Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

  • Annamalai Balamurugan Dept. of EEE, Sathyabama Institute of Science and Technology, Tamil Nadu, India
  • Thangavel Swaminathan Sivakumaran Dept. of EEE, Sasurie College of Engineering, Tirupur, Tamil Nadu, India
Keywords: Multi-phase induction motor, Fault, Distorted waveforms, Frequency components, Stator winding, Statistical measures


In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision.


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Elbouchikhi, Elhoussin, Yassine Amirat, Gilles Feld, and Mohamed Benbouzid. Generalized Likelihood Ratio Test Based Approach for Stator-Fault Detection in a PWM Inverter-Fed Induction Motor Drive. IEEE Transactions on Industrial Electronics. Vol. 66, No. 8, pp. 6343-6353, 2019. doi:10.1109/tie.2018.2875665 DOI:

Maraaba, Luqman, Zakariya Al-Hamouz, and Mohammad Abido. An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors. Energies (Basel) Vol. 11, No. 3, pp. 653, 2018. doi:10.3390/en11030653 DOI:

Rebouças Filho, Pedro Pedrosa, Navar MM Nascimento, Igor R. Sousa, Cláudio MS Medeiros, and Victor Hugo C. de Albuquerque. A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning. Computers & Electrical Engineering. Vol. 71, pp. 440-451, 2018. doi:10.1016/j.compeleceng.2018.07.046 DOI:

Contreras-Hernandez, Jose L., Dora Luz Almanza-Ojeda, Sergio Ledesma-Orozco, Arturo Garcia-Perez, Rene J. Romero-Troncoso, and Mario A. Ibarra-Manzano. Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection. IEEE Transactions on Industrial Electronics. Vol. 66, No. 11, pp. 8843-8850, 2019. doi:10.1109/tie.2019.2891468 DOI:

Singh, Megha, and Abdul Gafoor Shaik. Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine. Measurement. Vol. 131, pp. 524-533, 2019. doi:10.1016/j.measurement.2018.09.013 DOI:

Shao, Siyu, Ruqiang Yan, Yadong Lu, Peng Wang, and Robert Gao. DCNN-based Multi-signal Induction Motor Fault Diagnosis. IEEE Trans Instrum Meas. pp. 1-1, 2019. doi:10.1109/tim.2019.2925247 DOI:

Ali, Mohammad Zawad, Md Nasmus Sakib Khan Shabbir, Xiaodong Liang, Yu Zhang, and Ting Hu. Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals. IEEE Trans Ind Appl. Vol. 55, No. 3, pp. 2378-2391, 2019. doi:10.1109/tia.2019.2895797 DOI:

Hajary, Ali, Reza Kianinezhad, S. Gh Seifossadat, S. S. Mortazavi, and Alireza Saffarian. Detection and Localization of Open-Phase Fault in Three-Phase Induction Motor Drives Using Second Order Rotational Park Transformation. IEEE Trans Power Electron. Vol. 34, No. 11, pp. 11241-11252, 2019. doi:10.1109/tpel.2019.2901598 DOI:

Consoli Alfio. Special Section on Robust Operation of Electrical Drives. IEEE Trans Power Electron. Vol. 27, No. 2, pp. 476-478, 2012. doi: 10.1109/tpel.2011.2173231 DOI:

de Lillo, Liliana, Lee Empringham, Pat W. Wheeler, Sudarat Khwan-On, Chris Gerada, M. Nazri Othman, and Xiaoyan Huang. Multiphase Power Converter Drive for Fault-Tolerant Machine Development in Aerospace Applications. IEEE Transactions on Industrial Electronics. Vol. 57, No. 2, pp. 575-583, 2010. doi:10.1109/tie.2009.2036026 DOI:

Gnanaprakasam C, Chitra K. S-transform and ANFIS for detecting and classifying the vibration signals of induction motor. Journal of Intelligent & Fuzzy Systems. Vol. 29, No. 5, pp. 2073-2085, 2015. doi:10.3233/ifs-151684 DOI:

Hassan, Ola E., Motaz Amer, Ahmed K. Abdelsalam, and Barry Williams. Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review. IET Electric Power Applications. Vol. 12, No. 7, pp. 895-907, 2018. doi:10.1049/iet-epa.2018.0054 DOI:

Surya Gulamfaruk , Khan Z, Makarand Ballal, Hiralal Suryawanshi. A Simplified Frequency-Domain Detection of Stator Turn Fault in Squirrel-Cage Induction Motors Using an Observer Coil Technique. IEEE Transactions on Industrial Electronics. Vol. 64, No. 2, pp. 1495-1506, 2017. doi:10.1109/tie.2016.2611585 DOI:

Wu, Yunkai, Bin Jiang, and Yulong Wang. Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains. ISA Trans. 2019. doi:10.1016/j.isatra.2019.09.020 DOI:

Yang, Shih-Chin, Yu-Liang Hsu, Po-Huan Chou, Guan-Ren Chen, and Da-Ren Jian. Online Open-Phase Fault Detection for Permanent Magnet Machines With Low Fault Harmonic Magnitudes. IEEE Transactions on Industrial Electronics. Vol. 65, No. 5, pp. 4039-4050, 2018. doi: 10.1109/tie.2017.2758752 DOI:

Kuruppu Sandun, Kulatunga N. D-Q Current Signature-Based Faulted Phase Localization for SM-PMAC Machine Drives. IEEE Transactions on Industrial Electronics. Vol. 62, No. 1, pp. 113-121, 2015. doi:10.1109/tie.2014.2334652 DOI:

Safari, Azadeh, Cheecottu Vayalil Niras, and Yinan Kong. Power-performance enhancement of two-dimensional RNS-based DWT image processor using static voltage scaling. Integration. Vol. 53, pp. 145-156, 2016. doi:10.1016/j.vlsi.2015.12.006 DOI:

Transpire Online, (2020). An Efficient Harris Hawks Optimization (HHO) Algorithm for Solving Numerical Expressions, Transpire Online 2019. Available at: [Accessed on: Mar, 2020]

Heidari, Ali Asghar, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Huiling Chen. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems. Vol. 97, pp. 849-872, 2019. doi:10.1016/j.future.2019.02.028 DOI:

Kartci, Aslihan, Agamyrat Agambayev, Mohamed Farhat, Norbert Herencsar, Lubomir Brancik, Hakan Bagci, and Khaled N. Salama. Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm. IEEE Access, Vol. 7, pp. 80233-80246, 2019. doi: 10.1109/access.2019.2923166 DOI:

Rao, Haidi, Xianzhang Shi, and Ahoussou Kouassi Rodrigue. Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput, Vol. 74, pp. 634-642, 2019. doi:10.1016/j.asoc.2018.10.036 DOI:

How to Cite
Balamurugan, A., & Sivakumaran, T. S. (2021). Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique. EMITTER International Journal of Engineering Technology, 9(1), 45-59.