Abstract
A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.
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References
Shiroishi J, Li Y, Liang S, Kurfess T, Danyluk S, (1997) Bearing condition diagnostics via vibration and acoustic emission measurements. Mech Syst Signal Processing 11:693–705
McFadden PD (2000) Detection of gear faults by decomposition of matched differences of vibration signals. Mech Syst Signal Processing 14:805–817
Nandi, AK (2000) Advanced digital vibration signal processing for condition monitoring. In: Proceedings of COMADEM 2000, Houston, pp 129–143
Mechanical systems and signal processing (2001) 15 (5), Special issue on gear and bearing diagnostics. (Randall, guest editor)
Al-Balushi KR, Samanta B (2002) Gear fault diagnosis using energy-based features of acoustic emission signals. In: Proceedings of IMechE, Part I: J Syst Control Eng 216(I3):249–263
Antoni J, Randall RB (2002) Differential diagnosis of gear and bearing faults. Trans ASME J Vibration Acoustics 124:165–171
McCormick AC, Nandi AK (1997) Classification of the rotating machine condition using artificial neural networks. In: Proceedings of IMechE, Part C: J Mech Eng Sci 211(C6):439–450
Dellomo MR (1999) Helicopter gearbox fault detection: a neural network based approach. Trans ASME J Vibration Acoustics 121:265–272
Samanta B, Al-Balushi KR (2001) Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system. In: Proceedings of IMechE, Part I: J Syst Control Eng 215(I3):199–207
Samanta B, Al-Balushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Processing 17:317–328
IEEE Transactions on Neural Network (1997) 8 (1), Special Issue on Artificial Neural Networks and Statistical Pattern Recognition. (AK Jain and J Mao guest editors)
Baraldi A, Borghese NA (1998) Learning from data: general issues and special applications of Radial Basis Function networks. In: Technical report of international computer science research institute, Berkeley, TR-98-028
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Hornik K, Stinchcombe S, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366
Park J, Sandberg IW (1993) Universal approximation using radial-basis-function networks. Neural Comput 5:305–316
Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118
Wasserman PD (1995) Advanced methods in neural computing. Van Nostrand Reinhold, New York, pp 35–55
Yao X (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447
Jack LB, Nandi AK, McCormick AC (1999) Diagnosis of rolling element bearing faults using radial basis functions. Appl Signal Processing 6:25–32
Jack LB, Nandi AK (2000) Comparison of neural networks and support vector machines in condition monitoring applications. In: Proceedings of COMADEM 2000, Houston, pp 721–730
Jack LB (2000) Applications of artificial intelligence in machine condition monitoring. PhD Thesis, Department of Electrical Engineering and Electronics, the University of Liverpool
Jack LB, Nandi AK (2000) Genetic algorithms for feature extraction in machine condition monitoring with vibration signals. IEE Proc Vision Image Signal Processing 147:205–212
Samanta B, Al-Balushi KR, Al-Araimi SA (2001) Use of genetic algorithm and artificial neural network for gear condition diagnostics. In: Proceedings of COMADEM, 2001, University of Manchester, UK, pp 449–456
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd ed. Prentice Hall, New Jersey
Goldberg GE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, New York
Michalewicz Z (1999) Genetic algorithms + data structures = evolution programs, 3rd edition, Springer, Berlin Heidelberg New York
Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Processing Magazine 13:22–37
Houk CR, Joines J, Kay MA (1995) Genetic algorithm for function optimization: a matlab implementation. North Carolina State University, Report no: NCSU_IE_TR_95_09
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Samanta, B., Al-Balushi, K. & Al-Araimi, S. Artificial neural networks and genetic algorithm for bearing fault detection. Soft Comput 10, 264–271 (2006). https://doi.org/10.1007/s00500-005-0481-0
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DOI: https://doi.org/10.1007/s00500-005-0481-0