Research Unity of Industrial Process Control (UCPI), National Engineering School of Sfax (ENIS), B.P.: W 3038 Sfax-Tunisia Aroui.tarek@yahoo.fr, Yassine.koubaa@enis.rnu.tn, ahmad.tomi@enis.rnu.tn Copyright © JES 2007 on-line : http://journal.esrgroups.org/jes/ T. Aroui Y. Koubaa A. Toumi Regular paper Application of Feedforward Neural Network for Induction Machine Rotor Faults Diagnostics using Stator Current Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues. This motivates motor monitoring, incipient fault detection and diagnosis. Non-invasive, inexpensive, and reliable fault detection techniques are often preferred by many engineers. In this paper, a feedforward neural network based fault detection system is developed for performing induction motors rotor faults detection and severity evaluation using stator current. From the motor current spectrum analysis and the broken rotor bar specific frequency components knowledge, the rotor fault signature is extracted and monitored by neural network for fault detection and classification. The proposed methodology has been experimentally tested on a 5.5Kw/3000rpm induction motor. The obtained results provide a satisfactory level of accuracy. Keywords Feedforward neural network, Diagnosis, induction motors, rotor faults, single phase stator current 1. INTRODUCTION Induction motors play a pivotal role in industry and there is a strong demand for their reliable and safe operation, and the motor can be exposed to different hostile environments, misoperations, and manufacturing defects. Internal motor faults ( e.g., short circuit of motor leads, interturn short circuits, ground faults, bearing and gearbox failures, broken rotor bar and cracked rotor end-rings), as well as external motor faults (e.g., phase failure, asymmetry of main supply and mechanical overload), are expected to happen sooner or later[1]. Furthermore, the wide variety of environments and conditions that the motors are exposed to can age the motor and make it subject to incipient faults. These incipient faults, or gradual deterioration, can lead to motor failure if left undetected. The major faults of electrical machines can broadly be classified according to the main components of a machine: stator related faults, rotor related faults, bearing related faults and other faults [2]. Early fault detection allows preventative maintenance to be scheduled for machines during scheduled downtime and prevents an extended period of downtime caused by extensive motor failure, improving the overall availability of the motor driven system. With proper system monitoring and fault detection schemes, the costs of maintaining the motors can be greatly reduced, while the availability of these machines can be significantly improved. Many engineers and researchers have focused their attention on incipient fault detection and preventive maintenance in recent years. There are invasive and noninvasive methods for machine fault detection, they can be described as [3,4]: