Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique
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.
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: https://doi.org/10.1109/TIE.2018.2875665
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: https://doi.org/10.3390/en11030653
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: https://doi.org/10.1016/j.compeleceng.2018.07.046
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: https://doi.org/10.1109/TIE.2019.2891468
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: https://doi.org/10.1016/j.measurement.2018.09.013
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: https://doi.org/10.1109/TIM.2019.2925247
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: https://doi.org/10.1109/TIA.2019.2895797
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: https://doi.org/10.1109/TPEL.2019.2901598
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: https://doi.org/10.1109/TPEL.2011.2173231
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: https://doi.org/10.1109/TIE.2009.2036026
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: https://doi.org/10.3233/IFS-151684
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: https://doi.org/10.1049/iet-epa.2018.0054
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: https://doi.org/10.1109/TIE.2016.2611585
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: https://doi.org/10.1016/j.isatra.2019.09.020
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: https://doi.org/10.1109/TIE.2017.2758752
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: https://doi.org/10.1109/TIE.2014.2334652
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: https://doi.org/10.1016/j.vlsi.2015.12.006
Transpire Online, (2020). An Efficient Harris Hawks Optimization (HHO) Algorithm for Solving Numerical Expressions, Transpire Online 2019. Available at: https://transpireonline.blog/2020/01/28/an-efficient-harris-hawks-optimization-hho-algorithm-for-solving-numerical-expressions/. [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: https://doi.org/10.1016/j.future.2019.02.028
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: https://doi.org/10.1109/ACCESS.2019.2923166
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: https://doi.org/10.1016/j.asoc.2018.10.036
Copyright (c) 2021 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Plagiarism screening will be conducted by EMITTER Journal Editorial Board using iThenticate Plagiarism Checker and CrossCheck plagiarism screening service. Author should download and signing declaration of plagiarism form here and resubmit it with copyright transfer form via online submission.