Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC) Valves Used in Diesel Engines

In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC) valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS) type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves

A Review on Expert System Applications in Power Plants

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.DOI:http://dx.doi.org/10.11591/ijece.v4i1.502

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Marine gas turbine monitoring and diagnostics by simulation and pattern recognition

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK\uae model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model

Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q

In the processes of energy transformation, to carry out an adequate follow-up of the process parameters represent an opportunity to propose strategies to improve the processes' performance. For this reason, it is essential to analyze the behavior of process variables under the quantitative and qualitative optics supported by the experts. Thus, this work proposes a methodology of fuzzy Mandani type logic that allows the analysis of energy transformation processes (such as internal combustion engines) based on T2 and Q statistics, as a way to identify whether the operation limits are kept within the normal or exceed the limits, achieving to identify the anomaly in the process. In the initial stage, MATLAB implements two diffuse systems; the first system aims to determine the impact variables have on the generation of an anomaly, without identifying the type of defect. In the second stage, it's defined as a function of the number guests, the kind of monster that occurs in the observations made from the transition range in the operation of the system analyzed, until the last measurement obtained. In the third stage, the statistics T2, Q, and its limits are determined from the operating variables of the selected system. Finally, the previously calculated statistics are graphically processed in the diffuse systems. The results obtained in this work show that the analysis of processes or phenomena based on qualitative observations, the methodology implemented, is a useful tool for decision making in the industrial sector

Development of automated test system for diesel engines based on fuzzy logic

© 2016 IEEE.To control a diesel engine during testing, the principles of fuzzy output, which are widely used in fuzzy-logic controller development, could be applied. The controller's main task is to monitor an external object, in which case the behavior of the monitored object is described with the fuzzy rules. The most important application area of the fuzzy set theory is the fuzzy logic controllers. Their operation slightly differs from the operation of common controllers. In order to describe the system, the expert knowledge is used instead of differential equations. Control of the automation systems for engine testing (AST) with the fuzzy-logic controller should be based on a knowledge database with fuzzy rules. Such database could be created with expert knowledge, neural network, or direct measuring method. Development of an adaptive control system for diesel engine testing process based on the fuzzy logic enables simplification of the system's structural components and provision of discrete control procedure with some uninterruptible properties, which could improve the control and reduce the scope of the knowledge database. Fuzzy logic makes it simple to input a priory information about an object in the form of fuzzy control rules into the adaptive control system. Similarity of form and natural language relatively easy allows obtaining necessary expert knowledge. A prior information provides one of the key initial conditions of the system developed according to adaptive control method-the condition of supreme initial adaptation

An automated diagnostic system for ICE

© 2018, Institute of Advanced Scientific Research, Inc. All rights reserved. An automated system for testing diesel engines based on a neural network is considered. A neurofuzzy system has been designed to form fuzzy rules for diesel engine control during its testing Expansion of production of cars, tractors and their increasing role in meeting the needs of modern society leads to a continuous improvement of power units of cars - diesel engines. Declared capacity, economy, toxicity and other evaluation parameters of the diesel engine, as well as its reliability and durability, are established by testing in stand and operating conditions. Currently, all newly created, upgraded and serial engines of cars and tractors subject to different types of tests, the essence, volume and content of which is determined by their purpose and stipulated by GOST. Tests constitute the final stage of the complex process of creating and improving diesel engines. All kinds of new, modernized and serial engines are subjected to various types of tests in this connection. The tests allow to evaluate the quality of the diesel engine and compare its performance with the performance of other engines. In the process of testing determine the traction-dynamic, economic, environmental and other parameters of the engine and establish the compliance of these indicators with standards and technical conditions. During the tests, the peculiarities of this diesel are revealed, and comparing the results of tests of various types of engines, it is possible to evaluate the efficiency of design features, the quality of manufacture or their technical condition. At present, testing of diesel engines is a complex and time-consuming technological process, which differs little from an experimental study. Therefore, automated testing systems (ATS) for engines are created

Method for neuro-fuzzy inference system learning for ICE tests

© 2018, Institute of Advanced Scientific Research, Inc. All rights reserved. The application of an intelligent model for tuning an automated test system for diesel engines is considered. A neuro-fuzzy network has been designed to produce a control effect on the diesel. A technique for designing a knowledge base for controlling the operating modes of a diesel engine during its testing has been developed. In the life cycle of products, including internal combustion engines (ICE), a significant place is occupied by various technological tests of both individual units and the engine as a whole. Modern requirements to constant increase of technical level of let out designs result in that the share of expenses for carrying out of tests of diesel engines at creation of new samples all more increases. Especially large these costs become when the levels of automation of production and scientific research work do not match. In connection with this automation and mechanization of technological trials is one of the main tasks of increasing the technological level of production and the quality of the parts produced

Modeling of internal combustion engines by adaptive network-based fuzzy inference system

© 2018, Institute of Advanced Scientific Research, Inc. All rights reserved. The using of an adaptive network-based fuzzy inference system (ANFIS) for the automatic formation of fuzzy rules governing the operation of internal combustion engines during testing is considered. The topology of the hybrid neural network is determined. An estimation of the adequacy of control based on the fuzzy rules obtained by the hybrid network was carried out. In the article, the input parameters of the ICE are determined, which are necessary for the implementation of the control action. The structure of the neural network and the rules for controlling the engine based on fuzzy logic are determined. Scientific novelty of the article is to develop a technique for determining the control parameters of internal combustion engines based on specified input parameters using a fuzzy system tuned with a neural network. Based on the conducted studies, the accuracy of the ICE simulation based on neural networks and the fuzzy system

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted