Brake Health Prediction Using LogitBoost Classifier Through Vibration Signals A Machine Learning Framework
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Published
Oct 29, 2021
Harish
Anusha
R. Jegadeeshwaran
G Sakthivel
Abstract
Brake is one of the crucial elements in automobiles. If there is any malfunction in the brake system, it will adversely affect the entire system. This leads to tribulation on vehicle and passenger safety. Therefore the brake system has a major role to do in automobiles and hence it is necessary to monitor its functioning. In recent trends, vibration-based condition monitoring techniques are preferred for most condition monitoring systems. In the present study, the performance of various fault diagnosis models is tested for observing brake health. A real vehicle brake system was used for the experiments. A piezoelectric accelerometer is used to obtain the signals of vibration under various faulty cases of the brake system as well as good condition. Statistical parameters were extracted from the vibration signals and the suitable features are identified using the effect of the study of the combined features. Various versions of machine learning models are used for the feature classification study. The classification accuracy of such algorithms has been reported and discussed.
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Keywords
Brake condition monitoring, vibration signals, statistical features, machine learning, confusion matrix, Logitboost
References
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Saravanan Natarajan. (2017). Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis. International Journal of Systems, Control and Communication. vol. 8, no. 1, pp. 57–71.
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, vol. 22, no. 9, pp. 1567-1581.
Tehrany, M. S., Jones, S., Shabani, F., Martínez-Álvarez, F. & Dieu Tien Bui. (2019). A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theoretical and Applied Climatology, vol. 137, pp. 637–653.
Thaiparnit, S., Kritsanasung, S., & Chumuang, N. (2019). A Classification for Patients with Heart Disease Based on Hoeffding Tree. 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), (pp. 352-357), May, Pataya, Thailand.
Tripathy, P., Rautaray, S. S., & Pandey, M. (2017). Role of parallel support vector machine and map-reduce in risk analysis. 2017 International Conference on Computer Communication and Informatics (ICCCI), (pp. 1-3), January, Coimbatore, India.
Alamelu Manghai, T.M., & Jegadeeshwaran, R. (2019). Vibration based brake health monitoring using wavelet features: A machine learning approach. Journal of Vibration and control, vol. 25, no. 18, pp. 2534-2550.
Alamelu Mangai, 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, no. 1, pp. 1-8.
Alamelu Manghai, T. M. & Jegadeeshwaran, R. (2019). Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach. International Journal of Prognostics and Health Management, vol. 10, no. 1, pp. 1-9.
Albert Podusenko, Vsevolod Nikulin, Ivan Tanev & Katsunori Shimohara. (2017). Comparative Analysis of Classififier for Classifification of Emergency Braking of Road Motor Vehicles, Algorithms, vol.10, pp.129.
Aravinth, S. & Sugumaran, V. (2017).
Air compressor fault diagnosis through statistical feature extraction and random forest classifier. Progress in Industrial Ecology – An International Journal, vol. 12, no. 1/2, pp. 192-205.
Chen Lv, Yang Xing, Chao Lu, Yahui Liu, Hongyan Guo, Hongbo Gao & Dongpu Cao. (2018). Hybrid-Learning-Based Classification and Quantitative Inference of Driver Braking Intensity of an Electrified Vehicle, IEEE Transactions on vehicular technology. pp.99.
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, no. 1, pp. 14 – 23.
Jegadeeshwaran R. & Sugumaran, V. 2015. Health monitoring of a hydraulic brake system using nested dichotomy classifier–A machine learning approach. International Journal of Prognostics and Health Management, vol. 6, no. 1, pp. 1-10.
Jianhao Zhou , Jing Sun, Longqiang He, Yi Ding, Hanzhang Cao & Wanzhong Zhao. (2019).Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach-Energies 12,Vol.13,pp.2483.
Jie Liu, Yan-Fu Li & Enrico Zio. (2016). A SVM framework for fault detection of the braking system in a high speed train. Mechanical Systems and Signal Processing, vol.87, pp. 401-409.
Kim, K., Seo, M., Kang, H., Cho, S., Kim, H., Seo, K. S. (2015). Application of LogitBoost Classifier for Traceability Using SNP Chip Data. PLOS ONE, vol. 10, no. 10, e0139685.
Kumar, N., Sakthivel, G., Jegadeeshwaran, R., Sivakumar, R. & Kumar, S. (2019, December). Vibration based IC engine fault diagnosis using tree family classifiers-a machine learning approach. In 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS) (pp. 225-228), December, Rourkela, India.
Lira, M. M. S., de Aquino, R. R. B., Ferreira, A. A., Carvalho, M. A., Neto, O. N. & Santos, G. S. M. (2007). Combining Multipl Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification, International Joint Conference on Neural Networks, (pp. 2863-2868), August, Orlando, USA.
Mariela Cerrada, Grover Zurita, Diego Cabrera, René Vinicio Sánchez, Mariano Artés & Chuan Li. (2015). Fault diagnosis in spur gears based on genetic algorithm and random forest, Mechanical Systems and Signal Processing, vol. 70-71, pp. 87-103.
Merten Tiedemann, David Spieler, Daniel Schoepflflin, Norbert Hoffmann & Sebastian Oberst. (2021). Deep learning for brake squeal: Brake noise detection, characterization and prediction. Mechanical Systems and Signal Processing, vol. 149, pp.107 181
Niranjan A., Haripriya D. K., Pooja R., Sarah S., Deepa Shenoy P. & Venugopal K.R. (2019). EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing. Advances in Intelligent Systems and Computing, vol 713. Springer, Singapore.
Patange A. D. & Jegadeeshwaran, R. (2020). Application of bayesian family classifiers for cutting tool inserts health monitoring on CNC milling. International Journal of Prognostics and Health Management, vol. 11, no. 2, pp. 1-13.
Patrice Pajusco, Nadine Malhouroux-Gaffet & Ghaïs El Zein. (2015). Comprehensive Characterization of the Double Directional UWB Residential Indoor Channel. IEEE Transactions on Antennas and Propagation, vol. 63, pp.1129-1139.
Rahul Kumar Sharma & Sugumaran, V. (2015). A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal. International Journal of Decision Support Systems, vol. 1, no. 1, pp. 115-129.
Rong-Heng Lin, Zi-Xiang Pei, Ze-Zhou Ye, Cheng-Cheng Guo & Bu-Dan Wu. (2020). Hydrogen fuel cell diagnostics using random forest and enhanced feature selection. International Journal of Hydrogen Energy, vol. 45, no. 17, pp. 10523-10535.
Said, Badar & Puspa Dewi, Nindian. (2019). Implementation of Naïve Bayes updateable with modified absolute discount smoothing on Pamekasan Regent SMS center data classification. Journal of Physics: Conference Series. Vol. 1375, 012029.
Saptono, R., Sulistyo, M. E., & Trihabsari, N. S. (2016). Text Classification Using Naive Bayes Updateable Algorithm In SBMPTN Test Questions. Telematika: Jurnal Informatika dan Teknologi Informasi, vol. 13, no. 2, pp. 123-133.
Saravanan Natarajan. (2017). Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis. International Journal of Systems, Control and Communication. vol. 8, no. 1, pp. 57–71.
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, vol. 22, no. 9, pp. 1567-1581.
Tehrany, M. S., Jones, S., Shabani, F., Martínez-Álvarez, F. & Dieu Tien Bui. (2019). A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theoretical and Applied Climatology, vol. 137, pp. 637–653.
Thaiparnit, S., Kritsanasung, S., & Chumuang, N. (2019). A Classification for Patients with Heart Disease Based on Hoeffding Tree. 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), (pp. 352-357), May, Pataya, Thailand.
Tripathy, P., Rautaray, S. S., & Pandey, M. (2017). Role of parallel support vector machine and map-reduce in risk analysis. 2017 International Conference on Computer Communication and Informatics (ICCCI), (pp. 1-3), January, Coimbatore, India.
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Technical Papers