Abstract
Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor’s active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55:4109–4126
Filippetti F, Franceschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47(5):994–1004
Penman J, Yin CM (1994) Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines. IEE Proc Electr Power Appl 141(6):317–322
Zhong F, Shi T, He T (2005) Fault diagnosis of motor bearing using self-organizing maps. Electr Mach Syst ICEMS 3:2411–2414
Salles G, Filippetti F, Tassoni C, Grellet G, Franceschini G (2002) Monitoring of induction motor load by neural network techniques. IEEE Trans Power Electron 15(4):762–768
Bossio JM, De Angelo CH, Bossio GR, García G (2010) Fault diagnosis on induction motors using self-organizing maps. In: The 9th international conference of the IEEE/IAS industry applications
Germen E, Gökhan ED, Gerek ÖN (2007) Self organizing map (SOM) approach for classification of mechanical faults in induction motors, In: International work-conference on artificial neural networks, LNCS 4507, Springer, pp 855–861
Kowalski CT, Orlowska-Kowalska T (2003) Neural networks application for induction motor faults diagnosis. Math Comput Simul 63(3–5):435–448
Kato T, Inoue K, Takahashi T, Kono Y (2007) Automatic fault diagnosis method of electrical machinery and apparatus by using Kohonen’s self-organizing map. In: Power conversion conference, pp 1224–1229
Sid O, Menaa M, Hamdani S, Touhami O, Ibtiouen R (2011) Self-organizing map approach for classification of electricals rotor faults in induction motors. In: EPECS, pp 1–6
Bossio G, De Angelo C, Bossio JM, Pezzani C (2009), Separating broken rotor bars and load oscillations on IM fault diagnosis through the instantaneous active and reactive currents. IEEE Trans Ind Electron, 56:4571–4580. ISSN:0278-0046
Kohonen T (2001) Self organizing maps, 3rd edn. Springer, Berlin
Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM toolbox for Matlab
Long W, Habetler TG, Harley RG (2005) Separating load torque oscillation and rotor fault effects in stator current-based motor condition monitoring. In: IEEE international conference on electric machines and drives, pp 1889–1894
Wu L, Habetler, TG, Harley RG (2007) A review of separating mechanical load effects from rotor faults detection in induction motors. In: IEEE international symposium on diagnostics for electric machines, power electronics and drives, pp 221–225
De Angelo C, Bossio G, Garcia G (2010) Discriminating broken rotor bar from oscillating load effects using the instantaneous active and reactive powers. Electr Power Appl 4:281–290. ISSN:1751-8660
Krause PC, Wasynczuk O, Sudhoff SD (1996) Analysis of electric machinery. IEEE Press, New York, pp 133–210
Bossio JM, Bossio GR, De Angelo CH (2009) Angular misalignment in induction motors with flexible coupling. In: The 35th annual conference of the IEEE Industrial Electronics Society
Bellini A, Filippetti F, Franceschini G, Tassoni C, Kliman GB (2001) Quantitative evaluation of induction motor broken bars by means of electrical signature analysis. IEEE Trans Ind Appl 37(5):1248–1255
Verucchi CJ, Acosta GG, Carusso E (2005) Influence of the motor load inertia and torque in the fault diagnosis of rotors in induction machines. IEEE Lat Am Trans 3(4):48–53
De Angelo C, Bossio G, Bossio JM, Garcia G (2008) Broken bar detection in single-phase reciprocating compressors. In: The 34th annual conference of the IEEE Industrial Electronics Society, pp 1125–1130
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bossio, J.M., De Angelo, C.H. & Bossio, G.R. Self-organizing map approach for classification of mechanical and rotor faults on induction motors. Neural Comput & Applic 23, 41–51 (2013). https://doi.org/10.1007/s00521-012-1255-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-012-1255-0