Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.
This study proposes a novel method of partial discharge (PD) pattern recognition based on the Hilbert-Huang transform (HHT) with fractal feature enhancement. First, this study establishes three common defect types with one blank sample of 25 kV cross-linked polyethylene (XLPE) power cable joints and uses a commercial acoustic emission sensor to measure the acoustic signals caused by the PD phenomenon. The HHT can represent instantaneous frequency components through empirical mode decomposition, and then transform to a 3D Hilbert energy spectrum. Finally, this study extracts the fractal theory feature parameters from the 3D energy spectrum by using a neural network for PD recognition. To demonstrate the effectiveness of the proposed method, this study investigates its identification ability using 120 sets of field-tested PD patterns generated by XLPE power cable joints. Unlike the fractal features extracted from traditional 3D PD images, the proposed method can separate different defect types easily and shows good tolerance to random noise.
This study develops a condition monitoring system, which includes operating condition monitoring (OCM) and fault diagnosis analysis (FDA). The OCM uses a vibration detection approach based on the ISO 10816-1 and NEMA MG-1 international standards, and the FDA uses a vibration-electrical hybrid approach based on various indices. The system can acquire real-time vibration and electrical signals. Once an abnormal vibration has been detected by using OCM, the FDA is applied to classify the type of faults. Laboratory results indicate that the OCM can successfully diagnose induction motors healthy condition, and FDA can classify the various damages stator fault, rotor fault, bearing fault and eccentric fault. The FDA with the hybrid approach is more reliable than the traditional approach using electrical detection alone. The proposed condition monitoring system can provide simple and clear maintenance information to improve the reliability of motor operations.
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