In this work, Self Organizing Map (SOM) is used in order to detect and classify the broken rotor bars and misalignment type mechanical faults that often occur in induction motors which are widely used in industry. The feature vector samples are extracted from the sampled line current of motors with fault and healthy one. These samples are the poles of the AR model which is obtained from the spe...

In this paper, we propose an off-the-grid compressive sensing based method to detect brokenbar fault in squirrel-cage induction motors. To validate our method, we first build a dynamic model of squirrel-cage induction motor using multi-loop equivalent circuit to simulate motor current under fault conditions. We then develop an off-the-grid compressive sensing algorithm to extract the fault char...

This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used the...

Natural selection theory suggests that there should be fitness consequences for animals that possess morphological abnormalities such as missing or deformed appendages and other structures. As these characters are expected to be detrimental, morphological abnormalities could be considered ‘‘handicaps’’. However, little information exists about the nature, prevalence or consequences of naturally...

Induction motors are prime component in the industries. Hence, condition monitoring and fault diagnosis of induction motor important to avoid shutdowns unplanned maintenance. A technique based on time-domain grayscale current signal imaging (TDGCI) convolutional neural network (CNN) is proposed for intelligent detection broken rotor bar an motor. The standard dataset made available by Aline Ell...

Detection of squirrel cage induction motor (SCIM) broken bars has long been an important but difficult job in the detection area of motor faults. Early detection of this abnormality in the motor would help to avoid costly breakdowns. A new detection method based on particle swarm optimization (PSO) is presented in this paper. Stator current in an induction motor will be measured and characteris...

When an induction motor is running at stable speed and low slip, the fault signal of motor’s broken bar faults are easily submerged by power frequency (50 Hz) signal. Thus, it difficult to extract characteristics. The left-side harmonic component representing characteristics can be distinguished from owing V-shaped trajectory in time-frequency (t-f) domain during startup. This article proposed ...

This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and th...