Bearing fault diagnosis for electric machines using motor current signals and state classification



  • Dang Huu Hai Institute of Electronics, Academy of Military Science and Technology
  • Bui Ngoc My Academy of Military Science and Technology
  • Hoang Van Phuc Institute of System Integration, Military Technical Academy
  • Bui Quy Thang (Corresponding Author) Institute of System Integration, Military Technical Academy
  • Le Thi Huyen Naval Technical Institute



Bearing fault diagnosis, motor current signal, state classification


Procedures for diagnosing bearing failures of induction motors based on motor current signals in published methods commonly denoise signals acquired from current sensors, then extract the typical characteristics from the denoised signals, and use a classifier to discriminate the state of bearing. However, the current signals in practice could be affected by surrounding noises, creating extraordinary peaks in the signals which may lead to an inaccurate diagnostic result. Thus, the traditional methods may not be very effective in diagnosing failures for induction motors using motor current signals in real-time. To mitigate these issues, this work introduces a new technique, which consists of characterizing the bearing health as a state vector composed of signal features, evaluating the real bearing status from the characteristic-space using a Kalman filter and a k-NN classifier. This technique still achieves quite high precision even in noisy condition. Experimental results with noise-adding signals demonstrate that the proposed technique has a mean proper identification proportion of 92.06% and a mean false proportion of 7.94%, while the conventional ones turn out a maximum mean true identification proportion of 53.12 % and a minimum mean mistake proportion of 46.88%.


[1]. Singh GK. "Induction machine drive condition monitoring and diagnostic research—a survey". Electr Power Syst Res; 64:145–58, (2003). DOI:

[2]. Bessous N, Sbaa S, Megherbi AC. "Mechanical fault detection in rotating electrical machines using MCSA-FFT and MCSA-DWT techniques". Bull Pol Acad Sci Tech Sci 67 (2019).

[3]. Lei Y. "Fault diagnosis of rotating machinery based on empirical mode decomposition. Struct. Health Monit". Adv. Signal Process. Perspect., Springer, p. 259–92, (2017). DOI:

[4]. Li Y, Xu M, Wang R, Huang W. "A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy". J Sound Vib 360:277–99 (2016). DOI:

[5]. Gu X, Chen C. "Rolling bearing fault signal extraction based on stochastic resonance-based denoising and VMD". Int J Rotating; (2017). DOI:

[6]. Yan X, Jia M. "A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing". Neurocomputing 313:47–64 (2018). DOI:

[7]. He F, Ye Q. "A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm". Sensors 22:1410 (2022). DOI:

[8]. Chen FF, Li M, Chen BJ. "Fault diagnosis of roller bearing based on hybrid feature set and weighted KNN". J Mech Transm 40:138–43 (2016).

[9]. Kang M, Kim J, Wills LM, Kim J-M. "Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis". IEEE Trans Ind Electron 62:7749–61 (2015). DOI:

[10]. Kay SM. "Fundamentals of statistical signal processing: estimation theory". Prentice-Hall, Inc.; (1993).

[11]. Brown RG, Hwang PY. "Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions". Introd Random Signals Appl Kalman Filter MATLAB Exerc Solut (1997).

[12]. Koutroumbas K, Theodoridis S. "Pattern recognition". Academic Press; (2008).

[13]. Smith KJ. Precalculus: "A functional approach to graphing and problem solving". Jones & Bartlett Publishers; (2011).

[14]. Lessmeier C, Kimotho JK, Zimmer D, Sextro W. "Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification". PHM Soc. Eur. Conf., vol. 3, (2016).

[15]. Hoang DT, Kang HJ. "A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion". IEEE Trans Instrum Meas 69:3325–33 (2020). DOI:




How to Cite

Dang Huu, H., M. Bui Ngoc, P. Hoang Van, T. Bui Quy, and H. Le Thi. “Bearing Fault Diagnosis for Electric Machines Using Motor Current Signals and State Classification”. Journal of Military Science and Technology, vol. 89, no. 89, Aug. 2023, pp. 15-24, doi:10.54939/1859-1043.j.mst.89.2023.15-24.



Research Articles