This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A Machine Learning Approach for Vibration Signal Based Fault Classification on Hydraulic Braking System through C4.5 Decision Tree Classifier and Logistic Model Tree Classifier
Technical Paper
2020-28-0496
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Car hydraulic brakes are important safety components for passengers and are thus the good condition of brakes are essential for braking. By using the vibrational signatures, the state of the brake components can be determined. In this proposed study, electronic condition monitoring is suggested as a possible solution to such issues by using a machine learning method with a piezo-electric transducer and a dynamic data acquisition system. Ford EcoSport setup was used to acquire the vibration signals for both good and bad braking conditions. The mathematical Descriptive statistical features from the vibration signals were obtained and the feature selection has been done with the C4.5 decision tree classifier. The appropriate number of features needed to classify a particular problem is not determined by a specific method. A thorough study is, therefore, necessary to find the right number of features. The fault analysis of the Ford EcoSport hydraulic braking system has been established through the use of the C4.5 decision tree classifier and logistic model tree (LMT) classifier.
Authors
Topic
Citation
A, J., Anaimuthu, S., Selvaraju, N., Muthiya, S. et al., "A Machine Learning Approach for Vibration Signal Based Fault Classification on Hydraulic Braking System through C4.5 Decision Tree Classifier and Logistic Model Tree Classifier," SAE Technical Paper 2020-28-0496, 2020.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 |
Also In
References
- Fang , C. , Zhang , X. , Cheng , Y. , Wang , S. et al. Fault Diagnosis for Brake System in High-speed Trains Using the Phased Features and Multi-layer Perceptron IOP Conference Series: Materials Science and Engineering 470 1 012007 2019
- Jung , J.-C. and Yong-Gee , C.H.O. 2019
- Kumar , D. and Horst , F. 2019
- Xu , G. , Song , D. , Zhang , D. , Zhang , X. et al. A Novel Mechanical Design of Disc Brakes for Fault Diagnosis and Monitoring Positive Braking Pressure in Mine Hoist Advances in Mechanical Engineering 11 4 1687814019842494 2019
- Leung , J. , Pontrello , B. , Hirst , R. , Cameron , L. et al. 2019
- Houtman , A.J. , Pennala , B.C. , Kilmurray , P.A. , and Krueger , E.E. 2019
- Rashidi , B. , Krishnaswamy , M.S. , and Zhao , Q. 2019
- Jin , Y. , Liu , X.X. , and Liu , W.P. Design of Hydraulic Fault Diagnosis System Based on LabVIEW Advanced Materials Research 457 257 260 2012
- Entezami , M. , Hillmansen , S. , Weston , P. , and Papaelias , M.P. Fault Detection and Diagnosis within a Wind Turbine Mechanical Braking System Using Condition Monitoring Renewable Energy 47 175 182 2012
- Zhou , D. , Ji , H. , He , X. , and Shang , J. Fault Detection and Isolation of the Brake Cylinder System for Electric Multiple Units IEEE Transactions on Control Systems Technology 26 5 1744 1757 2017
- Liu , J. , Li , Y.-F. , and Zio , E. A SVM Framework for Fault Detection of the Braking System in a High Speed Train Mechanical Systems and Signal Processing 87 401 409 2017
- Yang , Y. , Zhu , C. , Li , S. , Zhang , S. , et al. Robust Fault Diagnosis for DK-2 Brake Based on Both Data-Driven and Model 2018 37th Chinese Control Conference (CCC) 6055 6060 2018
- Zhang , M. , Liu , Z. , and Dang , X. Fault Diagnosis on Train Brake System Based on Multi-Dimensional Feature Fusion and GBDT Enhanced Classification 2018 International Conference on Intelligent Rail Transportation (ICIRT) 1 5 2018
- Glowacz , A. Fault Diagnosis of Single-Phase Induction Motor Based on Acoustic Signals Mechanical Systems and Signal Processing 117 65 80 2019
- Wang , D. , Zhao , X. , Kou , L.-L. , Qin , Y. et al. A Simple and Fast Guideline for Generating Enhanced/Squared Envelope Spectra from Spectral Coherence for Bearing Fault Diagnosis Mechanical Systems and Signal Processing 122 754 768 2019
- Li , X. , Yu , Y. , Pan , H. , Cheng , J. et al. Non-parallel Least Squares Support Matrix Machine for Rolling Bearing Fault Diagnosis Mechanism and Machine Theory 145 103676 2020
- Ng , K.Y. , Frisk , E. , Krysander , M. , and Eriksson , L. A Realistic Simulation Testbed of a Turbocharged SI Engine System: A Platform for the Evaluation of Fault Diagnosis Algorithms & Strategies 2020
- Wang , J. , Guifu , D. , Zhu , Z. , Shen , C. et al. Fault Diagnosis of Rotating Machines Based on the EMD Manifold Mechanical Systems and Signal Processing 135 106443 2020
- Kaplan , K. , Kaya , Y. , Kuncan , M. , Mehmet , R.M. et al. An Improved Feature Extraction Method Using Texture Analysis with LBP for Bearing Fault Diagnosis Applied Soft Computing 87 106019 2020
- Jegadeeshwaran , R. , and Sugumaran , V. Fault Diagnosis of Automobile Hydraulic Brake System Using Statistical Features and Support Vector Machines Mechanical Systems and Signal Processing 52 436 446 2015
- Joshuva , A. , and Sugumaran , V. A Data Driven Approach for Condition Monitoring of Wind Turbine Blade Using Vibration Signals through Best-First Tree Algorithm and Functional Trees Algorithm: A Comparative Study ISA transactions 67 160 172 2017
- Pan , W. An Improved Feature Selection Algorithm for Fault Level Identification Recent Trends in Intelligent Computing, Communication and Devices Singapore Springer 2020 87 94
- Joshuva , A. , and Sugumaran , V. A Lazy Learning Approach for Condition Monitoring of Wind Turbine Blade Using Vibration Signals and Histogram Features Measurement 107295 2019
- Chen , W. , Zhao , X. , Shahabi , H. , Shirzadi , A. et al. Spatial Prediction of Landslide Susceptibility by Combining Evidential Belief Function, Logistic Regression and Logistic Model Tree Geocarto International 34 11 1177 1201 2019
- Pham , B.T. , Phong , T. , Nguyen , H.D. , Qi , C. et al. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping Water 12 1 239 2020
- Jung , K. , Bae , D.-H. , Um , M.-J. , Kim , S. et al. Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation Sustainability 12 1 400 2020