A smart fault identification system for ball bearing using simulation-driven vibration analysis

• Autorzy: Khaire Pallavi , Phalle Vikas

Identyfikatory

DOI

10.24425/ame.2023.145583

• Języki publikacji: EN

Abstrakty

EN

Bearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s health for avoiding the risk of failure. In this work, a simulation-based approach is adopted to identify surface defects at ball bearing raceways. The vibration data in time and frequency domain is captured by FFT analyzer from an experimental setup. The time frequency domain conversion of a raw time domain data was carried out by wavelet packet transform, as it takes into account the transients and spectral frequencies. The rotor bearing model is simulated in Ansys. Finally, most influencing statistical features were extracted by employing Principal Component Analysis (PCA), and fed to Multiclass Support Vector Machine (MSVM). To train the algorithm, the simulated data is used whereas the data acquired from FFT analyzer is used for testing. It can be concluded that the defects are characterized by Ball Pass Frequency (BPF) at inner race and outer raceway as indicated in the literature. The developed model is capable to monitor bearing’s health which gives an average accuracy of 99%.

Słowa kluczowe

EN

condition monitoring; bearing defect; FFT analyzer; BPFI; BPFO; multiclass support; vector machine

PL

monitorowanie stanu; wada łożyska; analizator FFT; BPFI; BPFO; maszyna wektorowa

• Wydawca: Komitet Budowy Maszyn PAN
• Czasopismo: Archive of Mechanical Engineering
• Rocznik: 2023
• Tom: LXX, nr 2
• Strony: 247--270
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Khaire Pallavi

• Veermata Jijabai Technological Institute, Mumbai, India.
• Fr. C. Rodrigues Institute of Technology, Navi Mumbai, India

• https://orcid.org/0000-0001-8485-7439

autor

Phalle Vikas

• Veermata Jijabai Technological Institute, Mumbai, Indi

Bibliografia

• [1] Z. Taha and N.T. Dung. Rolling element bearing fault detection with a single point defect on the outer raceway using finite element analysis. The 11th Asia Pacific Industrial Engineering and Management Systems Conference and the 14th Asia Pacific Regional Meeting of International Foundation for Production Research, Melaka, Malaysia, 7-10 Dec. 2010.
• [2] P. Jayaswal, S.N. Verma, and A.K. Wadhwani. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. Journal of Vibration and Control, 17(8):1131–1148, 2011. doi: 10.1177/1077546310361858.
• [3] V.V. Rao and Ch. Ratnam. A comparative experimental study on identification of defect severity in rolling element bearings using acoustic emission and vibration analysis. Tribology in Industry, 37(2):176–185, 2015.
• [4] S. Shah and A. Guha. Bearing health monitoring. Tribology in Industry, 38(3):297–307, 2016.
• [5] C. Ratnam, N.M. Jasmin, V.V. Rao, and K.V. Rao. A comparative experimental study on fault diagnosis of rolling element bearing using acoustic emission and soft computing techniques. Tribology in Industry, 40(3):501–513, 2018. doi: 10.24874/ti.2018.40.03.15.
• [6] K. Kappaganthu and C. Nataraj. Modelling and analysis of outer race defects in rolling element bearings. Advances in Vibration Engineering, 11(4):371–384, 2012.
• [7] P.K. Kankar, S.C. Sharma, and S.P. Harsha. Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11(2):2300–2312, 2011. doi: 10.1016/j.asoc.2010.08.011.
• [8] A. Sharma, M. Amarnath, and P.K. Kankar. Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1):176–192, 2014. doi: 10.1177/1077546314528021.
• [9] V. Hariharan and P.S.S. Srinivasan. Vibration analysis of parallel misaligned shaft with ball bearing system. Sonklanakarin Journal of Science and Technology, 33(1):61–68, 2011.
• [10] J.D. Wu and C.H. Liu. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Systems with Applications, 36(3):4278–4286, 2009. doi: 10.1016/j.eswa.2008.03.008.
• [11] J.S. Rapur and R.Tiwari. Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses. Measurement, 147:106809, 2019. doi: 10.1016/j.measurement.2019.07.037.
• [12] C. Cortes and V. Vapnik. Support vector network. Machine Learning, 20(3):273–297, 1995. doi: 10.1007/BF00994018.
• [13] S. Damuluri, K. Islam, P. Ahmadi, and N.S. Qureshi. Analyzing navigational data and predicting student grades using support vector machine. Emerging Science Journal, 4(4):243–252, 2020. doi: 10.28991/esj-2020-01227.
• [14] R. Tiwari. Rotor Systems: Analysis and Identification. CRC Press, 2017. doi: 10.1201/9781315230962.
• [15] V.C. Handikherkar and V.M. Phalle. Gear fault detection using machine learning techniques -- A simulation-driven approach. International Journal of Engineering, 34(1):212–223, 2021. doi: 10.5829/IJE.2021.34.01A.24.
• [16] S. Patil and V. Phalle. Fault detection of anti-friction bearing using ensemble machine learning methods. International Journal of Engineering, 31(11):1972–1981, 2018.
• [17] A.S. Minhas, G. Singh, J. Singh, P.K. Kankarand, and S. Singh. A novel method to classify bearing faults by integrating standard deviation to refined composite multi-scale fuzzy entropy. Measurement,154:107441, 2020. doi: 10.1016/j.measurement.2019.107441.
• [18] www.mfpt.org

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).