Comparison of Vibration, Sound and Motor Current Signature Analysis for Detection of Gear Box Faults
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Published
Nov 16, 2020
T Praveenkumar
M Saimurugan
K I Ramachandran
Abstract
Gear box is used in automobiles and industries for power transmission under different working conditions and applications. Failure in a gear box at unexpected time leads to increase in machine downtime and maintenance cost. In order to overcome these losses, the most effective condition monitoring technique has to be used for early detection of faults. Vibration and sound signal analysis have been used for monitoring the condition of rotating machineries. Motor Current Signature Analysis (MCSA) has rarely been used in gearbox condition monitoring. This work presents a methodology based on vibration, sound and motor current signal analysis for diagnosis of gearbox faults under various simulated gear and bearing fault conditions. Statistical features were extracted from the raw data of these three transducer signals and the best features were selected from the extracted features. Then the selected features were given as an input to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers and their performances were compared. In recent years, Hybrid Electric Vehicles (HEV) are gaining more interest for their advances and this work had a scope in monitoring the power loss in hybrid electric vehicle gearbox using MCSA.
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Keywords
Hybrid Electric Vehicle (HEV), Automobile Gear box, Fault diagnosis, Artificial Neural Network (ANN), Support Vector Machine (SVM)
References
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Praveenkumar, T., Saimurugan, M., Krishna kumar, K., & Ramachandran, K. I. (2014). Fault diagnosis of automobile gearbox based on machine learning techniques. Procedia Engineering, vol. 97, pp. 2092- 2098.
Negoita, A., Scutaru, Gh., & Ionescu, R.M. (2010). A brief review of monitoring techniques for rotating electrical machines. Bulletin of the Transilvania University of Brasov, vol. 3 (52), Series I.
Saimurugan, M., Ramachandran, K.I., Sugumaran, V., & Sakthivel, N. R. (2011). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications, vol. 38, pp. 3819-3826.
Fekia, N., Clercb, G., & Velexa, P. (2013). Gear and motor fault modeling and detection based on motor current analysis. Electric power R&D, vol. 95, pp. 28-37.
Feng, Z. P., Chu, F. L., & Zuo, M. J. (2011). Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation. Journal of Sound and Vibration, vol. 330, pp. 1225-1243.
Peng, Z. K. Tse, P.W., & Chu, F.L. (2005). An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration, vol. 286, pp. 187-205.
Benko, U., Petrovcic, J., Juricic, D., Tavcar, J., & Rejec, J. (2005). An approach to fault diagnosis of vacuum cleaner motors based on sound analysis. Mechanical Systems and Signal Processing, vol. 19, pp. 427-445.
Siliang Lu., Xiaoxian Wang., Qingbo He., Fang Liu., & Yongbin Liu. (2016). Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals. Journal of Sound and Vibration, vol. 385, pp. 16-32.
Rafiee, J., Rafiee M.A., & Tse P.W. (2010). Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, vol. 37 (6), pp. 4568-4579.
El Hachemi Benbouzid M. A. (2000). Review of Induction Motors Signature Analysis as a Medium for Faults Detection. IEEE Transactions on Industrial Electronics, vol. 47, (5), pp. 984- 993.
Tayyab Waqar., Mustafa Demetgul., & Cemal Kelesoglu. (2012). Fault Diagnosis on bevel gearbox with neural networks and feature extraction. Elektronika ir Elektrotechnika, vol. 21.
Gunal, S., Ece, D. G, & Gerek. (2009). Induction Motor Condition Monitoring Using Notch Filtered Motor Current. Mechanical Systems and Signal Processing, vol. 23, pp. 2658-2670.
Kanika Gupta., & Arunpreet Kaur. (2014). A review on fault diagnosis of induction motor using artificial neural networks. IJSR, vol. 3, pp. 680-689.
Samira ben salem. (2012). Support vector machine-based decision for induction motor fault diagnosis using air-gap torque frequency response. IJCA, vol. 38, pp. 4696-4702.
Alaa Abdulhady Jaber., & Robert Bicker. (2016). Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 017.
Swapna Vora., Jitendra, A., Gaikwad, Jayant., & Kulkarni, V. (2015). Fault diagnosis of bearing of electric motor using wavelet transform and fault classification based on support vector machine. AREEE, vol. 2, pp. 41-46.
Krisztian deak, Imre kocsis, Attila vamosi, & zoltan keviczki, Failure diagnostics with svm in machine maintenance engineering. Annals of the oredea university, vol. 1(1).
Bacha, K., Souahlia, S., & Gossa, M. (2012). Power transformer fault diagnosis based on dissolved gas analysis by support vector machine. Electric Power Systems Research, vol. 83, pp.73-79.
Liu Hong., & Jaspreet Singh Dhupia. (2014). A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, vol. 333, pp. 2164-2180.
Kar, C., & Mohanty, A.R. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, vol. 20, pp. 158-187.
Chandran, P., Lokesha, M., Majumder, M.C., & Raheemv, K.F. A. (2012). Application of Laplace Wavelet Kurtosis and Wavelet Statistical Parameters for Gear Fault Diagnosis. International journal of multidisciplinary sciences and engineering, vol. 3(9), pp. 1-8.
Chaari, F., Fakhfakh, T., & Haddar, M. (2009). Analytical modeling of spur gear tooth crack and influence on gear mesh stiffness. Europen Journal of Mechanics-A/ Solids, vol. 28, pp. 461-468.
Thomson, W., & Fenger, M. (2001). Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine, vol. 12, pp. 26-34.
Hsu, C.W., & Lin, C. J. (2012). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, vol. 13, pp. 415-425.
Samanta, B., & Al-Balushi, K. R. (2013). Artificial Neural Network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, Vol. 17, pp. 317-328.
Mozammel Mia., & Nikhil Ranjan Dhar. (2016). Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network. Measurement, vol. 92, pp. 464-474.
Mayssa Hajar., Amani Raad., & Mohamad Khalil. (2012). Bearing and gear fault detection using artificial neural networks. ACTEA, vol. 1, pp. 101-106. Vapnik, V.N. (1999) The nature of statistical learning theory. Springer-Verlag, (1999), pp. 138-146.
Mohandes, M. A., Halawani, T. O., Rehman, S., & Ahmed A. Hussain. (2004). Support vector machines for wind speed prediction. Renewable Energy, vol. 29, pp. 939-947.
M, Amarnath., V. Sugumaran., & Hemantha Kumar. (2013). Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement, vol. 46, pp. 1250-1256.
T, Praveenkumar., B, Sabhrish., M, Saimurugan., & K.I, Ramachandran. (2018). Pattern Recognition based On-line Vibration Monitoring System for Fault Diagnosis of Automobile Gearbox. Measurement, Vol. 114, pp. 233-242.
Juan Jose Saucedo-Dorantes., Miguel Delgado-Prieto., Juan Antonio Ortega-Redondo., Roque Alfredo Osornio-Rios., & Rene de Jesus Romero-Troncoso. (2016). Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain. Shock and Vibration, Article ID 5467643.
N.R, Sakthivel., V, Sugumaran.,& S.Babudevasenapati. (2010). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, Vol. 37, pp. 4040-4049.
P.V, Kane., & A.B, Andhare. (2016). Application of psychoacoustics for gear fault diagnosis using artificial neural network. Journal of Low Frequency Noise, Vibration and Active Control, Vol. 35, pp. 207-220.
Chandrima Sarkar., Sarah Cooley., & Jaideep Srivastava. (2014). Robust Feature Selection Technique using Rank Aggregation. Applied Artificial Intelligence, Vol. 28(3), pp. 243–257.
K, Selvakuberan., D, Kayathiri., B, Harini., & M, Indra Devi. (2011). An efficient feature selection method for classification in health care systems using machine learning techniques. IEEE Xplore, 3rd International Conference on Electronics Computer Technology (ICECT).
M, Saimurugan., & R, Nithesh. (2016). Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 018.
Laxmikant S, Dhamandea., & Mangesh B. Chaudhari. (2016). Detection of Combined Gear-Bearing Fault in Single Stage Spur Gear Box Using Artificial Neural Network. Procedia Engineering, Vol. 144, pp. 759 – 766.
Winder, R. L., & Littmann, W. E. (1976). Bearing damage analysis. National Bureau of Standard Publication.
Smalley, A. J., Baldwin, R. M., Mauney, D. A., & Millwater, H. R. (1996). Towards risk based criteria for rotor vibration. In International proceedings of the institute of mechanical engineers—Vibrations in rotating machinery pp. 517–527.
James Li, C., & Wu, S. M. (1989). Online detection of localized defects in bearing by pattern recognition analysis. ASME Journal of Engineering for Industries, Vol. 111, pp. 331–336.
O, Asi. (2006). Fatigue failure of a helical gear in a gearbox. Engineering Failure Analysis, Vol. 13, pp. 1116–1125.
Barshikar Raghavendra Rajendra., & Santosh.V, Bhaskar. (2013). Condition Monitoring of Gear Box by Using Motor Current Signature Analysis. International Journal of Scientific and Research Publications, Vol. 3, Issue 8.
Sukhjeet Singh., Amit Kumar., & Navin Kumar. (2014). Motor Current Signature Analysis for Bearing Fault Detection in Mechanical Systems. Procedia Material Science, Vol. 6, pp. 171-177.
Katsuhiko Shibata., Atsushi Takahashi., & Takuya Shirai. (2010). Fault diagnosis of rotating machinery through visualisation of sound signals. Mechanical Systems and Signal Processing, Vol. 14(2), pp. 229-241.
T, Oikawa., M, Tomizawa., & S, Degawa. (1997). New Monitoring System for Thermal Power Plants using Digital Image Processing and Sound Analysis. Control Engineering Practice, Vol. 5, No. 1, pp. 75-78.
Shi, X. Z., Xu, Z. Q., & Xu. (1988). A Study on the automatic recognition of vibration signal for ball bearing faults – The FFTAR feature extraction and classification methods. In Proceedings of ieee international workshop on applied time series analysis (pp. 318–321). World Scientific.
Fannia Pacheco., José Valente de Oliveira., René-Vinicio Sánchez., Mariela Cerrada., Diego Cabrera., Chuan Li., Grover Zurita., & Mariano Artés, (2016). A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions. Neurocomputing, Vol. 194, pp. 192–206.
M, Subrahmanyam., & C, Sujatha. (1997). Using neural networks for the diagnosis of localized defects in ball bearings. Tribology International, Vol. 30, No. 10, pp. 739–752.
Xin Lei., & Peter A. Sandborn. (2016). PHM-Based Wind Turbine Maintenance Optimization Using Real Options. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008.
Praveenkumar, T., Saimurugan, M., Krishna kumar, K., & Ramachandran, K. I. (2014). Fault diagnosis of automobile gearbox based on machine learning techniques. Procedia Engineering, vol. 97, pp. 2092- 2098.
Negoita, A., Scutaru, Gh., & Ionescu, R.M. (2010). A brief review of monitoring techniques for rotating electrical machines. Bulletin of the Transilvania University of Brasov, vol. 3 (52), Series I.
Saimurugan, M., Ramachandran, K.I., Sugumaran, V., & Sakthivel, N. R. (2011). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications, vol. 38, pp. 3819-3826.
Fekia, N., Clercb, G., & Velexa, P. (2013). Gear and motor fault modeling and detection based on motor current analysis. Electric power R&D, vol. 95, pp. 28-37.
Feng, Z. P., Chu, F. L., & Zuo, M. J. (2011). Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation. Journal of Sound and Vibration, vol. 330, pp. 1225-1243.
Peng, Z. K. Tse, P.W., & Chu, F.L. (2005). An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration, vol. 286, pp. 187-205.
Benko, U., Petrovcic, J., Juricic, D., Tavcar, J., & Rejec, J. (2005). An approach to fault diagnosis of vacuum cleaner motors based on sound analysis. Mechanical Systems and Signal Processing, vol. 19, pp. 427-445.
Siliang Lu., Xiaoxian Wang., Qingbo He., Fang Liu., & Yongbin Liu. (2016). Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals. Journal of Sound and Vibration, vol. 385, pp. 16-32.
Rafiee, J., Rafiee M.A., & Tse P.W. (2010). Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, vol. 37 (6), pp. 4568-4579.
El Hachemi Benbouzid M. A. (2000). Review of Induction Motors Signature Analysis as a Medium for Faults Detection. IEEE Transactions on Industrial Electronics, vol. 47, (5), pp. 984- 993.
Tayyab Waqar., Mustafa Demetgul., & Cemal Kelesoglu. (2012). Fault Diagnosis on bevel gearbox with neural networks and feature extraction. Elektronika ir Elektrotechnika, vol. 21.
Gunal, S., Ece, D. G, & Gerek. (2009). Induction Motor Condition Monitoring Using Notch Filtered Motor Current. Mechanical Systems and Signal Processing, vol. 23, pp. 2658-2670.
Kanika Gupta., & Arunpreet Kaur. (2014). A review on fault diagnosis of induction motor using artificial neural networks. IJSR, vol. 3, pp. 680-689.
Samira ben salem. (2012). Support vector machine-based decision for induction motor fault diagnosis using air-gap torque frequency response. IJCA, vol. 38, pp. 4696-4702.
Alaa Abdulhady Jaber., & Robert Bicker. (2016). Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 017.
Swapna Vora., Jitendra, A., Gaikwad, Jayant., & Kulkarni, V. (2015). Fault diagnosis of bearing of electric motor using wavelet transform and fault classification based on support vector machine. AREEE, vol. 2, pp. 41-46.
Krisztian deak, Imre kocsis, Attila vamosi, & zoltan keviczki, Failure diagnostics with svm in machine maintenance engineering. Annals of the oredea university, vol. 1(1).
Bacha, K., Souahlia, S., & Gossa, M. (2012). Power transformer fault diagnosis based on dissolved gas analysis by support vector machine. Electric Power Systems Research, vol. 83, pp.73-79.
Liu Hong., & Jaspreet Singh Dhupia. (2014). A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, vol. 333, pp. 2164-2180.
Kar, C., & Mohanty, A.R. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, vol. 20, pp. 158-187.
Chandran, P., Lokesha, M., Majumder, M.C., & Raheemv, K.F. A. (2012). Application of Laplace Wavelet Kurtosis and Wavelet Statistical Parameters for Gear Fault Diagnosis. International journal of multidisciplinary sciences and engineering, vol. 3(9), pp. 1-8.
Chaari, F., Fakhfakh, T., & Haddar, M. (2009). Analytical modeling of spur gear tooth crack and influence on gear mesh stiffness. Europen Journal of Mechanics-A/ Solids, vol. 28, pp. 461-468.
Thomson, W., & Fenger, M. (2001). Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine, vol. 12, pp. 26-34.
Hsu, C.W., & Lin, C. J. (2012). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, vol. 13, pp. 415-425.
Samanta, B., & Al-Balushi, K. R. (2013). Artificial Neural Network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, Vol. 17, pp. 317-328.
Mozammel Mia., & Nikhil Ranjan Dhar. (2016). Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network. Measurement, vol. 92, pp. 464-474.
Mayssa Hajar., Amani Raad., & Mohamad Khalil. (2012). Bearing and gear fault detection using artificial neural networks. ACTEA, vol. 1, pp. 101-106. Vapnik, V.N. (1999) The nature of statistical learning theory. Springer-Verlag, (1999), pp. 138-146.
Mohandes, M. A., Halawani, T. O., Rehman, S., & Ahmed A. Hussain. (2004). Support vector machines for wind speed prediction. Renewable Energy, vol. 29, pp. 939-947.
M, Amarnath., V. Sugumaran., & Hemantha Kumar. (2013). Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement, vol. 46, pp. 1250-1256.
T, Praveenkumar., B, Sabhrish., M, Saimurugan., & K.I, Ramachandran. (2018). Pattern Recognition based On-line Vibration Monitoring System for Fault Diagnosis of Automobile Gearbox. Measurement, Vol. 114, pp. 233-242.
Juan Jose Saucedo-Dorantes., Miguel Delgado-Prieto., Juan Antonio Ortega-Redondo., Roque Alfredo Osornio-Rios., & Rene de Jesus Romero-Troncoso. (2016). Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain. Shock and Vibration, Article ID 5467643.
N.R, Sakthivel., V, Sugumaran.,& S.Babudevasenapati. (2010). Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, Vol. 37, pp. 4040-4049.
P.V, Kane., & A.B, Andhare. (2016). Application of psychoacoustics for gear fault diagnosis using artificial neural network. Journal of Low Frequency Noise, Vibration and Active Control, Vol. 35, pp. 207-220.
Chandrima Sarkar., Sarah Cooley., & Jaideep Srivastava. (2014). Robust Feature Selection Technique using Rank Aggregation. Applied Artificial Intelligence, Vol. 28(3), pp. 243–257.
K, Selvakuberan., D, Kayathiri., B, Harini., & M, Indra Devi. (2011). An efficient feature selection method for classification in health care systems using machine learning techniques. IEEE Xplore, 3rd International Conference on Electronics Computer Technology (ICECT).
M, Saimurugan., & R, Nithesh. (2016). Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 018.
Laxmikant S, Dhamandea., & Mangesh B. Chaudhari. (2016). Detection of Combined Gear-Bearing Fault in Single Stage Spur Gear Box Using Artificial Neural Network. Procedia Engineering, Vol. 144, pp. 759 – 766.
Winder, R. L., & Littmann, W. E. (1976). Bearing damage analysis. National Bureau of Standard Publication.
Smalley, A. J., Baldwin, R. M., Mauney, D. A., & Millwater, H. R. (1996). Towards risk based criteria for rotor vibration. In International proceedings of the institute of mechanical engineers—Vibrations in rotating machinery pp. 517–527.
James Li, C., & Wu, S. M. (1989). Online detection of localized defects in bearing by pattern recognition analysis. ASME Journal of Engineering for Industries, Vol. 111, pp. 331–336.
O, Asi. (2006). Fatigue failure of a helical gear in a gearbox. Engineering Failure Analysis, Vol. 13, pp. 1116–1125.
Barshikar Raghavendra Rajendra., & Santosh.V, Bhaskar. (2013). Condition Monitoring of Gear Box by Using Motor Current Signature Analysis. International Journal of Scientific and Research Publications, Vol. 3, Issue 8.
Sukhjeet Singh., Amit Kumar., & Navin Kumar. (2014). Motor Current Signature Analysis for Bearing Fault Detection in Mechanical Systems. Procedia Material Science, Vol. 6, pp. 171-177.
Katsuhiko Shibata., Atsushi Takahashi., & Takuya Shirai. (2010). Fault diagnosis of rotating machinery through visualisation of sound signals. Mechanical Systems and Signal Processing, Vol. 14(2), pp. 229-241.
T, Oikawa., M, Tomizawa., & S, Degawa. (1997). New Monitoring System for Thermal Power Plants using Digital Image Processing and Sound Analysis. Control Engineering Practice, Vol. 5, No. 1, pp. 75-78.
Shi, X. Z., Xu, Z. Q., & Xu. (1988). A Study on the automatic recognition of vibration signal for ball bearing faults – The FFTAR feature extraction and classification methods. In Proceedings of ieee international workshop on applied time series analysis (pp. 318–321). World Scientific.
Fannia Pacheco., José Valente de Oliveira., René-Vinicio Sánchez., Mariela Cerrada., Diego Cabrera., Chuan Li., Grover Zurita., & Mariano Artés, (2016). A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions. Neurocomputing, Vol. 194, pp. 192–206.
M, Subrahmanyam., & C, Sujatha. (1997). Using neural networks for the diagnosis of localized defects in ball bearings. Tribology International, Vol. 30, No. 10, pp. 739–752.
Xin Lei., & Peter A. Sandborn. (2016). PHM-Based Wind Turbine Maintenance Optimization Using Real Options. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008.
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Technical Papers