Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach
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Published
Jan 1, 2019
Alamelu Manghai T. M
Jegadeeshwaran R
Abstract
Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.
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Keywords
Histogram Features, Confusion matrix., FURIA, RIPPER, J48 decision tree algorithm
References
Alamelu Manghai, T.M., Jegadeeshwaran, R., & Sugumaran, V. (2018). Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm. International Journal of Performability Engineering. Vol. 14(1), pp. 1-8.
Ana Palacios., Luciano Sanchez., Ines Couso., & Sebastien Destercke. (2016). An extension of the FURIA classification algorithm to low quality data through fuzzy rankings & its application to the early diagnosis of dyslexia. Neurocomputing. Vol. 176, pp. 60 - 71.
Bostrom, H. (2004) Pruning & exclusion criteria for unordered incremental reduced error pruning. Proceedings of the Workshop on Advances in Rule Learning, ECML. Pp. 17 – 29.
Chapelle., Olivier., Patrick Haffner., & Vladimir, N. Vapnik. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks. Vol. 10(5), pp. 1055 - 1064.
Eineborg, M., & Bostrom, H. (2001). Classifying uncovered examples by rule stretching. International Conference on Inductive Logic Programming. Springer-Verlag. Pp. 41 - 50.
Erdem, Ergin., & Jing Shi. (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy. Vol. 88(4), pp. 1405 - 1414.
Faris Elashaa., Matthew Greaves., David Mba., & Abdulmajid Addali. (2015). Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter gearbox, Procedia CIRP, Vol. 38, pp. 30 – 36.
Jafar Zarei., Mohammad Amin Tajeddini., & Hamid Reza Karimi. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics. Vol. 24, pp. 151-157.
Jegadeeshwaran, R., & Sugumaran, V. (2013). Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features. Measurement. Vol. 46 (9), pp. 3247 - 3260.
Jegadeeshwaran, R., & Sugumaran, V. (2015). Fuzzy classifier with automatic rule generation for fault diagnosis of hydraulic brake system using statistical features. International Journal of Fuzzy Computation and Modeling. Vol. 1 (3), pp. 333 – 350.
Livani., Hanif., & Yaman Evrenosoglu, C. (2011). A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Transactions on Smart Grid. Vol. 5(1), pp. 51 – 59.
Muralidharan, V., & Sugumaran, V. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing. Vol. 12 (8), pp. 2023 - 2029.
Painuli, S., Elangovan, M., Sugumaran, V. (2014). Tool condition monitoring using K-star algorithm. Expert Systems with Applications. Vol. 41 (6), pp. 2638 - 2643. Pan, X., Hu, X., Zhang, Y., Feng, K., Wang, S., Chen, L., & Cai, Y. (2018). Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection. Genes, 9(4), 208.
Pearson, K. (1895). Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. Vol. 186, pp. 343–414.
Ragini Sidar., Prakash Kumar Sen., & Gopal Sahu. (2015). Review of Vibration Based Fault Diagnosis in Rolling Element Bearing and Vibration Analysis Techniques, International Journal of Scientific Research Engineering & Technology, Vol. 4(10), pp. 998 – 1003.
Ravikumar, S., Ramachandran, K.I., & Sugumaran, V. (2011). Machine learning approach for automated visual inspection of machine components. Expert Systems with Applications. Vol. 38 (4), pp. 3260 - 3266.
Sakthivel N.R., Indira V., Binoy B. Nair, Sugumaran V. (2011) Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump, Int. J. ranular Computing, Rough Sets and Intelligent Systems, Vol. 2(1), pp. 23 – 36.
Sakthivel, N.R., Sugumaran, V., & Babudevasenapati, S. (2010) Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications. Vol. 37(6), pp. 4040 - 4049.
Saravanan, N., Cholairajan, S., & Ramachandran, K.I. (2009). Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique, Expert Systems with Applications, Vol. 36, pp. 3119–3135.
Sohn Hoon., & Charles, R. Farrar. (2011). Damage diagnosis using time series analysis of vibration signals. Smart materials and structures. Vol. 10(3), pp. 446 - 451.
Sugumaran, V., Muralidharan, V., & Ramachandran, K.I. (2007). Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing. Vol. 21(2), pp. 930 - 942.
Ana Palacios., Luciano Sanchez., Ines Couso., & Sebastien Destercke. (2016). An extension of the FURIA classification algorithm to low quality data through fuzzy rankings & its application to the early diagnosis of dyslexia. Neurocomputing. Vol. 176, pp. 60 - 71.
Bostrom, H. (2004) Pruning & exclusion criteria for unordered incremental reduced error pruning. Proceedings of the Workshop on Advances in Rule Learning, ECML. Pp. 17 – 29.
Chapelle., Olivier., Patrick Haffner., & Vladimir, N. Vapnik. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks. Vol. 10(5), pp. 1055 - 1064.
Eineborg, M., & Bostrom, H. (2001). Classifying uncovered examples by rule stretching. International Conference on Inductive Logic Programming. Springer-Verlag. Pp. 41 - 50.
Erdem, Ergin., & Jing Shi. (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy. Vol. 88(4), pp. 1405 - 1414.
Faris Elashaa., Matthew Greaves., David Mba., & Abdulmajid Addali. (2015). Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter gearbox, Procedia CIRP, Vol. 38, pp. 30 – 36.
Jafar Zarei., Mohammad Amin Tajeddini., & Hamid Reza Karimi. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics. Vol. 24, pp. 151-157.
Jegadeeshwaran, R., & Sugumaran, V. (2013). Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features. Measurement. Vol. 46 (9), pp. 3247 - 3260.
Jegadeeshwaran, R., & Sugumaran, V. (2015). Fuzzy classifier with automatic rule generation for fault diagnosis of hydraulic brake system using statistical features. International Journal of Fuzzy Computation and Modeling. Vol. 1 (3), pp. 333 – 350.
Livani., Hanif., & Yaman Evrenosoglu, C. (2011). A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Transactions on Smart Grid. Vol. 5(1), pp. 51 – 59.
Muralidharan, V., & Sugumaran, V. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing. Vol. 12 (8), pp. 2023 - 2029.
Painuli, S., Elangovan, M., Sugumaran, V. (2014). Tool condition monitoring using K-star algorithm. Expert Systems with Applications. Vol. 41 (6), pp. 2638 - 2643. Pan, X., Hu, X., Zhang, Y., Feng, K., Wang, S., Chen, L., & Cai, Y. (2018). Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection. Genes, 9(4), 208.
Pearson, K. (1895). Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. Vol. 186, pp. 343–414.
Ragini Sidar., Prakash Kumar Sen., & Gopal Sahu. (2015). Review of Vibration Based Fault Diagnosis in Rolling Element Bearing and Vibration Analysis Techniques, International Journal of Scientific Research Engineering & Technology, Vol. 4(10), pp. 998 – 1003.
Ravikumar, S., Ramachandran, K.I., & Sugumaran, V. (2011). Machine learning approach for automated visual inspection of machine components. Expert Systems with Applications. Vol. 38 (4), pp. 3260 - 3266.
Sakthivel N.R., Indira V., Binoy B. Nair, Sugumaran V. (2011) Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump, Int. J. ranular Computing, Rough Sets and Intelligent Systems, Vol. 2(1), pp. 23 – 36.
Sakthivel, N.R., Sugumaran, V., & Babudevasenapati, S. (2010) Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications. Vol. 37(6), pp. 4040 - 4049.
Saravanan, N., Cholairajan, S., & Ramachandran, K.I. (2009). Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique, Expert Systems with Applications, Vol. 36, pp. 3119–3135.
Sohn Hoon., & Charles, R. Farrar. (2011). Damage diagnosis using time series analysis of vibration signals. Smart materials and structures. Vol. 10(3), pp. 446 - 451.
Sugumaran, V., Muralidharan, V., & Ramachandran, K.I. (2007). Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing. Vol. 21(2), pp. 930 - 942.
Section
Technical Papers