Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach
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Published
Nov 1, 2020
R. Jegadeeshwaran
V. Sugumaran
Abstract
Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned.
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Keywords
Statistical features, nested dichotomy, class balanced nested dichotomy, data near balanced nested dichotomy, decision tree
References
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Burgess C. J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol. 2, pp. 955–974.
Dong L. L, Frank E & Kramer S. (2005) Ensembles of Balanced Nested Dichotomies for Multi-Class Problems, Knowledge Discovery in Databases: PKDD 2005, 2005, pp. 84-95.
Frank, E. & Kramer, S., Ensembles of nested dichotomies for multi-class problems. In: Proc. Int. Conf. on Machine Learning. ACM, 2004, pp.305 – 312.
Huang C. H., (2009). Feature selection for text classification with Naïve Bayes, Expert Systems with Applications, vol. 36, pp. 5432–5435.
Jack L. B. & Nandi A. K. (2000). Comparison of neural networks and support vector machines in condition monitoring application. In Proceedings of COMADEM 2000, Houston, TX, USA, pp. 721–730.
Jegadeeshwaran R. & Sugumaran V., (2013). Method and Apparatus for Fault Diagnosis of Automobile Brake System Using Vibration Signals”, Recent Patents on Signal Processing, 3, 2013, pp. 2-11.
Jegadeeshwaran R. & Sugumaran V., (2015). Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) - A statistical learning approach, Engineering Science and Technology, an International Journal, Vol. 18, pp. 14-23.
Jegadeeshwaran R. & Sugumaran v., (2015). Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines, Mechanical Systems and Signal Processing, Vol. 52-53, pp. 436–446.
Miller R. R., Marshall R. J., David Aexander Baiey & Nicholas Charles Griffin, (2004). Brake condition monitoring, US0011596 (A1).
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. Journal of Applied Soft Computing. , vol. pp. 1-7.
Nowicki, R., Slowinski, R., & Stefanowski, J. (1992). Evaluation of vibro-acoustic diagnostic symptoms by means of the Rough Sets Theory. in Computers in Industry, vol. 20, pp. 141–152.
Rajakarunakaran S., Venkumar P., Devaraj D., & Surya Prakasa Rao K., (2008). Artificial neural network approach for fault detection in rotary system, Applied Soft Computing, vol. 8, pp. 740–748.
Reinecke, E., (1988). Apparatus for the measurement and / or of a braking torque, US4790606.
Rodríguez, J. J., César García-Osorio & Jesús Maudes, Forests of nested dichotomies, Pattern Recognition Letters, 31, 2010, pp. 125–132.
Sakthivel N.R., Indira V., Nair B.B., & Sugumaran V. (2011). Use of histogram features for decision tree based fault diagnosis of monoblock centrifugal pump. International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), vol. 2, pp. 23–36.
Sakthivel N.R., Sugumaran V., & Babudevasenapati S. (2010). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. International Journal of Expert Systems with Application, vol. 2, pp. 38–61.
Sakthivel N.R., Sugumaran V., & Nair B. B. (2010). Comparison of Decision Tree-Fuzzy and Rough set-Fuzzy Methods for Fault Categorization of Mono-block Centrifugal Pump, Mechanical Systems and Signal Processing, vol. 24, pp. 1887–1906.
Samanta B., Al-balushi K.R., & Al-araim S.A. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, Vol. 16, pp. 657–665.
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.
Shen Yin, Steven X. Ding, Adel Haghani, Haiyang Hao, & Ping Zhang. (2012). A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark - Tennessee Eastman process, Journal of Process Control, Volume 22, Issue 9, October 2012, Pages 1567-1581.
Soman, K. P., & Ramachandran, K. I. (2005). Insight into wavelets from theory to practice. Prentice-Hall of India Private Limited, New Delhi, India.
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, pp. 930–942.
Sugumaran V., & Ramachandran K.I. (2007). Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mechanical System and Signal Processing, vol. 21, pp. 2237-2247.
Sugumaran V., & Ramachandran K. I. (2011). Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications. Vol. 38, pp. 4088–4096.
Suykens, J. A. K., Van Gestel T., Vandewalle J., & De Moor B. (2003). A support vector machine formulation to PCA analysis and its Kernel version, ESAT-SCD-SISTA Technical Report.
Section
Technical Papers