Abstract
Multi-level stator winding failure investigation on the insulation material for industrial induction motor at its incipient stage can achieve tremendous energy savings, minimize maintenance costs, and enhance the operational life. Early detection has the main advantage of preventing motor failure, which gradually leads to catastrophic or complete shutdown. In this paper, the multi-level stator winding failure of an industrial induction motor in the numerical model was designed and implemented using Finite-Element Analysis (FEA). Since multi-level stator winding failure constitutes substantial time and frequency domains, the signature from the motor is analysed employing a time-frequency-based technique using wavelets. Early detection of the multi-level stator winding failures uses signatures from the experimental setup of both healthy and faulty motors by combining both modular and virtual instruments. FEA results were then validated under the same operating conditions using an experimental setup. It has been observed that the results obtained from the FEA are in good agreement with the experimental setup. The frequency component variation in the sensor frequency response for fault conditions at various severity levels suggests multiple frequency bands. When the motor was under the influence of a 50% stator turn-turn fault, a similar frequency band between 700-800 Hz was observed. As a result, the proposed research was accurate, with a correlation factor of 0.96.
Similar content being viewed by others
Abbreviations
- STTF:
-
Stator Turn-Turn Fault
- FEA:
-
Finite Element Analysis
- WFM:
-
Winding Function Method
- DCM:
-
Dynamic Circuit Method
- FEM:
-
Finite Element Method
- MLF:
-
Multi-Level Failure
- \(V_{abc}^{s}\) :
-
Stator Voltage
- \(i_{abc}^{s}\) :
-
Stator Current
- L s r :
-
Mutual Inductance
- \(V_{abc}^{r}\) :
-
Rotor Voltage
- T l :
-
Load Torque
- ω :
-
Angular Speed
- \(\text {V}_{abcf}^{s}\) :
-
Stator Failure Voltage
- \(i_{abcf}^{s}\) :
-
Stator Failure Current
- \(r_{abcf}^{s}\) :
-
Stator Failure Resistance
- \(r_{abc}^{s}\) :
-
Stator Resistance
- L s s :
-
Stator Self Inductance
- \(i_{abc}^{r}\) :
-
Rotor Current
- T e :
-
Electromagnetic Torque
- J :
-
Moment of Inertia
- θ :
-
Angular Rotation
- \(r_{abcf}^{s}\) :
-
Failure Resistance
- \(i_{abcf}^{r}\) :
-
Rotor Failure Current
- \(V_{abcf}^{r}\) :
-
Rotor Failure Voltage
References
Verma AK, Radhika S, Padmanabhan S (2018) Wavelet based fault detection and diagnosis using online mcsa of stator winding faults due to insulation failure in industrial induction machine. In: 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, pp 204–208
Yunusa-Kaltungo A, Kermani MM, Labib A (2017) Investigation of critical failures using root cause analysis methods: Case study of ash cement plc. Eng Fail Anal 73:25–45
Singh J, Singh S, Singh A (2019) Distribution transformer failure modes, effects and criticality analysis (fmeca). Eng Fail Anal 99:180–191
Yetgin AG (2019) Effects of induction motor end ring faults on motor performance. experimental results. Eng Fail Anal 96:374–383
Ranjan G, Verma AK, Radhika S (2019) K-nearest neighbors and grid search cv based real time fault monitoring system for industries. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE, pp 1–5
Vamsi I, Abhinav N, Verma AK, Radhika S (2018) Random forest based real time fault monitoring system for industries. In: 2018 4th international conference on computing communication and automation (ICCCA). IEEE, pp 1–6
Verma AK, Nagpal S, Desai A, Sudha R An efficient neural-network model for real-time fault detection in industrial machine. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05033-z
Verma AK, Jain A, Radhika S (2020) Neuro-fuzzy classifier for identification of stator winding inter-turn fault for industrial machine. In: International conference on modelling, Simulation and Intelligent Computing. Springer, pp 101–110
Kumar Verma A, Radhika S, Surampudi N (2020) Web based application for quick and handy health condition monitoring system for a reliable wind power generation. In: ASME International Mechanical Engineering Congress and Exposition, vol 84669. American Society of Mechanical Engineers, pp V014T14A009
Yu M, Xiao C, Jiang W, Yang S, Wang H (2018) Fault diagnosis for electromechanical system via extended analytical redundancy relations. IEEE Trans Ind Inf 14(12):5233–5244
Ranjan J, Patra K, Szalay T, Mia M, Gupta MK, Song Q, Krolczyk G, Chudy R, Pashnyov VA, Pimenov DY (2020) Artificial intelligence-based hole quality prediction in micro-drilling using multiple sensors. Sensors 20(3):885
Ebrahimi B, Faiz J, Javan-Roshtkhari M, Nejhad AZ (2008) Static eccentricity fault diagnosis in permanent magnet synchronous motor using time stepping finite element method. IEEE Trans Magn 44(11):4297–4300
Vaseghi B, Takorabet N, Meibody-Tabar F (2009) Transient finite element analysis of induction machines with stator winding turn fault. Prog Electromagn Res 95:1–18
Härsjö J, Bongiorno M (2015) Modeling and harmonic analysis of a permanent magnet synchronous machine with turn-to-turn fault. In: 2015 17th European Conference on Power Electronics and Applications (EPE’15 ECCE-Europe). IEEE, pp 1–10
Maraaba L, Al-Hamouz Z, Milhem A, Abido M (2018) Modelling of interior-mount lspmsm under asymmetrical stator winding. IET Electric Power Appl 12(5):693–700
Glowacz A, Glowacz W, Glowacz Z, Kozik J (2018) Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement 113:1–9
Ojaghi M, Sabouri M, Faiz J (2018) Performance analysis of squirrel-cage induction motors under broken rotor bar and stator inter-turn fault conditions using analytical modeling. IEEE Trans Magn 54(11):1–5
Wolkiewicz M, Kowalski CT (2016) Incipient stator fault detector based on neural networks end symmetrical components analysis for induction motor drives. In: 2016 13th selected issues of electrical engineering and electronics (WZEE). IEEE, pp 1–7
Berzoy A, Eldeeb HH, Mohammed O (2018) Online fault detection of stator winding faults in im driven by dtc using the off-diagonal term of the symmetrical component impedance matrix. In: 2018 IEEE applied power electronics conference and exposition (APEC). IEEE, pp 2482–2487
Wu Y, Jiang B, Wang Y Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on crh trains, ISA transactions
Mohammed A, Melecio JI, Djurović S (2018) Stator winding fault thermal signature monitoring and analysis by in situ fbg sensors. IEEE Trans Ind Electron 66(10):8082–8092
Malekpour M, Phung B, Ambikairajah E (2017) Online technique for insulation assessment of induction motor stator windings under different load conditions. IEEE Trans Dielectr Electr Insul 24(1):349–358
Vilhekar TG, Ballal MS, Umre BS (2016) Application of sweep frequency response analysis for the detection of winding faults in induction motor. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp 1458–1463
Dorrell DG, Makhoba K (2017) Detection of inter-turn stator faults in induction motors using short-term averaging of forward and backward rotating stator current phasors for fast prognostics. IEEE Trans Magn 53(11):1–7
Devi NR, Sarma DVS, Rao PVR (2015) Detection of stator incipient faults and identification of faulty phase in three-phase induction motor–simulation and experimental verification. IET Electric Power Appl 9 (8):540–548
Roshanfekr R, Jalilian A (2016) Wavelet-based index to discriminate between minor inter-turn short-circuit and resistive asymmetrical faults in stator windings of doubly fed induction generators: a simulation study. IET Gener Transmiss Distrib 10(2):374– 381
Radhika S, Sabareesh G, Jagadanand G, Sugumaran V (2010) Precise wavelet for current signature in 3ϕ im. Expert Syst Appl 37(1):450–455
Moosavi SS, Esmaili Q, Djerdir A, Amirat YA (2017) Inter-turn fault detection in stator winding of pmsm using wavelet transform. In: 2017 IEEE vehicle power and propulsion conference (VPPC). IEEE, pp 1–5
Praveen G, Vamsi I, Suresh K, Radhika S (2020) Evaluation of surface roughness in incremental forming using image processing based methods. Measurement:108055
Yu M, Lan D, Huang Y, Wang H, Jiang C, Zhao L (2018) Event-based sequential prognosis for uncertain hybrid systems with intermittent faults. IEEE Trans Ind Inf 15(8):4455– 4468
Witkovskỳ V, Frollo I (2020) Measurement science is the science of sciences-there is no science without measurement. Measur Sci Rev 20(1):1–5
Ebrahimi B, Faiz J (2012) Magnetic field and vibration monitoring in permanent magnet synchronous motors under eccentricity fault. IET Electr Power Appl 6(1):35–45
Vamsi I, Sabareesh G, Penumakala P (2019) Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mech Syst Signal Process 124:1–20
Hoang KD, Zhu Z. -Q., Foster M (2013) Direct torque control of permanent magnet brushless ac drive with single-phase open-circuit fault accounting for influence of inverter voltage drop. IET Electric Power Appl 7(5):369–380
Wolkiewicz M, Skowron M (2017) Diagnostic system for induction motor stator winding faults based on axial flux. Power Electron Drives 2(2):137–150
Irhoumah M, Pusca R, Lefevre E, Mercier D, Romary R (2019) Detection of the stator winding inter-turn faults in asynchronous and synchronous machines through the correlation between harmonics of the voltage of two magnetic flux sensors. IEEE Trans Ind Appl 55(3):2682–2689
Keravand M, Faiz J, Soleimani M, Ghasemi-Bijan M, Bandar-Abadi M, Cruz SM, fast A (2017) Precise and low cost stator inter-turn fault diagnosis technique for wound rotor induction motors based on wavelet transform of rotor current. In: Diagnostics for electrical machines, power electronics and drives (SDEMPED). IEEE, pp 254–259
Cheng Y, Wang Z, Zhang W (2018) A novel condition-monitoring method for axle-box bearings of high-speed trains using temperature sensor signals. IEEE Sens J 19(1):205– 213
Dias CG, Pereira FH (2018) Broken rotor bars detection in induction motors running at very low slip using a hall effect sensor. IEEE Sens J 18(11):4602–4613
Dias CG, Chabu IE (2014) Spectral analysis using a hall effect sensor for diagnosing broken bars in large induction motors. IEEE Trans Instrum Meas 63(12):2890–2902
Henao H, Demian C, Capolino G-A (2003) A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Trans Ind Appl 39(5):1272–1279
Maraaba L, Al-Hamouz Z, Abido M (2018) An efficient stator inter-turn fault diagnosis tool for induction motors. Energies 11(3):653
Hamilton A, Cleary A, Quail F (2013) Development of a novel wear detection system for wind turbine gearboxes. IEEE Sens J 14(2):465–473
Luo C, Mo Z, Wang J, Jiang J, Dai W, Miao Q Multiple discolored cyclic harmonic ratio diagram based on meyer wavelet filters for rotating machine fault diagnosis. IEEE Sensors Journal
Verma AK, Akkulu P, Padmanabhan SV, Radhika S (2021) Automatic condition monitoring of industrial machines using fsa-based hall-effect transducer. IEEE Sens J 21(2):1072–1081. https://doi.org/10.1109/JSEN.2020.2990727
Verma AK, Vinod JV, Sudha R A modular zigbee-based iot platform for reliable health monitoring of industrial machines using refsa. In: Microelectronics and Signal Processing. CRC Press, pp 179–188
Wang Z, Zhang Q, Xiong J, Xiao M, Sun G, He J (2017) Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens J 17(17):5581–5588
Xiong J, Zhang Q, Sun G, Zhu X, Liu M, Li Z (2015) An information fusion fault diagnosis method based on dimensionless indicators with static discounting factor and knn. IEEE Sens J 16(7):2060–2069
Li K, Chen P, Wang H (2012) Intelligent diagnosis method for rotating machinery using wavelet transform and ant colony optimization. IEEE Sens J 12(7):2474–2484
Amiruddin AAAM, Zabiri H, Taqvi SAA, Tufa LD (2018) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput Appl:1–26
Sun S, Przystupa K, Wei M, Yu H, Ye Z, Kochan O Fast bearing fault diagnosis of rolling element using lévy moth-flame optimization algorithm and naive bayes. Eksploatacja Niezawodność 22(4)
Kumar A, Vashishtha G, Gandhi C, Zhou Y, Glowacz A, Xiang J (2021) Novel convolutional neural network (ncnn) for the diagnosis of bearing defects in rotary machinery. IEEE Trans Instrum Meas 70:1–10
Verma AK, Spandana P, Padmanabhan SV, Radhika S (2020) Quantitative modeling and simulation for stator inter-turn fault detection in industrial machine. In: Intelligent Computing and Communication. Springer Singapore, pp 87–97
Seera M, Lim CP, Ishak D, Singh H (2012) Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid fmm–cart model. IEEE Trans Neural Netw Learn Syst 23(1):97–108
Acknowledgements
The authors would like to express special thanks of gratitude to Birla Institute of Technology and Science, Pilani - Hyderabad for Additional Competitive Research Grant (BITS/GAU/ACRG/ 2019/H0595) support for a duration of 2 years.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest Statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Verma, A.K., Radhika, S. Multi-Level Stator Winding Failure Analysis on the Insulation Material for Industrial Induction Motor. Exp Tech 46, 441–455 (2022). https://doi.org/10.1007/s40799-021-00490-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40799-021-00490-0