Using support vector machines and acoustic noise signal for degradation analysis of rotating machinery

Artificial Intelligence Review

Volumn 28, Issue 1 SPEC. ISS., 2007, Pages 1-15

  Scanlon, Patricia ^a   Bergin, Susan ^b  

^a LUCENT TECHNOLOGIES   (United States)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Acoustic signal processing; Degradation analysis; Feature selection; Support vector machines

Indexed keywords

ACOUSTIC NOISE; ACOUSTIC SIGNAL PROCESSING; ACOUSTIC WAVES; ACOUSTICS; ARTIFICIAL INTELLIGENCE; CLASSIFIERS; CLUSTERING ALGORITHMS; DEGRADATION; FEATURE EXTRACTION; FEEDFORWARD NEURAL NETWORKS; GEARS; IMAGE RETRIEVAL; IMAGE SEGMENTATION; LEARNING SYSTEMS; MACHINERY; MULTILAYER NEURAL NETWORKS; PROBABILITY; PROBABILITY DISTRIBUTIONS; QUALITY ASSURANCE; RADIAL BASIS FUNCTION NETWORKS; ROTATING MACHINERY; ROTATION; SIGNAL PROCESSING; UNDERWATER ACOUSTICS; VECTORS;

ACOUSTIC DATUMS; ACOUSTIC SIGNALS; AUTOMATED APPROACHES; CLASS LABELS; DEGRADATION ANALYSIS; FEATURE SELECTION; FEATURE SPACES; HIGH RESOLUTIONS; IRRELEVANT FEATURES; LIFE-TIMES; LINEARLY SEPARABLE; MISCLASSIFICATIONS; PREVENTATIVE MAINTENANCES; PROBLEM SPACES; RADIAL BASIS FUNCTIONS; REMAINING USEFUL LIVES; ROTATING MACHINES; SPECTRAL FEATURES;

SUPPORT VECTOR MACHINES;

EID: 57649240556     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-008-9081-6     Document Type: Article

References (24)

1. D Bell H Wang 2000 A formalism for relevance and its application in feature subset selection Mach Learn 41 2 175 195
2. C Burges 1998 A tutorial on support vector machines for pattern recognition Knowl Discov Data Min 2 2 121 167
3. Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York
4. D Doane 1985 Aesthetic frequency classifications Am Stat 30 181 183
5. S Goumas M Zervakis G Stavrakakis 2002 Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction IEEE Trans Instrum Meas 51 3 497 508
6. R Heng M Nor 1998 Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition Appl Acoust 53 211 226
7. D Ho RB Randall 2000 Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals Mech Syst Signal Process 14 5 763 788
8. CW Hsu CJ Lin 2002 A comparison of methods for multiclass support vector machines IEEE Trans Neural Netw 13 2 415 425
9. V Jayaram F Jarchow 1978 Experimental studies on ball bearing noise Wear 46 321 326
10. Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Hérault J, Fogelman F (eds) Neurocomputing, algorithms, architectures and applications. Springer-Verlag, Berlin, pp 41-50
11. H Lao S Zein-Sabatto 2001 Analysis of vibration signal's time-frequency patterns for prediction of bearing's remaining useful life Proc 33rd Southeast Symp Syst Theory 1 25 29
12. J MacQueen 1967 Some methods for classification and analysis of multivariate observations Proc 5th Berkeley Symp Math Stat Probab 1 281 297
13. P McFadden J Smith 1984 Vibration monitoring of rolling element bearings by the high frequency resonance technique a review Tribol Int 17 1 1 18
14. Milgram J, Cheriet M, Sabourin R (2006) 'one against one' or 'one against all': which one is better for handwriting recognition with svms? Tenth International Workshop on Frontiers in Handwriting Recognition
15. Platt J (2000) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola A, Bartlett P, Schölkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge, MA, USA, pp 61-74
16. Proakis J, Manolakis D (1996) Digital signal processing-principles, algorithms and applications. Prentice Hall, Englewood Cliffs, NJ, USA
17. Rojas A, Nandi A (2005) Detection and classification of rolling-element bearing faults using support vector machines. IEEE Workshop on Machine Learning for Signal Processing, pp 153-158
18. B Samanta K Al-Balushi S Al-Araimi 2003 Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection Eng Appl Artif Intell 16 7 657 665
19. P Scanlon D Ellis R Reilly 2007 Using broad phonetic group experts for improved speech recognition IEEE Trans Speech Audio Process 15 3 803 812
20. Scanlon P, Lyons A, O'Loughlin A (2007b) Acoustic signal processing for degradation analysis of rotating machinery to determine the remaining useful life. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
21. ScholKopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MA, USA
22. D Shi W Wang L Qu 2004 Defect detection for bearings using envelope spectra of wavelet transform J Vib Acoust 126 4 567 573
23. N Tandon A Choudhury 1999 A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings Tribol Int 32 469 480
24. N Tandon B Nakra 1990 The application of the sound intensity technique to defect detection in rolling element bearings Appl Acoust 29 3 207 217