A method to detect broken bars in induction machine using pattern recognition techniques

Abstract

In this paper, a pattern recognition (PR) method is used to provide the tracking and the diagnosis of a system. First of all, from measurements carried out on the system, features are extracted from current and voltage measurements without any other sensors. These features are used to build up a pattern vector, which is considered as the system signature. Then, a feature selection method is applied in order to select the most relevant features, which define the representation space. The decision phase is based on the "k-nearest neighbors" (knn) rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states. The appearance of a new operating mode is taken into account in order to enrich the initial knowledge base and thus to improve the diagnosis. This approach is illustrated on asynchronous motor of 5.5 kW with squirrel cage, in order to detect broken bars under any load level. The experimental results prove the efficiency of PR methods in condition monitoring of electrical machines.

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

“…[1][2]3,4,5,6,7,8,9,10,11,12,13,14 A widely studied method for motor fault detection is frequency spectrum analysis. 4,[6][7]8,9,11,13 In, 5 the effect of broken bars on the air-gap torque was used as a fault indicator.…”

“…The data obtained from these methods were manually investigated to determine the motor operating condition. In, 14 a broken bar monitoring method based on pattern recognition was presented. This method was used to detect faulty motors even at no-load condition.…”

“…Thus, significant current disturbance might be found for this particular motor even at no-load condition. Additionally, the method of 14 requires a new set of system signatures for each motor.…”

Image Processing based Classifier for Detection and Diagnosis of Induction Motor Stator Fault

“…Therefore, fully automatic pattern recognition methods are required to identify induction motor stator fault. Thus some artificial intelligence tools have been introduced (Nejjari et al, 2000) (Haji et al, 2001) (Ondel et al, 2006) . However, many of these tools require a prior identification of the system, and only then they are able to identify some faulty situation.…”

Fuzzy Pattern Recognition Based Fault Diagnosis

“…,x kq ] T , called the pattern vector or feature vector, where x kj the j th characteristic (feature) associated with observation k: temperature, pressure, flow, sound noise frequency, etc. and q the pattern vector length [8][9][10][11]. Fuzzy logic concept is included to better manage uncertainty and make useful quantification of hard attributes [12][13][14][15][16].…”