Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

Valuation of real estate investments through Fuzzy Logic

This paper aims to outline the application of Fuzzy Logic in real estate investment. In literature, there is a wide theoretical background on real estate investment decisions, but there has been a lack of empirical support in this regard. For this reason, the paper would fill the gap between theory and practice. The fuzzy logic system is adopted to evaluate the situations of a real estate market with imprecise and vague information. To highlight the applicability of the Possibility Theory, we proceeded to reconsider an example of property investment evaluation through fuzzy logic. The case study concerns the purchase of an office building. The results obtained with Fuzzy Logic have been also compared with those arising from a deterministic approach through the use of crisp numbers

Incorporating the Basic Elements of a First-degree Fuzzy Logic and Certain Elments of Temporal Logic for Dynamic Management Applications

The approximate reasoning is perceived as a derivation of new formulas with the corresponding temporal attributes, within a fuzzy theory defined by the fuzzy set of special axioms. For dynamic management applications, the reasoning is evolutionary because of unexpected events which may change the state of the expert system. In this kind of situations it is necessary to elaborate certain mechanisms in order to maintain the coherence of the obtained conclusions, to figure out their degree of reliability and the time domain for which these are true. These last aspects stand as possible further directions of development at a basic logic level. The purpose of this paper is to characterise an extended fuzzy logic system with modal operators, attained by incorporating the basic elements of a first-degree fuzzy logic and certain elements of temporal logic.Dynamic Management Applications, Fuzzy Reasoning, Formalization, Time Restrictions, Modal Operators, Real-Time Expert Decision System (RTEDS)

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

The Application of Fuzzy Logic in Determining Linguistic Rules and Associative Membership Functions for the Control of a Manufacturing Process

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory. Its methodology aims to provide a definitive solution from information that may be construed as ambiguous, imprecise or noisy. Classical set theory studies the properties of sets, while fuzzy set theory investigates the degree to which an element can be related to a set. The aim of this project is to develop a control strategy for a specific technical challenge relating to the food processing sector based on the deployment of fuzzy logic control concepts. Specifically, in this paper the author is concerned with the ability to control the density input of a variable feed product stream by automatically adjusting the „thermo pressure‟ & „feed flow‟ within desired limits. For the purpose of this study, the expert knowledge of both senior automation engineers and process operators was procured in order to develop an understanding of the dynamics and the limitations of the manufacturing process. The focus of this study is the development of a fuzzy logic control system for the production of “Whey Permeate Concentrate” in the production facilities of Glanbia plc. in Ballyragget, County Kilkenny

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity

How are hospitals using artificial intelligence in strategic decision making? —a scoping review

Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decision-making were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise- or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decision-makers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians