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 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.

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

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This study Development a high-speed data acquisition (DAQ) device by using AD9226 analogto-digital converters, a field programmable gate array, and an ARM Cortex-A8 microprocessor for a selfdesigned synchronous 6-channel high-speed DAQ card that was able to transmit data to a computer through its network interface. Its cost was approximately 10% that of a commercial model, the National Instruments PXI-5105, and thus overcame the prohibitively high cost of commercial DAQ cards. A high-frequency current transformer (HFCT) was used to measure three types of typical partial discharge (PD) in self-made models to compare the performance of the self-designed DAQ card and that of the National Instruments PXI-5105. The HFCT signals were converted into three-dimensional PD patterns, and mean discharge was chosen as the feature to be extracted for the application of extension theory in the recognition of discharge models. The results revealed that the self-designed DAQ card was comparable to the commercial model in the recognition of high-frequency PD signals. Given the high price of commercial high-speed DAQ devices, the self-designed DAQ card was deemed to have considerable advantages in cost and expandability.INDEX TERMS Data acquisition card, extension, feature extraction, partial discharge.