The Role of Expert Systems In Condition Monitoring

1993 ◽  
pp. xv-xxxii
Author(s):  
Robert Milne
Keyword(s):  
Expert Systems ◽  
Condition Monitoring

A State-of-the-Art Critique Review of the Role of Decision-Making Techniques in NDT for Condition Monitoring

2019 ◽  
pp. 222-236
Author(s):  
Ashish Khaira ◽  
Ravi K. Dwivedi
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Research Work ◽  
Research Papers ◽  
Operating Personnel ◽  
Detailed Review ◽  
Effective Condition ◽  
Year 2000 ◽  
Selection Of

Nondestructive testing (NDT) is a vital tool in maintenance. Each NDT technique has some benefits and hindrances; therefore, the selection is crucial. Generally, the selection of a technique relies on operating personnel experience, and very few research papers shows uses of the decision-making (DM) approach. It was highlighted by various researchers that if a proper DM approach is used, it will save time and increase fault detection reliability. By keeping this fact in mind, this chapter is an attempt to provide a detailed review of research work from the year 2000-2018 that covered the role of DM techniques while making combinations of NDT for effective condition monitoring. It observed from the literature that very few researchers effectively utilized the power of DM tool. The researcher can use the outcome of this work as a beacon and improve it further.

scholarly journals The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

2019 ◽  
Vol 28 (01) ◽  
pp. 027-034 ◽  
Author(s):  
Laszlo Balkanyi ◽  
Ronald Cornet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Frequency Analysis ◽  
Medical Subject Headings ◽  
Subject Headings

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

Expert Systems and the Role of Internal Audit

1990 ◽  
pp. 145-147
Author(s):  
J. C. van Dijk ◽  
Paul Williams
Keyword(s):  
Expert Systems ◽  
Internal Audit

The Strategic Role of Expert Systems in UK Organisations

1998 ◽  
pp. 171-176
Author(s):  
Elayne W. Coakes ◽  
Kim Merchant
Keyword(s):  
Expert Systems ◽  
Strategic Role

Controlling power systems during emergencies: the role of expert systems

1989 ◽  
Vol 2 (2) ◽  
pp. 41-45 ◽  
Author(s):  
D.S. Kirschen ◽  
B.F. Wollenberg ◽  
G.D. Irisarri ◽  
J.J. Bann ◽  
B.N. Miller
Keyword(s):  
Power Systems ◽  
Expert Systems

On the role of expert systems and numerical taxonomy in soil classification

1989 ◽  
Vol 40 (2) ◽  
pp. 223-234 ◽  
Author(s):  
M. B. DALE ◽  
A. B. McBRATNEY ◽  
J. S. RUSSELL
Keyword(s):  
Expert Systems ◽  
Numerical Taxonomy ◽  
Soil Classification

The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics

1999 ◽  
Vol 121 (4) ◽  
pp. 607-612 ◽  
Author(s):  
H. R. DePold ◽  
F. D. Gass
Keyword(s):  
Neural Networks ◽  
Expert Systems ◽  
Condition Monitoring ◽  
Intelligent System ◽  
Diagnostic Tools ◽  
Diagnostic Systems ◽  
Recent Developments ◽  
Gas Generators ◽  
Internet Link ◽  
Airline Fleet

Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

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