• Vol 9, No 1 (2018)
  • Electrical, Electronics, and Computer Engineering

Wavelet Transform Based Ball Bearing Fault Detection Scheme for Heavy Duty Mining Electrical Motors under Supply Frequency Regulation using MCSA

Ashish Kumar Sinha, Sukanta Das, Tarun Kumar Chatterjee

Publish at : 27 Jan 2018 - 00:00
IJtech : IJtech Vol 9, No 1 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i1.1507

Cite this article as:
Sinha, A.K., Das, S., Chatterjee, T.K., 2018. Wavelet Transform Based Ball Bearing Fault Detection Scheme for Heavy Duty Mining Electrical Motors under Supply Frequency Regulation using MCSA. International Journal of Technology. Volume 9(1), pp. 170-180
Ashish Kumar Sinha Department of Mining Machinery Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad
Sukanta Das Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad
Tarun Kumar Chatterjee Department of Mining Machinery Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad
Email to Corresponding Author


Most heavy duty mining electrical drives employ squirrel cage induction motors (SCIMs) which are subjected to various undesirable stresses. Therefore, condition monitoring of the SCIMs is indispensable for achieving production goals with minimum downtime in a fault-free working environment. Because bearing damage is the most frequently occurring fault in SCIMs, an effective fault detection scheme will aid in achieving production targets in an industrial mining scenario. In this regard, the present work intends to propose an effective fault monitoring algorithm, which is immune to supply frequency regulation, for the detection of ball bearing damage in an SCIM. Discrete Wavelet Transform (DWT) is used for the design of the fault detection scheme. Validation of the proposed scheme is done in a LabVIEW based laboratory interface. The complete analysis is carried out in MATLAB/ Simulink using a 5.5 kW, 3-phase, 415 V, 50 Hz SCIM.

Bearing fault; Condition monitoring; Discrete wavelet transform; Squirrel cage induction motor


The precarious working environment prevalent in mines increases the likelihood of damage to ball bearings. Quick detection is indispensable for reduced downtime of drive systems in underground mines. A DWT-based MRA of stator current for ball bearing damage detection is presented in the present work. The proposed fault detection scheme employs the standard deviation of level-five detailed coefficient [d5] from the pre-fed values of the same for the detection of ball bearing damage, based on the careful choice of an analyzing mother wavelet (‘sym31’ in the present work) and working sampling frequency (6.25 kHz in the present work). The proposed scheme is further validated in real time by a LabVIEW based 5.5 kW SCIM laboratory interface. The proposed scheme is also robust to ± 4% (approx.) supply frequency regulation and ± 1.5% (approx.) sampling frequency variation at a given supply frequency. The implementation of this proposed approach requires a minimal instrumentation system, ideal for the grimy and hazardous mine environment.


Albrecht, P.F., Appiarius, J.C., McCoy, R.M., Owen, E.L., Sharma, D.K., 1986. Assessment of the Reliability of Motors in Utility Applications – Updated.  IEEE Transactions on Energy Conversion, Volume EC-1(1), pp. 39–46

Bindu, S., Thomas, V.V., 2014. Diagnoses of Internal Faults of Three Phase Squirrel Cage Induction Motor — A Review. In: International Conference on Advances in Energy Conversion Technologies (ICAECT). Manipal, India, pp. 48–54

Burrus, C.S., Guo, H., 1998. Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, USA

Choi, S. Akin, B., Rahimian, M.M., Toliyat, H.A., 2011. Implementation of a Fault-diagnosis Algorithm for Induction Machines based on Advanced Digital-signal-processing Techniques. IEEE Transactions on Industrial Electronics, Volume 58(3), pp. 937–948

Elkasabgy, N.M., Eastham, A.R., Dawson, G.E., 1992. Detection of Broken Bars in the Cage Rotor on an Induction Machine. IEEE Transactions on Industry Applications, Volume 28(1), pp. 165–171

Eren, L., Devaney, M.J., 2001. Motor Bearing Damage Detection via Wavelet Analysis of the Starting Current Transient. In: IEEE Instrumentation and Measurement Technology Conference. Budapest, pp. 1797–1800

Eschmann, P., Hasbargen, L., Weigand, K., 1958. Ball and Roller Bearings: Their Theory, Design, and Application. K.G. Haydon & Co. Ltd, London, UK

Gritli, Y., Bellini, A., Rossi, C., Casadei, D., Filippetti, F., Capolino, G.-A., 2017. Condition Monitoring of Mechanical Faults in Induction Machines from Electrical Signatures: Review of Different Techniques. In: 11th International Symposium on Diagnostics for Electrical Machines Power Electronics and Drives (SDEMPED). Tinos, Greece, pp. 77–84

Group, I.M.R.W., 1985. Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I. IEEE Transactions on Industry Applications, Volume IA-21(4), pp. 853–864

Kliman, G.B., Stein, J., 1990. Induction Motor Fault Detection via Passive Current Monitoring. In: Proceedings of the International Conference on Electric Machines. Cambridge, MA, pp. 13–17

Kliman, G.B., Stein, J., 1992. Methods of Motor Current Signature Analysis. Electric Machines and Power Systems, Volume 20(5), pp. 463–474

Krause, P.C., 1986. Analysis of Electric Machinery. McGraw-Hill Book Company, USA

Kumar, P., Sinha, A.K., Chatterjee, T.K., 2016. An Assessment of Vibration Monitoring as an Effective Tool for Induction Motor Condition Monitoring and Fault Diagnosis: A Brief Review. International Journal of Control Theory and Applications, Volume 9(41), pp. 407–416

Mobley, R.K., 1990. An Introduction to Predictive Maintenance. Van Nostrand Reinhold, New York, USA

Riddle, J., 1955. Ball Bearing Maintenance. University of Oklahoma Press, Norman, Oklahoma

Rodriguez, P.J., Belahcen, A., Arkkio, A., 2006. Signatures of Electrical Faults in the Force Distribution and Vibration Pattern of Induction Motors. IEE Proceedings: Electric Power Applications, Volume 153(4), pp. 523–529

Schoen, R.R., Habetler, T.G., 1993. Effects of Time Varying Loads on Rotor Fault Detection in Induction Machines. In: Conference Record of the 28th Annual IAS Meeting. Toronto, Ontario, Canada, pp. 324–330

Shashidhara, S.M., Raju, P.S., 2013. Tradeoff Analysis of Wavelet Transform Techniques for the Detection of Broken Rotor Bars in Induction Motors. Advance in Electronic and Electric Engineering, Volume 3(8), pp. 1019–1030

Shi, P., Chen, Z., Vagapov, Y., 2013. Wavelet Transform Based Broken Rotor-bar Fault Detection and Diagnosis Performance Evaluations. International Journal of Computer Applications, Volume 69(14), pp. 36–43

Siddiqui, K.M., Sahay, K., Giri, V.K., 2015. Detection of Bearing Fault in Inverter Fed Induction Motor by Transformative Techniques. In: Annual IEEE India Conference (INDICON). New Delhi, India, pp. 1–6

Singh, G.K., Kazzaz, S.A.S.A., 2009. Isolation and Identification of Dry Bearing Faults in Induction Machine using Wavelet Transform. Tribology International, Volume 42, pp. 849–861

Sinha, A.K., Das, S., Chatterjee, T.K., 2016. A Case Study of bearing Fault Monitoring Techniques for Induction Motors. Journal of Mines, Metals and Fuels, Volume 64(5&6), pp. 249–255

Su, H., Chong, K.T., 2007. Induction Machine Condition Monitoring using Neural Network Modeling. IEEE Transactions on Industrial Electronics, Volume 54(1), pp. 241–249

Taher, S.A., Malekpour, M., 2011. A Novel Technique for Rotor Bar Failure Detection in Single-cage Induction Motor using FEM and MATLAB/SIMULINK. Mathematical Problems in Engineering, Volume  2011, pp. 1–14

Vas, P., 1999. Parameter Estimation, Condition Monitoring, and Diagnosis for Electrical Machines. Clarendon, Oxford, UK