PL EN
Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals
 
More details
Hide details
1
Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, Algeria.
2
Department of Electrical Engineering, Mohamed Cherif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria
CORRESPONDING AUTHOR
lakehal Abdelaziz   

Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, Algeria.
Submission date: 2020-07-31
Final revision date: 2021-01-09
Acceptance date: 2021-02-05
Online publication date: 2021-02-09
Publication date: 2021-03-04
 
Diagnostyka 2021;22(1):65–74
 
KEYWORDS
TOPICS
ABSTRACT
Installations and the detection of their faults has become a major challenge. In order to develop a reliable approach for monitoring and diagnosis faults of these components, a test rig was mounted. In this article, a Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) has been structured and optimized for online monitoring of induction motors. The input layer of our ANN used eight indicators calculated from the collected time signals and which represent the different states of the motor (Healthy, broken rotor bars, bearing fault and Misalignment) and the output layer used a codified matrix. However, based on L27 Taguchi design, the architecture for the hidden layers of our network is chosen, with the use of the Levenberg-Marquardt learning algorithm. Garson's algorithm and connection weight approach showed that there's a great sensitivity of the crest factor, the kurtosis and the variance on the effectiveness of our diagnostic system. Consequently, the obtained results are capable of detecting faults in the induction motor under different operating conditions.
FUNDING
The authors would like to thank the Directorate General for Scientific Research and Technological Development (DGRSDT) for financial support (PRFU code: A01L09UN410120190002). Also, they would like to express their appreciation for the valuable time that anonymous reviewers have dedicated to the review process.
 
REFERENCES (35)
1.
Choudhary A, Goyal D, Shimi SL, Akula A. Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. Arch Computat Methods Eng 2019; 26: 1221–1238. https://doi.org/10.1007/s11831....
 
2.
Chen Z, Deng S, Chen X, Li C, Sanchez RV, Qin H. Deep neural networks-based rolling bearing fault diagnosis, Microelectronics Reliability 2017; 75: 327-333 https://doi.org/10.1016/j.micr....
 
3.
Da Silva AM, Povinelli RJ, Demerdash NA, Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Trans. Ind. Electron. 2008; 55: 1310–1318. https://doi.org/10.1109/TIE.20....
 
4.
Verucchi C, Bossio J, Bossio G, Acosta G. Misalignment detection in induction motors with flexible coupling by means of estimated torque analysis and MCSA. Mechanical Systems and Signal Processing 2016; 80: 570-581. https://doi.org/10.1016/j.ymss....
 
5.
Glowacz A, Glowacz W, Glowacz Z, Kozik J. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement 2018; 113: 1-9. https://doi.org/10.1016/j.meas....
 
6.
Tsypkin M.Induction motor condition monitoring: Vibration analysis technique - diagnosis of electromagnetic anomalies. IEEE AUTOTESTCON, Schaumburg, IL 2017: 1-7. https://doi.org/10.1109/AUTEST....
 
7.
Culbert I, Letal J. Signature Analysis for Online Motor Diagnostics: Early Detection of Rotating Machine Problems Prior to Failure. IEEE Industry Applications Magazine 2017; 23: 76-81. https://doi.org/10.1109/MIAS.2....
 
8.
Filippetti F, Franceschini G, Tassoni C, Vas P.Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Transactions on Industrial Electronics 2000; 47: 994-1004. https://doi.org/10.1109/41.873....
 
9.
Burriel-ValenciaJ, Puche-Panadero R, Martinez-Roman J, Sapena-Bano A, Pineda-Sanchez M, Perez-Cruz J, Riera-Guasp M. Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems. Electronics 2019; 8. https://doi.org/10.3390/electr....
 
10.
Guerra de Araujo CruzA. Delgado Gomes R, Antonio Belo F, Cavalcante Lima Filho A. A Hybrid System Based on Fuzzy Logic to Failure Diagnosis in Induction Motors. IEEE Latin America Transactions 2017; 15: 1480-1489. https://doi.org/10.1109/TLA.20....
 
11.
Ali MZ, Shabbir MNSK, Liang X, Zhang Y, Hu T. Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals. IEEE Transactions on Industry Applications 2019; 55: 2378-2391. https://doi.org/10.1109/TIA.20....
 
12.
Lakehal A. Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications. Bulletin of the Polish Academy of Sciences: Technical Sciences 2020; 68: 467-476. https://doi.org/10.24425/bpast....
 
13.
Zhang Y, Hu T, Liang X, Ali MZ, Shabbir MNSK. Fault Detection and Classification for Induction Motors Using Genetic Programming. In: Sekanina L., Hu T., Lourenço N., Richter H., García-Sánchez P. (eds) Genetic Programming. EuroGP 2019. Lecture Notes in Computer Science, Vol 11451. Springer, Cham. https://doi.org/10.1007/978-3-....
 
14.
Pezzani CM, Fontana JM, Donolo PD, De Angelo CH, Bossio GR, Silva LI. SVM-Based System for Broken Rotor Bar Detection in Induction Motors. IEEE ANDESCON, Santiago de Cali 2018: 1-6. https://doi.org/10.1109/ANDESC....
 
15.
Lashkari N, Azgomi HF, Poshtan J, Poshtan M. Asynchronous motors fault detection using ANN and fuzzy logic methods. IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, 2016: 1-5. https://doi.org/10.1109/ECCE.2....
 
16.
Cho HC, Knowles J, Fadali MS, Lee KS. Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling. IEEE Transactions on Control Systems Technology 2010; 18: 430-437. https://doi.org/10.1109/TCST.2....
 
17.
Rajeswaran N, Lakshmi Swarupa M, Sanjeeva Rao T, Chetaswi K.Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor. Materials Today: Proceedings 2018; 5: 565-571. https://doi.org/10.1016/j.matp....
 
18.
Unal M, Onat M, Demetgul M, Kucuk H. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement 2014; 58: 187-196. https://doi.org/10.1016/j.meas....
 
19.
Ghate VN, Dudul SV. Optimal MLP neural network classifier for fault detection of three phase induction motor. Expert Systems with Applications 2010; 37: 3468-3481. https://doi.org/10.1016/j.eswa....
 
20.
Gui-li Y, Shi-wei Q, Mi G. Motor fault diagnosis of RBF neural network based on immune genetic algorithm. 25th Chinese Control and Decision Conference (CCDC), Guiyang 2013: 1060-1065. https://doi.org/10.1109/CCDC.2....
 
21.
Gangsar P, Tiwari R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing 2020; 144: 106908. https://doi.org/10.1016/j.ymss....
 
22.
Kumar Verma A, Sarangi S, H. Kolekar M. Misalignment fault detection in induction motor using rotor shaft vibration and stator current signature analysis. International Journal of Mechatronics and Manufacturing Systems 2013; 6: 422-436. https://doi.org/10.1504/IJMMS.....
 
23.
Abd-el-Malek MK. Abdelsalam A, E. Hassan O. Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing 2017; 93: 332-350. https://doi.org/10.1016/j.ymss....
 
24.
Khoualdia T, Lakehal A, Chelli Z. Practical Investigation on Bearing Fault Diagnosis Using Massive Vibration Data and Artificial Neural Network”. In: Farhaoui Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, Vol 81. Springer, Cham. https://doi.org/10.1007/978-3-....
 
25.
Dhamande S, Chaudhari M. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement 2018; 125: 63-77. https://doi.org/10.1016/j.meas....
 
26.
Laissaoui A, Bouzouane B, Miloudi A, Hamzaoui N. Perceptive analysis of bearing defects (Contribution to vibration monitoring). Applied Acoustics 2018; 140: 248-255. https://doi.org/10.1016/j.apac....
 
27.
WangZH, Gong DY, Li X, Li GT, Zhang DH. Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA). International Journal of Advanced Manufacturing Technology 2017; 93: 3325-3338. https://doi.org/10.1007/s00170....
 
28.
Khoualdia K, Hadjadj Aoul E, Khoualdia T. (in press). Application of optimised neural networks models in gears and bearings faults diagnosis. International Journal of Vehicle Noise and Vibration.
 
29.
Chen PC, Yang MW, Wei CH, Lin SZ. Selection of blended amine for CO2 capture in a packed bed scrubber using the Taguchi method. International Journal of Greenhouse Gas Control 2016; 45: 245-252. https://doi.org/10.1016/j.ijgg.... 9. Huang CN, Yu CC. Integration of Taguchi's method and multiple-input, multiple-output ANFIS inverse model for the optimal design of a water-cooled condenser. Applied Thermal Engineering 2016; 98: 605-609. https://doi.org/10.1016/j.appl....
 
30.
Xie J, Yuan C. Parametric study of ice thermal storage system with thin layer ring by Taguchi method. Applied Thermal Engineering 2016; 98: 246-255. https://doi.org/10.1016/j.appl....
 
31.
Nor NHM, Muhamad N, Ibrahim MHI, Ruzi M, Jamaludin KR. Optimization of injection molding parameter of Ti-6Al-4 V powder mix with palm stearin and polyehylene for the highest green strength by using Taguchi method. International Journal of Mechanical and Materials Engineering (IJMME) 2011; 6(1): 126-132.
 
32.
Khoualdia T, Hadjadj Aoul E, Bouacha K, Ould Abdeslam D. Multi-objective optimization of ANN fault diagnosis model for rotating machinery using grey rational analysis in Taguchi method. Int J Adv Manuf Technol 2017; 89: 3009–3020. https://doi.org/10.1007/s00170....
 
33.
Garson DG. Interpreting neural network connection weights. Arti. Intell. Expert 1991; 6: 46–51.
 
34.
Acharyya R, Dey A. Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput & Applic 2019; 31: 8087–8100. https://doi.org/10.1007/s00521....
 
35.
Acharyya R, Dey A, Kumar B. Finite element and ANN-based prediction of bearing capacity of square footing resting on the crest of c-φ soil slope. International Journal of Geotechnical Engineering 2020; 14(2): 176-187. https://doi.org/10.1080/193863....
 
eISSN:2449-5220
ISSN:1641-6414