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Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm

Volume 14, Number 1, January 2018, pp. 1-8
DOI: 10.23940/ijpe.18.01.p1.18

Alamelu Mangai. M., Jegadeeshwaran R., Sugumaran V.

School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai, 600127, India

(Submitted on June 30, 2017; Revised on November 22, 2017; Accepted on November 27, 2017)


Brakes are responsible for the stability of the vehicle. Brake failure is one of the key elements where more attention is required. Normally, a brake system failure is not an instantaneous process. It is caused by faults due to reasons like wear, mechanical fade, and oil leak, which started long before the failure progresses. Hence, it is essential to build a model that can recognize the condition from the signal. Condition monitoring is one such supervision approach, which continuously monitors the system and gives characteristics data. These data can be analysed and the condition of the component can be extracted using a machine learning approach. This study focuses on one such machine learning approach using the vibration characteristics of the brake system. The machine learning approach was carried out using feature extraction and feature classification. The statistical information extracted from the vibration signals under various fault conditions were used as features. The features were classified using machine learning algorithms, namely, Simple logistics, Logit boost and Multinominal Regression. Results were compared and discussed. The Logit boost algorithm, which produced 98.91 % classification accuracy, has been suggested as an effective approach for the brake fault diagnosis study.


References: 18

1. A. Bahri, V. Sugumaran, R. Jegadeeshwaran and S. B. Devasenapathi, “Misfire Detection in Spark-Ignition Engine using Statistical Learning Theory,” International Journal of Performability Engineering, vol. 12, no. 1, pp. 79-88.
2. L. Batista, B. Badri, R. Sabourin and M. Thomas, “A classifier fusion system for bearing fault diagnosis”, Expert Systems with Applications, vol.40, no. 1, pp.6788–6797, 2013.
3. M. Borner, H. Straky, T. Weispfenning and R. Isermann. “Model-based fault detection of vehicle suspension and hydraulic brake systems”, Mechatronics, Vol.12, No. 1, pp. 999–1010, 2002.
4. J. Friedman, T. Hastie and R. Tibshirani, “Additive logistic regression: A statistical view of boosting,” The Annuals of statistics 2000, vol. 28, no.2, pp.337-407, 2000.
5. Y-L He, R. Wang, S. Kwong and X-Z Wang, “Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis,” Information Sciences, vol. 259, no. 1, pp. 252–268, 2014.
6. K. Salahshoor, K. Mojtaba and M. S. Khoshro, “Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers”, Energy, vol.35, pp. 5472-5482, 2010.
7. A. K. Jalan and A. R. Mohanty, “Model based fault diagnosis in rotating machinery,” International Journal of Performability Engineering, vol. 7, no. 6, pp. 515-523, 2011.
8. O. Janssens, R. Schulz, V. Slavkovikj, K. Stockman, M. Loccufier, R. van de Walle and S. Van Hoecke. “Thermal image based fault diagnosis for rotating machinery”, Infrared Physics & Technology, vol. 73, pp. 78–87, 2015.
9. R. Jegadeeshwaran, and V. Sugumaran, "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, no. 9, pp.3247–3260, 2013.
10. R. Jegadeeshwaran, and V. Sugumaran, “Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines”, Mechanical systems and signal processing, vol. 52-53, no. 1, pp. 436-446, 2015.
11. J. Padmavathi, “Logistic regression in feature selection in data mining”, International Journal of Scientific & Engineering Research, vol. 3, no. 8, 2012.
12. H-W Peng and P-J Chiang, “Control of Mechatronics Systems Ball Bearing Fault Diagnosis Using Machine Learning Techniques”, Proceedings of 2011 8th Asian Control Conference (ASCC) Kaohsiung, Taiwan, 2011.
13. N. R. Sakthive, V. Sugumaran, and S. Babudevasenapati, “Vibration based fault diagnosis of monoblock centrifugal pump using decision tree”, Expert Systems with Applications, vol. 37, no. 6, pp. 4040-4049, 2010.
14. V. Sugumaran, and K. I. Ramachandran, “Effect of number of features on classification of roller bearing faults using SVM and PSVM”, Expert Systems with Applications, vol. 38, no. 4, pp. 4088-4096, 2011.
15. M. Sumner, E. Frank, M. Hall, “Speeding up Logistic Model Tree Induction,” In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.
16. P. Sun, M. D. Reid and J. Zhou, “AOSO-Logit Boost: Adaptive One-Vs-One Logit Boost for Multi-Class Problem,” 2012.
17. C-C Wang, C-W Lee and C-S Ouyang. “A Machine-Learning-Based Fault Diagnosis Approach for Intelligent Condition Monitoring”, Ninth International Conference on Machine Learning and Cybernetics, Qingdao, 2014.
18. K. Watanabe and T. Kurita, “Locality Preserving Multi-nominal Logistic Regression,” 2008.


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