Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers
Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Author : Maamar Ali Saud AL Tobi, Ramachandran. KP., Saleh AL-Araimi, Rene Pacturan, Amuthakannan Rajakannu, Chetha Achuthan
|DOI : 10.14445/22315381/IJETT-V70I12P204|
How to Cite?
Maamar Ali Saud AL Tobi, Ramachandran. KP., Saleh AL-Araimi, Rene Pacturan, Amuthakannan Rajakannu, Chetha Achuthan, " Machinery Faults Diagnosis using Support Vector Machine (SVM) and Naïve Bayes classifiers," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 26-34, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P204
This work aims to investigate some machinery conditions viz. (healthy, imbalance, misalignment, gear fault, inner bearing fault, outer bearing fault, and ball bearing fault). The machinery conditions are simulated based on real-time vibration data acquired from a Machinery Fault Simulator (MFS). There are three main stages for the diagnosis process, which are the data acquisition, pre-processing (feature extraction), and the classification of the condition based on Artificial Intelligence (AI) classifiers, where the Continuous Wavelet Transform (CWT) method is applied to pre-process the obtained datasets (signals), and extract the features based on five statistical parameters namely: RMS, Kurtosis, Peak, Impulse Factor and Shape Factor. Two classifiers based on Artificial Intelligence (AI) are applied and compared to classify the machinery conditions; namely, Support Vector Machine (SVM) using three different kernels, namely; Radial Basis Function (RBC), Linear and Polynomial, and Naïve Bayes classifiers, and the best number of feature inputs and the best value of some kernel parameters are investigated and identified. The classification accuracy and rate in SVM are evaluated through different evaluation metrics, and the results are compared with the classification rates from Naïve Bayes, where SVM has shown better performance in terms of classification. This work emphasized the novelty by integrating feature extraction methods like CWT with the two different AI classifiers and using different performance evaluation parameters.
Machinery Fault Simulator (MFS), Continuous Wavelet Transform (CWT), Support Vector Machine (SVM), Naïve Bayes and Feature Extraction.
 Xin Zhang et al., “Dynamic Modeling and Analysis of Rolling Bearing with Compound Fault on Raceway and Rolling Element,” Shock and Vibration, vol. 2020, p.16, 2020. Crossref, https://doi.org/10.1155/2020/8861899
 Khalaf Salloum Gaeid, and Hew Wooi Ping, “Wavelet Fault Diagnosis and Tolerant of Induction Motor: A Review,” International Journal of the Physical Sciences, vol. 6, no. 3, pp. 358–376, 2011.
 Poulose, J, Prasad SR, V, and Sadique, A, “Fault Diagnosis of Ball Bearings Using Machine Learning of Vibration Signals,” SAE International, 2021. Crossref, https://doi.org/ 10.4271 /2021-28-0178
 Omar AlShorman et al., "A Review of Condition Monitoring and Fault Diagnosis and Detection of Rotating Machinery Based on Image Aspects," International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1-5, 2020. Crossref, https://doi.org/10.1109/ICDABI51230.2020.9325635
 Juhani Nissilä, and Jouni Laurila, “Diagnosing Simultaneous Faults Using the Local Regularity of Vibration Signals,” Measurement Science and Technology, vol. 30, no. 4, 2019. Crossref, https://doi.org/10.1088/1361-6501/aaf8fa
 Aalim M. Mustafa, and Jamal F. Nayfeh, "Experimental Research on Machinery Fault Simulator (MFS): A Review," Prognostics and Health Management Conference (PHM-Besançon), pp. 72-78, 2020. Crossref, https://doi.org/10.1109/PHM-Besancon49106.2020.00019
 Naqash Azeem et al., “Experimental Condition Monitoring for the Detection of Misaligned and Cracked Shafts by Order Analysis,” Advances in Mechanical Engineering. vol. 11, no. 5, pp. 1–11, 2019. Crossref, https://doi.org/10.1177/1687814019851307
 Abbas Rohani Bastami and Sima Vahid “A Comprehensive Evaluation of the Effect of Defect Size in Rolling Element Bearings on the Statistical Features of the Vibration Signal,” Mechanical Systems and Signal Processing, vol. 151, p. 107334, 2021. Crossref, https://doi.org/10.1016/j.ymssp.2020.107334
 Wu Deng et al., “A Novel Intelligent Diagnosis Method Using Optimal LS-SVM with Improved PSO Algorithm,” Soft Computing, vol. 23, no. 7, pp. 2445–2462, 2019. Crossref, https://doi.org/10.1007/s00500-017-2940-9
 Ashraf AlShalalfeh, and Laith Shalalfeh “Bearing Fault Diagnosis Approach Under Data Quality Issues,” Applied Sciences, vol. 11, no. 7, p. 3289, 2021. Crossref, https://doi.org/10.3390/app11073289
 Omar AlShorman et al., “A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor,” Shock and Vibration, vol. 2020, pp. 1-20, 2020. Crossref, https://doi.org/10.1155/2020/8843759
 Maamar Ali SaudALTobi et al., “Fault Diagnosis of a Centrifugal Pump Using MLP-GABP and SVM with CWT,” Engineering Science and Technology, An International Journal, vol. 22, no. 3, pp. 854-861, 2019. Crossref, https://doi.org/10.1016/j.jestch.2019.01.005
 Maamar Al Tobi et al., “Faults Diagnosis of a Centrifugal Pump Using Multilayer Perceptron Genetic Algorithm Back Propagation and Support Vector Machine with Discrete Wavelet Transform‐Based Feature Extraction,” Computational Intelligence, vol. 37, no. 1, pp.21- 46, 2021a. Crossref, https://doi.org/10.1111/coin.12390
 Maamar Al Tobi et al., “Using MLP‐GABP and SVM with Wavelet Packet Transform‐Based Feature Extraction for Fault Diagnosis of A Centrifugal Pump,” Energy Science & Engineering, vol. 10, pp. 1826-1839, 2022. Crossref, https://doi.org/10.1002/ese3.933
 RuiZhao et al., “Deep Learning and its Applications to Machine Health Monitoring,” Mechanical Systems and Signal Processing, vol. 115, pp. 213-237, 2019. Crossref, https://doi.org/10.1016/j.ymssp.2018.05.050
 B. Balaji et al., "Fault Prediction of Induction Motor Using Machine Learning Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 8, no. 11, pp. 1-6, 2021. Crossref, https://doi.org/10.14445/23488379/IJEEE-V8I11P101
 Zhuyun Chen et al., “Gearbox Fault Diagnosis Using Convolutional Neural Networks and Support Vector Machines,” 27th European Signal Processing Conference (EUSIPCO), 2019. Crossref, https://doi.org/10.23919/EUSIPCO.2019.8902686
 R. Surendran, Osamah Ibrahim Khalaf, and Carlos Andres Tavera Romero, “Deep Learning Based Intelligent Industrial Fault Diagnosis Model,” Computers, Materials and Continua, vol. 70, no. 3, pp. 6323-6338.
 Ignacio Martin-Diaz et al., “An Experimental Comparative Evaluation of Machine Learning Techniques for Motor Fault Diagnosis Under Various Operating Conditions,” IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 2215–2224, 2018. Crossref, https://doi.org/10.1109/TIA.2018.2801863
 WeiGuo et al., “Multi-Frequency Weak Signal Detection Based on Multi-Segment Cascaded Stochastic Resonance for Rolling Bearings,” Microelectronics Reliability, vol. 75, pp. 239-252, 2017. Crossref, https://doi.org/10.1016/j.microrel.2017.03.018
 Geetha Dhandapani, and Veilumuthu, R, “Dynamic Analysis of Vibration Signals and Adaptive Measures for Effective Condition Monitoring of Electrical Machines,” International Journal of COMADEM, vol. 21, no. 2, pp. 29–38, 2018.
 Sharma,V, “A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions,” Journal of the Institution of Engineers (India): Series C, vol. 102, no. 2, pp. 507–523, 2021. Crossref, https://doi.org/10.1007/s40032-021-00666-y
 Prashant H. Jain, and Dr. Santosh P. Bhosle, "A Review on Vibration Signal Analysis Techniques Used for Detection of Rolling Element Bearing Defects," SSRG International Journal of Mechanical Engineering, vol. 8, no. 1, pp. 14-29, 2021. Crossref, https://doi.org/10.14445/23488360/IJME-V8I1P103
 Yerui Fan et al., “Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm,” Shock and Vibration, vol. 2020, pp. 1-11, Crossref, https://doi.org/10.1155/2020/9096852.
 Sun Shuang et al., “Fast Bearing Fault Diagnosis of Rolling Element Using Lévy Moth-Flame Optimization Algorithm and Naive Bayes,” Eksploatacja I Niezawodnosc – Maintenance and Reliability, vol. 22, no. 4, pp. 730–740, 2020. Crossref, http://dx.doi.org/10.17531/ein.2020.4.17
 Nannan Zhang et al., “Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data,” Sensors, vol. 18, no. 2, p. 463, 2018. Crossref, https://doi.org/10.3390/s18020463
 Khalid F Al-Raheem and Waleed Abdul-Karem, “Rolling Bearing Fault Diagnostics Using Artificial Neural Networks Based on Laplace Wavelet Analysis,” International Journal of Engineering,Science and Technology, vol. 2, no. 6, pp. 278-290, 2010. Crossref, https://doi.org/10.4314/ijest.v2i6.63730
 Corinna Cortes, and Vladimir Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, pp. 273-297, 1995. Crossref, https://doi.org/10.1007/BF00994018
 Yajuan Liu, and Tao Liu “Rotating Machinery Fault Diagnosis Based on Support Vector Machine,” International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lumpur, IEEE, 2010. Crossref, https://doi.org/10.1109/ICICCI.2010.64
 Jianhua Zhong, Zhixin Yang, and S. F. Wong., “Machine Condition Monitoring and Fault Diagnosis Based on Support Vector Machine,” International Conference of Industrial Engineering Management, IEEE, 2010. Crossref, https://doi.org/10.1109/IEEM.2010.5674594
 Scholkopf B, “SVMs- a Practical Consequence of Learning Theory,” IEEE Intelligent Systems, vol. 13, no. 4, pp. 18-19, 1998. Crossref, http://dx.doi.org/10.1109/5254.708428
 Samanta B, “Gear Fault Detection Using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms,” Mechanical Systems and Signal Processing, vol. 18, no. 3, pp. 625-644, 2004. Crossref, https://doi.org/10.1016/S0888-3270(03)00020-7
 Berrar, D, “Bayes’ Theorem and Naive Bayes Classifierm” Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, pp. 403-406, 2018. Crossref, http://dx.doi.org/10.1016/B978-0-12-809633-8.20473-19
 Zheng H., Li Z., and Chen X, “Gear Fault Diagnosis Based on Continuous Wavelet Transform,” Mechanical Systems and Signal Processing, vol. 16, no. (2-3), pp. 447-457, 2002. Crossref, https://doi.org/10.1006/mssp.2002.1482
 Saravanan N, and Ramachandran K. I, “Incipient Gear Box Fault Diagnosis Using Discrete Wavelet Transform (DWT) for Feature Extraction and Classification Using Artificial Neural Network (ANN),” Expert Systems with Applications, vol. 37, no. 6, pp. 4168-4181, 2010. Crossref, https://doi.org/10.1016/j.eswa.2009.11.006
 Maamar Ali Saud Al Tobi et al., "Machinery Fault Diagnosis Using Continuous Wavelet Transform and Artificial Intelligence Based Classification," Proceedings of the 2022 3rd International Conference on Robotics Systems and Vehicle Technology, pp. 51-59, 2022. Crossref, https://doi.org/10.1145/3560453.3560463
 Altaf, M et al., “Automatic and Efficient Fault Detection in Rotating Machinery Using Sound Signals,” Acoustics Australia, vol. 47, no. 2, pp.125-139, 2019. Crossref, https://doi.org/10.1007/s40857-019-00153-6