Machine Learning Strategy for Fault Classification Using Only Nominal Data
Machine learning methods are increasingly used for rotating machinery monitoring. Usually at set up, only data associated to an engine in a good state, the so called nominal data, are available for the machine learning phase. Nevertheless a classifier requires faulty data to be trained at identifying the causes of the anomalies and this fact has generally limited the usage of data driven approaches to fault detection tasks. The paper suggests a strategy to use machine learning methods even for fault classification purposes and diagnostics. Within the proposed framework three different machine learning methods, Gaussian Mixture Model (GMM), Support Vector Machines (SVM) and Auto Associative Neural Networks (AANN) have been implemented, tested and compared. The idea is to take into account some ‘a priori’ knowledge about the faults to be classified, to drive the behavior of the machine learning methodology (SVM or AANN or GMM) to be more or less reactive to the different faults. The indicators (features) more sensitive to each kind of fault are firstly selected on the basis of expert knowledge. For each different fault, a set of indicators is defined and computed from nominal data only. Each set is then used to produce training data for one specific fault. Such data sets are then used to train one instance of each method for each different fault. The underlying logic is that fault tuned input data is able to produce fault tuned instances of the methods. For example the instance trained with the indicators associated to a fault ‘A’ reacts more powerfully in presence of the fault ‘A’ than the others. Once an anomaly is detected, the comparison among the reactions of the different ‘fault tuned’ instances allows classifying the fault, not just to detect it. The results show best detection performances for SVM whilst AANN outperforms the other two methods for classification.
How to Cite
Automatic diagnostics, Auto-Associative Neural Network, rotating machinery
Bilmes, J. A. (1998). A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Berkeley: International computer science institute.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer Science+Business Media.
Booth, C., & McDonald, J. R. (1998). The use of artificial neural networks for condition monitoring of electrical power transformers. Neurocomputing, 97-109.
Fulufhelo, V. N., Tshilidzi, M., & Unathi, M. (2005). Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency cepstral coefficients and fractals. International Journal of Innovative Computing, Information and Control, 1281-1299.
Guttormsson, S., Marks, R., El-Sharkawi, M., & Kerszenbaum, I. (1999). Elliptical novelty grouping for on-line short-turn detection of excited running rotors. Energy Conversion, IEEE Transactions on, 16-22.
Jack, L. B., & Nandi, A. K. (2002). Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing, 373-390.
Johannes, M. D. (2001). One-class classification. Delft: Advanced School for Computing and Imaging.
Kramer, M. A. (1992). Autoassociative neural networks. Computers & chemical engineering, 16(4), 313-328.
Ng, A. (2015, September 21). Machine Learning Course Materials. Retrieved January 15, 2016, from http://cs229.stanford.edu/materials.html
Prego, T. d., de Lima, A. A., Netto, S. L., da Silva, E. A., Gutierrez, R. H., Monteiro, U. A., . . . Vaz, L. (2013). On Fault Classification in Rotating Machines using Fourier Domain Features and Neural Networks. Circuits and Systems (LASCAS), 2013 IEEE Fourth Latin American Symposium on, 1-4.
Rojas, A., & Nandi, A. K. (2006). Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines. Mechanical Systems and Signal Processing 20, 1523-1536.
Rubio, E., & Jáuregui, J. C. (2011). Time-Frequency Analysis for Rotor-Rubbing Diagnosis. In F. Ebrahimi, Advances in Vibration Analysis Research (pp. ISBN: 978-953-307-209-8, InTech, DOI:10.5772/15186).
Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2003). Artificial neural networks and support vector machines with genetix algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 657-665.
Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration 302, 981-999.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., & Smola, A. J. (2001). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443-1471.
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