# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
## 1 Introduction  
Induction motors are among the most important components for driving DC motors, rectifiers and DC motor systems, and industrial equipment. However, plant shutdowns because of unexpected induction motor faults cause significant economic losses. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early [1].  
Fault diagnosis involves detecting faults in the system and classifying them. Fault diagnosis methods are broadly classified into model-based methods and non-model-based methods. In model-based methods, system faults are diagnosed using a mathematical model of the system, but it is not easy to obtain an accurate mathematical model owing to system nonlinearity. Non-model-based methods include fault diagnosis based on measurements, experience, or physical reviews of the target system, as well as thresholding, expert system techniques, and neural networks [2].  
Faults in an induction motor are diagnosed mainly by using characteristic motor signals, such as vibration signals, motor currents [3; 4; 5; 6; 7; 8; 9], acoustic signals [10], and thermal images [11]. In the method that employs vibration signals [3; 4; 5], the vibration signals generated by an induction motor are measured, and fault diagnosis is performed by frequency analysis of the measured signals. In the method that employs motor current [6; 7; 8], a fixed magnetic current is obtained, and fault diagnosis is performed by frequency analysis of the measured signals. In the method that employs acoustic signals, the acoustic signals generated by an induction motor are measured, and fault diagnosis is performed by frequency analysis of the measured signals. In the method that employs thermal images, thermal images of an induction motor are acquired, and fault diagnosis is performed using features extracted from the images. [PERSON] [1] proposed a fault diagnosis method for induction motors based on discrete wavelet transform and ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters. This method is composed of a data preprocessing part by frequency analysis of vibrationsignals and a fault classification part for fault isolation by using ART2 NN. However, this method requires data conversion from the time domain into the frequency domain.  
In recent years, NN (neural network) models have been studied extensively for application to fault diagnosis [12; 13; 14]. The structure of these models is suitable for representing unknown nonlinear functions in a general manner. Therefore, NN models can be used as a powerful tool for solving nonlinear problems. However, a neural network model with perfect connection layers cannot learn invariant properties. Consequently, it cannot be learnt by extracting effective features from periodic vibration signals [15].  
In this study, a CNN (convolutional neural network) model with local connectivity is used for fault diagnosis. The vibration signals of three states, namely, normal, rotor fault, and bearing fault, are obtained from an induction motor and used for fault classification. In addition, a GUI (graphical user interface) is implemented to enable users to diagnose induction motor faults efficiently and intuitively. The performance of the proposed fault diagnosis method is verified through an experiment and a simulation.