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Condition Monitoring Using Computational Intelligence Methods de Tshilidzi Marwala

Condition Monitoring Using Computational Intelligence Methods

Autor Tshilidzi Marwala
en Limba Engleză Hardback – 23 ian 2012
Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:
•
fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.
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Specificații

ISBN-13: 9781447123798
ISBN-10: 1447123794
Pagini: 252
Ilustrații: XVI, 236 p.
Dimensiuni: 160 x 241 x 20 mm
Greutate: 0.5 kg
Ediția:2012
Editura: Springer
Locul publicării:London, United Kingdom

Public țintă

Research

Cuprins

Introduction to Condition Monitoring.- Data Gathering Methods.- Preprocessing and Feature Selection.- Condition Monitoring Using Neural Networks.- Condition Monitoring Using Support Vector Machines.- Condition Monitoring Using Neuro-fuzzy Methods.- Condition Monitoring Using Neuro-rough Methods.- Condition Monitoring Using Hidden Markov Models and Gaussian Mixture Models.- Condition Monitoring Using Hybrid Techniques.- Condition Monitoring Using Incremental Learning with Genetic Algorithms.- Conclusion.

Notă biografică

Tshilidzi Marwala is the Vice-Chancellor and Principal of the University of Johannesburg. He was previously the Deputy Vice Chancellor: Research and Internationalization and a Dean of the Faculty of Engineering at the University of Johannesburg. He was previously a full Professor of Electrical Engineering, the Carl and Emily Fuchs Chair of Systems and Control Engineering as well as the SARChI chair of Systems Engineering at the University of the Witwatersrand. Prior to this, he was an executive assistant to the technical director at the South African Breweries. He holds a Bachelor of Science in Mechanical Engineering (magna cum laude) from Case Western Reserve University (USA), a Master of Mechanical Engineering from the University of Pretoria, a PhD in Engineering from Cambridge University and was a post-doctoral research associate at the Imperial College (London). He is a registered professional engineer, a Fellow of TWAS, the World Academy of Sciences, the Academy of Science of South Africa (ASSAf), the African Academy of Sciences and the South African Academy of Engineering. He is a Senior Member of the IEEE (Institute of Electrical and Electronics Engineering) and a distinguished member of the ACM (Association for Computing Machinery). His research interests are multi-disciplinary and they include the theory and application of computational intelligence to engineering, computer science, finance, social science and medicine. He has supervised 47 Masters and 21 PhD students to completion. He has published 9 books (one translated into Mandarin), over 280 papers and holds three international patents. He is an associate editor of the International Journal of Systems Science (Taylor and Francis Publishers).
Evan Hurwitz is a South African computer scientist. He obtained his BSc Engineering (Electrical) (2004), his MSc Engineering (2006) from the University of the Witwatersrand and PhD from the University of Johannesburg in
2014 supervized by Tshilidzi Marwala. He is known for his work on teaching a computer how to bluff which was widely covered by the magazine New Scientist. Hurwitz together with Tshilidzi Marwala proposed that there is less level of information asymmetry between two artificial intelligent agents than between two human agents and that the more artificial intelligent there is in the market the less is the volume of trades in the market.

Textul de pe ultima copertă

Condition monitoring uses the observed operating characteristics of a machine or structure to diagnose trends in the signal being monitored and to predict the need for maintenance before a breakdown occurs. This reduces the risk, inherent in a fixed maintenance schedule, of performing maintenance needlessly early or of having a machine fail before maintenance is due either of which can be expensive with the latter also posing a risk of serious accident especially in systems like aeroengines in which a catastrophic failure would put lives at risk. The technique also measures responses from the whole of the system under observation so it can detect the effects of faults which might be hidden deep within a system, hidden from traditional methods of inspection.
Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as:
·        fuzzy systems;
·        rough and neuro-rough sets;
·        neural and Bayesian networks; ·        hidden Markov and Gaussian mixture models; and
·        support vector machines.
On-line learning methods such as Learn++ and ILUGA (incremental learning using genetic algorithms) are used to enable the classifiers to take on additional information and adjust to new condition classes by evolution rather than by complete retraining. Both the chosen methods have good incremental learning abilities with ILUGA, in particular, not suffering from catastrophic forgetting.
Researchers studying computational intelligence and its applications will find Condition Monitoring Using Computational Intelligence Methods to be an excellent source of examples. Graduate students studying condition monitoring and diagnosis will find this alternative approach to the problem of interest and practitioners involved in fault diagnosis will be able to use these methods for the benefit of their machines and of their companies.

Caracteristici

Helps the practitioner prevent machine failure, improving safety and cutting costs Shows the reader how computational intelligence can provide an efficacious alternative to traditional visual inspection techniques Uses on-line learning methods to avoid the need for time-consuming systems retraining