Int. Journal of Renewable Energy Development 7 (1) 2018: 43-52 Page | 43 © IJRED – ISSN: 2252-4940, February 15 th 2018, All rights reserved Contents list available at IJRED website Int. Journal of Renewable Energy Development (IJRED) Journal homepage: http://ejournal.undip.ac.id/index.php/ijred Automatic and Online Detection of Rotor Fault State Ali Ouanas * *, Ammar Medoued*, Salim Haddad**, Mourad Mordjaoui*, Djamel Sayad* *Department of Electrical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000 Algeria. **Department of Mechanical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000 Algeria. ABSTRACT. In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench. Keywords: : broken bar fault, Fuzzy c mean clustering, Hilbert Transform, neural network, KNN, envelope. Article History: Received July 16 th 2017; Received: October 5th 2017; Accepted: January 6 th 2018; Available online How to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52. http://dx.doi.org/10.14710/ijred.7.1.43-52 1. Introduction Due to their great benefits, induction motors are widely used in industry. The industrial processes require good reliability and safety of machines operation. However, the unexpected failure of the machines leads to losses in the production process, in addition to the very high cost of maintenance and long stopping times that may cause. Usually, regular maintenance and planned maintenance schedules are carried out in the aim of detecting problems in the machine before resulting in catastrophic failures (Tavner, 2008). Therefore, a monitoring system is quite necessary to improve the availability and to increase the expected lifetime in various domains like electricity generation (Rahman et al., 2017; Toke, 2015; Sreedharan et al., 2011; Munshi and Sampath, 2016; McLaughlin and Pearce, 2013; Benakcha et al. 2017). In fact, the monitoring of the proper functioning of the machine is becoming quite useful and increasingly crucial to eliminate any negative impact. In recent years, researchers have studied several varieties of machine faults, such as eccentricity faults, bearing faults, winding faults and broken bars faults. This latter represents about 7% of the totality of the induction motor breakdowns (Bonnett and Yung, 2008). * Corresponding author: amedoud75@yahoo.fr When one bar of the squirrel cage rotor is broken, it may not cause an immediate failure in the induction motor, but rather an increase of vibrations in the machine, oscillations in the stator current, changes in temperature, etc. In case the fault persists and the induction motor continues to operate, the rotor will probably have more broken bars and symptoms of faults multiply. This makes the detection of the first broken bar a crucial priority to avoid any kind of serious damage of the induction motor. Many diagnosis methods have experienced several perfections to be able to detect the faults that prevent the normal operation of the machine. Recently the monitoring of the imperfections behavior of the machines is becoming a major interest for the researchers. A variety of solutions have been proposed in order to ensure the diagnosis accuracy and reliability. among these monitoring technique, the motor current signature analysis (MCSA) method which has experienced a huge success of stator and rotor faults detection (Benbouzid, 2000; Lebaroud and Medoued, 2013). The signal processing techniques such as wavelets (Ordaz-Moreno et al., 2008) and wavelet packet decomposition in which we can pull the signatures of faults from the stator current (Ye, Wu, & Zargari, 2000)