characterizing the physical, climatic, hydrogeological, pedological and agricultural settings of typical agricultural regions across Canada required by the pesticide model, as well as aiding the user with the model predictions. EXPRES is designed to be used as a management tool to aid in policy decisions and is not intended for use as a research tool. Thus, its purpose is not to provide insight into the processes that control the fate of pesticides in porous media, but to provide an assessment of the potential hazards and to identify if further study is warranted.

A knowledge-based fuzzy expert system to analyse degraded terrain

2009 ◽  
Vol 36 (2) ◽  
pp. 2459-2472 ◽  
Author(s):  
Dieter D. Genske ◽  
Klemens Heinrich
Keyword(s):  
Expert System ◽  
Fuzzy Expert System ◽  
Knowledge Based

Methodology for integrating fuzzy expert systems and discrete event simulation in construction engineering

2009 ◽  
Vol 36 (9) ◽  
pp. 1478-1490 ◽  
Author(s):  
Ahmed A. Shaheen ◽  
Aminah Robinson Fayek ◽  
Simaan M. AbouRizk
Keyword(s):  
Expert System ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Simulation Models ◽  
Integrated System ◽  
Fuzzy Expert System ◽  
Factors Affecting ◽  
Construction Engineering ◽  
Event Simulation ◽  
Fuzzy Expert Systems

This paper demonstrates how fuzzy expert systems can be integrated within discrete event simulation models to enhance their modeling and predictive capabilities for construction engineering applications. A proposed methodology is presented for extracting the information from experts to develop the fuzzy expert system rules. The developed fuzzy expert system is integrated within a discrete event simulation model to enhance its modeling capability by explicitly accounting for the different factors affecting some of the simulation activities. A tunneling case study is used to illustrate the features of the integrated system. The outputs generated from the integrated system are very comparable to those from the original probabilistic simulation model. The integrated system represents a more realistic modeling scenario, since it thoroughly accounts for the different factors affecting the tunnel boring machine (TBM) advance rate. This paper is relevant to researchers because it provides an advance in combining artificial intelligence techniques with simulation models to yield better tools for construction modeling. It is of relevance to practitioners because it provides a useful tool for modeling construction engineering problems.

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