Warianty tytułu
Języki publikacji
Abstrakty
Vibrodiagnosis helps in detecting incipient faults in rotating machines like pumps and generators. Early detection prevents from undesired breakdown of the machine and allows to schedule maintenance times. The application of neural networks in classification of the rotating machine condition has been described in this work. Different types of networks and methods on feature extraction was described and compared. Additionally it was proposed a novelty feature set consisted of harmonics from vibration spectrum. The set were combined with using of probabilistic neural networks which has been modified that it could recognize defects that did not occur in the training set. Such architecture was tested in detection of two defects, shaft misalignment and mass unbalance. It was found that such network works better than a multi layered perceptron with statistical features.
Czasopismo
Rocznik
Tom
Strony
21-44
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
- Institute of Computer Science, Jagiellonian University Prof. Stanisława Łojasiewicza 6, 30-348 Kraków, Poland, tomasz.romaniuk@gmail.com
Bibliografia
- [1] Reliability direct, http://www.reliabilitydirect.com. [22]
- [2] van der Merwe N.T., Hoffman A.J.; The application of neural networks to vibrational diagnostics for multiple fault conditions, Computer Standards & Interfaces, 24, 2002. [27]
- [3] Ebersbach S., and Peng Z.; Expert system development for vibration analysis in machine condition Monitoring, Expert Systems with Applications, 34, 2008.[25]
- [4] McCormick A.C., Nandi A.K.; Classification of the rotating machine condition using artificial neural networks, Proceedings of the IMechE, 1997. [26, 32, 36,38, 40]
- [5] Samanta B., Al-Balushi K.R.; Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing, 17(2), 2001. [24, 32, 36, 38, 40]
- [6] Samanta B., Al-Balushi K.R., Al-Araimi S.A.; Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence, 16, 2003. [26, 32, 40]
- [7] Specht D.F.; Probabilistic neural networks for classification, mapping, or associative memory, Proceedings of the IEEE International Conference on Neural Networks, 1, 1988. [28]
- [8] Specht D.F.; Probabilistic neural networks, Neural Networks, 3, 1990. [28]
- [9] Wang C.-C., Too G.-P.-J., Rotating machine fault detection based on hos and artificial neural Networks, Journal of Intelligent Manufacturing, 2002. [28]
- [10] Yanga B.-S., Limb D.-S., Tan A.C.C.; Vibex: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table monitoring, Expert Systems with Applications, 28, 2005. [25]
- [11] Zhang L., Lindsay B.J., Nandi A.K.; Fault detection using genetic programming, Mechanical Systems and Signal Processing, 19, 2005. [26, 38]
- [12] Zhong B., MacIntyre J., He1 Y., and Tait J.; High order neural networks for simultaneous diagnosis of multiple faults in rotating machines, Neural Computing & Applications, 8, 1999. [27]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ7-0007-0078