639 research outputs found
A review of applications of fuzzy sets to safety and reliability engineering
Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering
A Literature Review of Fault Diagnosis Based on Ensemble Learning
The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance
Fuzzy Systems
This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students
Evaluation of a fuzzy-expert system for fault diagnosis in power systems
A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the
time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults.
The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic
alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented
techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert
system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical
modelling fails and the period of alarm activity is high.
This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)
New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic & Plithogenic Optimizations
This Special Issue puts forward for discussion state-of-the-art papers on new topics related to neutrosophic theories, such as neutrosophic algebraic structures, neutrosophic triplet algebraic structures, neutrosophic extended triplet algebraic structures, neutrosophic algebraic hyperstructures, neutrosophic triplet algebraic hyperstructures, neutrosophic n-ary algebraic structures, neutrosophic n-ary algebraic hyperstructures, refined neutrosophic algebraic structures, refined neutrosophic algebraic hyperstructures, quadruple neutrosophic algebraic structures, refined quadruple neutrosophic algebraic structures, neutrosophic image processing, neutrosophic image classification, neutrosophic computer vision, neutrosophic machine learning, neutrosophic artificial intelligence, neutrosophic data analytics, neutrosophic deep learning, neutrosophic symmetry, and their applications in the real world. This book leads to the further advancement of the neutrosophic and plithogenic theories of NeutroAlgebra and AntiAlgebra, NeutroGeometry and AntiGeometry, Neutrosophic n-SuperHyperGraph (the most general form of graph of today), Neutrosophic Statistics, Plithogenic Logic as a generalization of MultiVariate Logic, Plithogenic Probability and Plithogenic Statistics as a generalization of MultiVariate Probability and Statistics, respectively, and presents their countless applications in our every-day world
Fuzzy temporal fault tree analysis of dynamic systems
YesFault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques
Machine condition monitoring using artificial intelligence: The incremental learning and multi-agent system approach
Machine condition monitoring is gaining importance in industry due to the
need to increase machine reliability and decrease the possible loss of production
due to machine breakdown. Often the data available to build a condition
monitoring system does not fully represent the system. It is also often common
that the data becomes available in small batches over a period of time. Hence,
it is important to build a system that is able to accommodate new data as
it becomes available without compromising the performance of the previously
learned data. In real-world applications, more than one condition monitoring
technology is used to monitor the condition of a machine. This leads to large
amounts of data, which require a highly skilled diagnostic specialist to analyze.
In this thesis, artificial intelligence (AI) techniques are used to build a
condition monitoring system that has incremental learning capabilities. Two
incremental learning algorithms are implemented, the first method uses Fuzzy
ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition,
intelligent agents and multi-agent systems are used to build a condition
monitoring system that is able to accommodate various analysis techniques.
Experimentation was performed on two sets of condition monitoring data; the
dissolved gas analysis (DGA) data obtained from high voltage bushings and the
vibration data obtained from motor bearing. Results show that both Learn++
and FAM are able to accommodate new data without compromising the performance
of classifiers on previously learned information. Results also show
that intelligent agent and multi-agent system are able to achieve modularity
and flexibility
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