Multi-criteria decision making using Fuzzy Logic and ATOVIC with application to manufacturing

In this paper multi-criteria decision making (MCDM) is investigated as a framework for classification of part quality in a manufacturing process. The importance of linguistic interpretability of decisions is highlighted, and a new framework relying on the integration of Fuzzy Logic and an existing MCDM method is proposed. ATOVIC, previously developed as a TOPSIS-VIKOR-based MCDM framework is enhanced with a Fuzzy Logic framework for decision making - Fuzzy-ATOVIC. This research work demonstrates how to add linguistic interpretability to decisions made by the MCDM framework. This contributes to explainable decisions, which can be crucial on numerous domains, for example on safety-critical manufacturing processes. The case study presented is the one of ultrasonic inspection of plastic pipes, where thermomechanical joining is a critical part of the manufacturing process. The proposed framework is used to classify (take decisions) on the quality of manufactured parts using ultrasonic images around the joint region of the pipes. For comparison, both the original and the Fuzzy Logic-enhanced MCDM methods are contrasted using data from manufacturing trials and subsequent ultrasonic testing. It is shown, that Fuzzy-ATOVIC provides a framework for linguistic interpretability while the performance is the same or better compared to the original MCDM framework

MAT-713: EVALUATION OF NDT TECHNIQUES FOR CONCRETE BRIDGE DECKS USING FUZZY ANALYTICAL HIERARCHY PROCESS

Considering the colossal backlog of deteriorating bridges, transportation agencies need to systematically evaluate bridge deck conditions in order to optimize the timing, scope, and approach of preventive maintenance, repair, and replacement. Over the last few years, there have been growing interest among bridge infrastructure stakeholders in using non-destructive methodologies for bridge inspection, evaluation, and maintenance. Nondestructive testing (NDT) techniques can provide needed information about the “under-the-surface” deteriorated condition of bridge decks. This paper examines the most common NDT technologies for assessing bridge decks. Each technology was rated based on five performance measures: capability to detect subsurface defects, speed of data collection, simplicity of analysis and interpretation, accuracy of results, and cost of measurement. The study has particular emphasis on reinforcement corrosion, delamination, and internal cracking. The information sought to identify the significance of the factors affecting the analysis process was collected through a survey questionnaire. In order to incorporate the imprecise information and vagueness of human judgment in the decision-making, the fuzzy analytical hierarchy process (FAHP) is employed, as per the fuzzy preference programming method. Results demonstrate the capabilities of each technology and its ability to address bridge challenges. In order to assist bridge engineers and decision makers, recommendations were made with respect to the selection of the most appropriate technologies to identify specific deterioration mechanisms

Diagnosis of MEA degradation for health management of polymer electrolyte fuel cells

Diagnostics and health management are fundamental components in a strategy to improve durability and lifetime of polymer electrolyte fuel cells. Fuel cells require a range of operating conditions to be well managed for achieving performance or durability objectives. So far, water management issues and single parameter diagnostics for individual degradation modes have been the focus of research in the literature. However, there has been minimal research on the application of fuzzy inference systems for online, multiple parameter diagnosis of fuel cells. This research presents an advanced fuzzy inference system for diagnostics and health management of a membrane electrode assembly (MEA) for polymer electrolyte fuel cells. The fuzzy inference system facilitates simplified connections of the complex relationships between numerous operating conditions and subsequent degradation modes. The approach utilises the most important operating parameters for diagnosis of high priority degradation modes using multiple health sensors. The developed fuzzy inference system classifies the fuel cell input data into simple linguistic categories for example ‘cell voltage is very high’ or ‘stack temperature is low’ through a fuzzification process. Based on a set of antecedent-consequent (if-then) rules, an inference calculation is performed without necessity for complex mathematical models. This enables a fast diagnosis with fuel cell parameters classified on a scale of inclusion to the linguistic categories. The linguistic classification of a degradation mode is converted back into a numerical value through a defuzzification process. The output data can be used to inform the user on the fuel cell state of health. The investigation has focused on the diagnosis of MEA degradation as it has been identified as having critical impact on fuel cell performance and lifetime. A single cell with a 25cm2 active area was used for testing under numerous moderate to extreme operating conditions known to cause membrane and electro-catalyst degradation. A database of if-then rules was initially developed based on knowledge in the literature and refined with experimental testing. Results so far have supported validation of the fuzzy inference system membership functions and the rule base for diagnosing the consequential degradation modes based on fuel cell operating conditions. This diagnostic and health management approach facilitates proactive decision making for mitigation strategies to be employed according to performance or lifetime targets and can increase fuel cell availability and lifetime therefore improving the overall value of the system.</div

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability

In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting classifier complexity and transparency is comparable with classifiers generated during a single crossvalidation procedure while the improved classification performance and reduced variance is comparable to the ensemble of classifiers with combined (averaged/voted) decisions. We also illustrate how combining at the model level can be used for speeding up the training of GFMM classifiers for large data sets

Fuzzy investment decision support for brownfield redevelopment

Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.This dissertation focuses on decision making, investing and brownfield redevelopment. Especially on the analysis, evaluation and selection of previously used real estates suitable for commercial use. The objective of this dissertation is to design a method that facilitates the decision making process with many possible alternatives and large number of relevant parameters influencing the decision. The proposed method is based on the use of fuzzy logic, modeling, statistic analysis, cluster analysis, graph theory and sophisticated methods of information collection and processing. New method allows decision makers to process much larger amount of information and evaluate possible investment alternatives efficiently.