Machine fault diagnosis and condition prognosis using classification and regression trees and neuro-fuzzy inference systems
This paper presents an approach to machine fault diagnosis and condition prognosis based on classification and regression trees (CART) and neuro-fuzzy inference systems (ANFIS). In case of diagnosis, CART is used as a feature selection tool to select pertinent features from data set, while ANFIS is used as a classifier. The crisp rules obtained from CART are then converted to fuzzy if-then rules, employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. The data sets obtained from vibration signals and current signals of the induction motors are used to evaluate the proposed algorithm. In case of prognosis, both of these models in association with direct prediction strategy for long-term prediction of time series techniques are utilized to forecast the future values of machine operating condition. In this case, the number of available observations and the number of predicted steps are initially determined by false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models. The performance of the proposed prognosis system is then evaluated by using real trending data of a low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results of the proposed methods in both cases indicate that CART and ANFIS offer a potential for machine fault diagnosis and for condition prognosis.
Bibliogr. 46 poz., rys., wykr.
- ABBAS, M., FERRI, A.A., ORCHARD, M.E.and VACHTSEVANOS, G.J. (2007) An intelligent diagnostic/prognostic framework for automotive electrical systems. Proc. 2007 IEEE Intelligent Vehicles Symposium, Istanbul, 352-357.
- ACOSTA, G.G., VERUCCHI, C.J. and GELSO, E.R. (2006) A current monitoring system for diagnosis electrical failures in induction motors. Mechanical Systems and Signal Processing 20, 953-965.
- ALTUG, S., CHOW, M.Y. and TRUSSELL, H.J. (1999) Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. IEEE Trans. Industrial Electronics 46 (6) 1069-1079.
- BENBOUZID, M.E.H and NEJJARI, H. (2001) A simple fuzzy logic approach for induction motors stator condition monitoring. Proc. IEEE IEMDC 2001, 634-639.
- BREIMAN, L., FRIEDMAN, J.H., OLSHEN, R.A. and STONE, C.J. (1984) Classification and Regression Trees. Chapman & Hall Press.
- BROOMHEAD, D.S. (1986) Extracting qualitative dynamics from experimental data. Physica D 20, 217-236.
- BROWN, E.R., McCOLLOM, N.N., MOORE, E. and HESS, A. (2007) Prognostics and health management - a data-driven approach to supporting the F-35 Lightning II. Proc. of IEEE Aerospace Conference, 1-12.
- BYINGTON, C.S., WATSON, M., ROEMER, M.J., GALIC, T.R. and McGROARTY, J. J. (2003) Prognostic enhancements to gas turbine diagnostic systems. Proc. IEEE Aerospace Conference 7, 3247-3255.
- CAO, L. (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110, 43-50.
- CASIMIR, R., BOUTLEUX, E., CLERC, G. and YAHOUI, A. (2006) The use of feature selection and nearest neighbors rule for faults diagnosis in induction motors. Engineering Applications of Artificial Intelligence 19, 169-177.
- CHEN, Y. (1995) A fuzzy decision system for fault classification under high levels of uncertainty. J. of Dynamic Systems, Measurement, and Control 117, 108-115.
- CHO, K.R., LANG, J.H. and UMANS, S. (1992) Detection of broken rotor bars using state and parameter estimation. IEEE Trans, on Industry Applications 28, 702-713.
- FRASER, A.M. arid SWINNEY, H.L. (1986) Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134-1140.
- GOODE, P. and CHOW, M.Y. (1995) Using a neural/fuzzy to extract knowledge of incipient fault in induction motor: Part 1 - methodology. IEEE Trans. Industrial Electronics 42 (2) 131-138.
- HUANG, R., XI, L., LI, X., LIU, C.R., QIU, H. and LEE, J. (2007) Residual life prediction for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 21, 193-207.
- IKONEN, E. and NAJIM, K. (1996) Fuzzy neural networks and application to the FBC process. Proc. Control Theory Application 143, 259-269.
- ISERMANN, R. (1984) Process fault detection based on modeling and estimation methods - a survey. Automatica 20, 387-404.
- ISERMANN, R. and FREYERMUTH, B. (1991) Process fault diagnosis based on process model knowledge - part I. J. of Dynamic Systems, Measurement, and Control 113, 620-626.
- JANG, J.S.R. (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. System, Man and Cybernetics 23 (3) 665-685.
- JANG, J.S.R., SUN, C.T. and MIZUTANI, E. (1996) Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall.
- JI, Y., HAO, J., REYHANI, N. and LENDASSE, A. (2005) Direct and recursive prediction of time series using mutual information selection. LNCS 3512, Springer, 1010-1017.
- KENNEL, M.B., BROWN, R. and ABARBANEL, H.D.I. (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A 45, 3403-3411.
- KUMAR, R., JAYARAMAN, V.K. and KULKARNI, R.D. (2005) An SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples. Pattern Recognition 38, 41-49.
- LEI, Y., HE, Z., ZI Y. and HU, Q. (2009) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, in print.
- LI, Y., BILLINGTON,S., ZHANG, C., KURFESS, T., DANYLUK,S. and LIANG, S. (1999) Adaptive prognostics for rolling element bearing condition. Mechanical Systems and Signal Processing 13 (1) 103-113.
- LI, Y., KURFESS, T.R. and LIANG, S.Y. (2000) Stochastic prognostics for rolling element bearings. Mechanical Systems and Signal Processing 14 (5) 747-762.
- LOU, X. and LOPARO, K.A. (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 18, 1077-1095.
- LOU, M., WANG, D., PHAM, M., LOW, C.B., ZHANG, J.B., ZHANG, D.H. and ZHAO, Y.Z. (2005) Model-based fault diagnosis/prognosis for wheeled mobile robots: a review. Proc. 31st Annual Conference of IEEE Industrial Electronics Society, New York, 2267-2272.
- ROSENSTEIN, M.T., COLLINS, J.J. and LUCA, C.J.D. (1994) Reconstruction expansion as a geometry-based framework for choosing proper delay time. Physica D 73, 82-89.
- SATISH, B. and SAMAR, N.D.R. (2005) A fuzzy approach for diagnosis and prognosis of bearing faults in induction motors. IEEE Power Engineering Society General Meeting 3, 2291-2294.
- SCHWABACHER, M. and GOEBEL, K. (2007) A survey of artificial intelligence for prognostics. Proc. AAAI Fall Symposium on Artificial Intelligence for Prognostics, 9-11 Nov. 2007.
- SHUKRI, M., KHALID, M., YUSUF, R. and SHAFAWI, M. (2004) Induction machine diagnostic using adaptive neuron fuzzy inference system. In: M. Gh. Negoitaet al., eds., KES2004, 380-387.
- SOOD, A.K.. FAHS, A.A. and HENEIN, N.A. (1985) Engine fault analysis -part I: statistical methods; part II: Parameter estimation approach. IEEE Trans, on Industrial Electronics 32, 294-300 and 301-307.
- SORJAMAA, A., HAO, J., REYHANI, N., JI, Y.and LENDASSE, A. (2007) Methodology for long-term prediction of time series. Neurocomputing 70, 2861-2869.
- SORJAMAA, A. and LENDASSE, A. (2007) Time series prediction as a problem of missing values: application to ESTSP and NN3 competition benchmarks. Proc. European Symposium on Time Series Prediction, 165-174.
- TRAN, V.T., YANG, B.S., OH, M.S. and TAN, A.C.C. (2008) Machine condition prognosis based on regression trees and one-step-ahead prediction. Mechanical Systems and Signal Processing 22, 1179-1193.
- TRAN, V.T., YANG, B.S. and TAN, A.C.C. (2009) Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems. Expert Systems with Applications 36, 9378-9387.
- TU, F., GHOSHAL, S., LUO, J., BISWAS, G., MAHADEVAN, S., JAW, L. and NAVARRA, K. (2004) PHM integration with maintenance and inventory management systems. IEEE Aerospace Conference Digest, Motana, 1-12.
- VACHTSEVANOS, G. and WANG, P. (2001) Fault prognosis using dynamic wavelet neural networks. Proc. IEEE Systems Readiness Technology Conference, 857-870.
- WANG, W.Q., GOLNARAGHI, M.F. and ISMAIL, F. (2004) Prognosis of machine health condition using neuro-fuzzy system. Mechanical Systems and Signal Processing 18, 813-831.
- WATSON, M., BYINGTON, C., EDWARDS, D. and AMIN, S. (2005) Dynamic modeling and wear-based remaining useful life prediction of high power clutch systems. Tribology Trans. 48 (2) 208-217.
- WIDODO, A., YANG, B.S. and HAN, T. (2007) Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications 32 (2) 299-312.
- YANG, B.S., HAN, T. and YIN, Z.J. (2006) Fault diagnosis system of induction motors using feature extraction, feature selection and classification algorithm. JSME Int. J. (C) 49 (3) 734-741.
- YANG, B.S. and KIM, K.J. (2006) Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing 20, 403-420.
- YANG, B.S., JEONG, S.K., OH, Y.M. and TAN, A.C.C. (2004) Case-based reasoning with Petri nets for induction motors fault diagnosis. Expert Systems with Applications 27 (2) 301-311.
- YE, Z., SADEGHIAN, A. and WU, B. (2006) Mechanical fault diagnosis for induction motor with variable speed drives using adaptive neuro-fuzzy inference system. Electric Power Systems Research 76, 742-752.