Abstract
When searching for faults threatening a system, the human expert is sometimes performing an amazingly accurate analysis of available information, frequently by using only elementary statistics. Such reasoning is referred to as “fuzzy reasoning,” in the sense that the expert is able to extract and analyse the essential information of interest from a data set strongly affected by uncertainty. Automating the reasoning mechanisms that represent the foundation of such an analysis is, in general, a difficult attempt, but also a possible one, in some cases. The chapter introduces a nonconventional method of fault diagnosis, based upon some statistical and fuzzy concepts applied to vibrations, which intends to automate a part of human reasoning when performing the detection and classification of defects.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Angelo M (1987) Vibration Monitoring of Machines. Bruel & Kjiaer Technical Review 1:1–36
Barkov AV, Barkova NA, Mitchell JS (1995a) Condition Assessment and Life Prediction of Rolling Element Bearings — Part 1. Journal of Sound and Vibration 6:10–17, June 1995 (http://www.inteltek.com/articles/sv95/part1/index.htm)
Barkov AV, Barkova NA, Mitchell JS (1995b) Condition Assessment and Life Prediction of Rolling Element Bearings — Part 2. Journal of Sound and Vibration 9:27–31, September 1995 (http://www.inteltek.com/articles/sv95/part2/index.htm)
Bedford A, Drumheller DS (1994) Introduction to Elastic Wave Propagation. John Wiley & Sons, Chichester, UK
Braun S (1986) Mechanical Signature Analysis. Academic Press, London, UK
Cohen L (1995) Time-Frequency Analysis. Prentice Hall, New Jersey, USA
FAG OEM & Handel AG (1996) Wälzlagerschäden — Schadenserkennung und Begutachtung gelaufener Wälzlager. Technical Report WL 82 102/2 DA
FAG OEM & Handel AG (1997) Rolling Bearings — State-of-the-Art, Condition-Related Monitoring of Plants and Machines with Digital FAG Vibration Monitors. Technical Report WL 80-65 E
Howard I (1994) A Review of Rolling Element Bearing Vibration: Detection, Diagnosis and Prognosis. Report of Defense Science and Technology Organization, Australia
Isermann R (1993) Fault Diagnosis of Machines via Parameter Estimation and Knowledge Processing. Automatica 29(4):161–170
Isermann R (1997) Knowledge-Based Structures for Fault Diagnosis and its Applications. In: Proceedings of the 4th IFAC Conference on System, Structure and Control, SSC’97, Bucharest, Romania, pp.15–32
Kaiser JF (1974) Nonrecursive Digital Filter Design Using the I0-sinh Window Function. In: Proceedings of the IEEE Symposium on Circuits and Systems, pp.20–23
Klir GJ, Folger TA (1988) Fuzzy sets, Uncertainty, and Information. Prentice Hall, New York, USA
LMS International (1999) LMS Scalar Instruments Roadrunner. User Guide. LMS Scalar Instruments Printing House, Leuven, Belgium
Maness PhL, Boerhout JI (2001) Vibration Data Processor and Processing Method. United States Patent No. US 6,275,781 B1 (http://www.uspto.gov/go/ptdl/)
McConnell KG (1995) Vibration Testing. Theory and Practice. John Wiley & Sons, New York, USA
Oppenheim AV, Schafer R (1985) Digital Signal Processing. Prentice Hall, New York, USA
Proakis JG, Manolakis DG (1996) Digital Signal Processing. Principles, Algorithms and Applications (third edition). Prentice Hall, Upper Saddle River, New Jersey, USA
Reiter R (1987) A Theory of Diagnosis from First Principles. Artificial Intelligence 32: 57–95
Söderström T, Stoica P (1989) System Identification. Prentice Hall, London, UK
Stefanoiu D, Ionescu F (2002) Mathematical Models of Defect Encoding Vibrations. A Tutorial. Journal of the American-Romanian Academy (ARA), Montréal, Canada, Vol. 2001–2002
von Tscharner V (2000) Intensity Analysis in Time-Frequency Space of Modelled Surface Myoelectric Signals by Wavelets of Specified Resolution, preprint
Ulieru M, Stefanoiu D, Norrie D (2000) Identifying Holonic Structures in Multi-Agent Systems by Fuzzy Modeling. In: Kusiak A & Wang J (eds) Art for Computational Intelligence in Manufacturing, CRC Press, Boca Raton, Florida, USA
Willsky AS (1976) A Survey of Design Methods for Failure Detection Systems. Automatica 12:601–61
Wowk V (1995) Machinery Vibration. Balancing. McGraw-Hill, Upper Saddle River, New York, USA
Xi F, Sun Q, Krishnappa G (2000) Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters. Journal of Vibration and Control 6:375–392
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag London Limited
About this chapter
Cite this chapter
Stefanoiu, D., Ionescu, F. (2006). Fuzzy-Statistical Reasoning in Fault Diagnosis. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_5
Download citation
DOI: https://doi.org/10.1007/978-1-84628-631-5_5
Publisher Name: Springer, London
Print ISBN: 978-1-84628-343-7
Online ISBN: 978-1-84628-631-5
eBook Packages: Computer ScienceComputer Science (R0)