A Comparison of Machine Learning Methods for the Diagnosis of Motor Faults Using Automated Spectral Feature Extraction Technique
Tóm tắt
Centrifugal pumps (CPs) are mainly composed of impeller and bearings. The operation of the CPs is disturbed if any of its components is faulty. Bearings faults are reported to be the major reason for pump failures. Condition monitoring of the CPs helps in early diagnosis and assists to keep machines in working condition with minimum maintenance costs. Famous non-intrusive techniques known as motor current analysis (MCA) have been reported in the literature for the detection of bearing anomalies. However, limited literature is available to diagnose the minor scratches in the bearing surface. Recent research on the diagnosis of bearing scratches identification through MCA has shown some promising results. The comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% was reported which is very low. The low amplitudes of the bearing scratch in the MCA spectrum, environment noise and utilization of conventional feature extraction techniques were the key reasons for the low accuracy. This problem has been tackled in this paper by developing an automated frequency features extraction algorithm (ASFEA) to extract useful feature from the integrated current and voltage sensors data. ASFEA operates based on the feature location identification in the spectrum, feature extraction, measuring the amplitude of the fault component and comparing it with the statistical threshold. The experimental data has been collected and the performance of the ASFEA has been tested on several machine learning techniques and the better classification accuracy of ASFEA (SVM 96.87%, k-NN 100%, NBC 96%, Gbdt 96.87%, CNN 100%) has been achieved as compared to previously reported methods.
Tài liệu tham khảo
Gülich, J.F.: Centrifugal Pumps. Springer, Berlin (2010)
Jiang, Q., Heng, Y., Liu, X., Zhang, W., Bois, G., Si, Q.: A review of design considerations of centrifugal pump capability for handling inlet gas-liquid two-phase flows. Energies 12, 1078 (2019). https://doi.org/10.3390/en12061078
Stel, H., Ofuchi, E.M., Sabino, R.H.G., Ancajima, F.C., Bertoldi, D., Marcelino Neto, M.A., Morales, R.E.M.: Investigation of the motion of bubbles in a centrifugal pump impeller. J. Fluids Eng. 141, 1031203 (2018)
Irfan, M., Alwadie, A., Glowacz, A.: Design of a novel electric diagnostic technique for fault analysis of centrifugal pumps. Appl. Sci. MDPI 9(23), 89 (2019)
Si, Q., Cui, Q., Zhang, K., Yuan, J., Bois, G.: Investigation on centrifugal pump performance degradation under air-water inlet two-phase flow conditions. La Houille Blanche 3, 41–48 (2018)
Dalvand, F., Kang, M.: Detection of generalized-roughness and single point bearing fault using linear prediction-based current noise cancellation. IEEE Trans. Ind. Electron. 65(12), 9728–9738 (2018)
Omar, A., Fahad, A., Mahmoud, M., Irfan, M., Adam, G., Faisal, A., Jaroslaw, K.: Glowacz Witold “Sound and acoustic emission-based early condition monitoring and fault diagnosis of induction motor: a review study.” Adv. Mech. Eng. 13, 128 (2021)
Glowacz, A., Glowacz, W., Kozik, J., Irfan, M., Khan, Z.F.: Detection of deterioration of three-phase induction motor using vibration signals. Measure. Sci. Rev. 19, 241–249 (2019)
Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., Cesarotti, V.: Maintenance transformation through Industry 40 technologies: a systematic literature review. Comput. Indust. 23, 103335 (2020). https://doi.org/10.1016/j.compind.2020.103335
Tortorella, G.L., Fogliatto, F.S., Cauchick-Miguel, P.A., Kurnia, S., Jurburg, D.: Integration of industry 40 technologies into total productive maintenance practices. Int. J. Product. Econ. 240, 108224 (2021). https://doi.org/10.1016/j.ijpe.2021.108224
Irfan, M.: Modeling of fault frequencies for distributed damages in bearing raceways. J. Nondestruct. Eval. 38, 1–10 (2019)
Riera-Guasp, M., Antonino-Daviu, J.A., Capolino, G.A.: Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art. IEEE Trans. Ind. Electron. 62(3), 1746–1759 (2015)
Kumar, R., Singh, M.: Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement 46(1), 537–545 (2013)
Kulkarni, S., Bewoor, A.: Vibration based condition assessment of ball bearing with distributed defects. J. Meas. Eng. 4(2), 87–94 (2016)
Kuruppu, S.S., Kulatunga, N.A.: D-Q current signature-based faulted phase localization for SM-PMAC machine drives. IEEE Trans. Industr. Electron. 62(1), 113–121 (2015)
Irfan, M., Saad, N., Alwadie, A.: An automated spectral extraction algorithm for the fault diagnosis of gears. J. Fail Anal. Prevent 19(1), 98–105 (2019)
Esakimuthu, P.S., Mizuno, Y., Nakamura, H.: A comparative study between machine learning algorithm and artificial intelligence neural network in detecting minor bearing fault of induction motors. Energies 12(11), 41 (2019)
Irfan, M., Alwadie, A.S., Glowacz, A., Awais, M., Rahman, S., Khan, M.K.A., Jalalah, M., Alshorman, O., Caesarendra, W.: A Novel feature extraction and fault detection technique for the intelligent fault identification of water pump bearings. Sensors 21, 4225 (2021). https://doi.org/10.3390/s2112422
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: Condition Monitoring and Faults Diagnosis of Induction Motors: Electrical Signature Analysis. CRC Press, Routledge (2018)
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Alwadie, A.S., Sheikh, M.A.: An assessment on the non-invasive methods for condition monitoring of induction motors. Fault Diagn. Detect. 87, 14 (2017)
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: An intelligent diagnostic condition monitoring system for AC motors via instantaneous power analysis. Int. Rev. Electr. Eng. 8(2), 664–672 (2013)
Sheikh, M.A., Nor, N.M., Ibrahim, T., Bakhsh, S.T., Irfan, M., Daud, H.B.: Non-invasive methods for condition monitoring and electrical fault diagnosis of induction motors”. Fault Diagn. Detect. 86, 14 (2017)
Singh, S., Kumar, N.: Detection of bearing faults in mechanical system using stator current monitoring. IEEE Trans. Ind. Inform. 13, 1341–1349 (2017)
Piantsop, M.C., Hameyer, K.: Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection. IEEE Trans. Ind. Appl. 52, 3861–3868 (2016)
Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques part I: fault diagnosis with model based and signal-based approaches. IEEE Trans. Industr. Electron. 62(6), 3757–3767 (2015)
Sawalhi, N., Randall, R.B.: Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniques to track the spall size. Mech. Syst. Signal Process. 25(3), 846–870 (2011)
Dolenc, B., Boškoski, P., Pfajfar, J., Juričić, Đ.: Vibration based diagnosis of distributed bearing faults. In: Vibration Engineering and Technology of Machinery, Proceedings of VETOMAC X, University of Manchester (2014)
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Magzoub, M.: An online fault diagnosis system for induction motors via instantaneous power analysis. Tribol. Trans. 60(4), 592–604 (2017)
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: Condition monitoring of induction motors via instantaneous power analysis. J. Intell. Manuf. 28(6), 1259–1267 (2017)
Hurtado, Z.Y.M., Tello, C.P., Sarduy, J.G.: A review on detection and fault diagnosis in induction machines. Publicaciones en Ciencias y Tecnologa 8(1), 11–30 (2014)
Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Magzoub, M., Hung, N.T.: A Non invasive method for condition monitoring of induction motors operating under arbitrary loading conditions. Arab. J. Sci. Eng. 4, 1–8 (2016). https://doi.org/10.1007/s13369-015-1996-z,2015
Eftekharnejad, B., Charnley, B., Carrasco, M.R.: The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process. 25(1), 266–284 (2011)
Glowacz, A., Glowacz, W., Glowacz, Z., Kozik, J., Gutten, M., Korenciak, D., Khan, Z.F., Irfan, M., Carletti, E.: Fault diagnosis of three phase induction motor using current signal, MSAF-ratio15 and selected classifiers. Arch. Metall. Mater. 62(4), 2413–2419 (2017)
Li, Y., Xu, M., Liang, X., Huang, W.: Application of bandwidth EMD and bdaptive multi-scale morphology analysis for incipient fault diagnosis of rolling bearings. IEEE Trans. Ind. Electron. 64(8), 6506–6517 (2017)
Irfan, M., Saad, N., Ibrahim, R.: Vijanth S Asirvadam and Muawia Magzoub, “An intelligent fault diagnosis of induction motors in an arbitrary noisy environment.” J. Nondestr. Eval. 35(1), 1–13 (2016)
Gunasekaran, S., Esakimuthu Pandarakone, S., Asano, K., Mizuno, Y., Nakamura, H.: Condition monitoring and diagnosis of outer raceway bearing fault using support vector machine. In Proceedings of the International Conference on Condition Monitoring and Diagnosis (CMD 2018), Perth, 23–26 September 2018; pp. 1–6
Frosini, L., Harlisca, C., Szabo, L.: Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Trans. Ind. Electron. 62(3), 1846–1854 (2015)
AlShorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A.: A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock Vibr. J. 2020.
Faiz, J., Takbash, A.M., Mazaheri-Tehrani, E.: a review of application of signal processing techniques for fault diagnosis of induction motors – part I. AUT J Electric. Eng. (2017). https://doi.org/10.22060/eej.2017.13219.5142
Nandi, S., Toliyat, H.A., Li, X.: Condition monitoring and fault diagnosis of electrical motors: a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)
Faiz, J., Ebrahimi, B.M., Sharifian, M.B.B.: Different faults and their diagnosis techniques in three-phase squirrel cage induction motors: a review. Electromagnetics 26, 543–569 (2006)
Elforjani, M., Shanbr, S.: Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Trans. Ind. Electron. 65, 5864–5871 (2018)
Soualhi, A., Razik, H., Clerc, G., Doan, D.D.: Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system. IEEE Trans. Ind. Electron. 61, 2864–2874 (2014)
Tayyab, S.M., Asghar, E., Pennacchi, P., Chatterton, S.: Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection. Procedia Manuf. 51, 266–273 (2020)
Orrù, P.F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., Arena, S.: Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry. Sustainability 12, 4776 (2020)
Jin, T., Yan, C., Chen, C., Yang, Z., Tian, H., Wang, S.: Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery. Measurement 181, 109639 (2021)
Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal. Process. 108, 33–47 (2018)
Zhuang, Z., Lv, H., Jie, Xu., Huang, Z., Qin, W.: A deep learning method for bearing fault diagnosis through stacked residual dilated convolutions. Appl. Sci. 19(9), 2019 (1823)
Wang, J., Mo, Z., Zhang, H., And, Q.M.: A deep learning method for bearing fault diagnosis based on time-frequency image”. IEEE Access 7, 42373 (2019)
Chen, Z., Mauriciob, A., Li, W., Gryllia, K.: A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mech. Syst. Signal Process 40, 10663 (2020)
He, Z., Shao, H., Zhong, X., Zhao, X.: Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions. Knowl. Based Syst. 207, 106396 (2020)
Bai, R., Quansheng, X., Meng, Z., Cao, L., Xing, K., Fan, F.: Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement 184, 109885 (2021)
Zhao, Bo., Zhang, X., Zhan, Z., Qiqiang, Wu.: Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis. J. Manuf. Syst. 59, 565–576 (2021)
Jiao, J., Zhao, M., Lin, J., Liang, K.: A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing 417, 36–63 (2020)
Rezaeianjouybari, B., Shang, Yi.: Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement 163, 109929 (2020)
Liu, X., Huang, H., Xiang, J.: A personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an FEM simulation driving support vector machine. Sensors 20, 420 (2020). https://doi.org/10.3390/s20020420
Li, F., Tang, B., Yang, R.: Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment. Measurement 46(8), 2525–2539 (2013). https://doi.org/10.1016/j.measurement.2013.04.061
He, D., Li, R., Zhu, J.: Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Trans. Industr. Electron. 60(8), 3429–3440 (2013)
Van, M., Kang, H.J.: Bearing-fault diagnosis using non-local means algorithm and empirical mode decomposition-based feature extraction and twostage feature selection. IET Sci. Meas. Technol. 9, 671–680 (2015)
Jiang, S.B., Wong, P.K., Liang, Y.C.: A fault diagnostic method for induction motors based on feature incremental broad learning and singular value decomposition. IEEE Access 7, 157796–157806 (2019)