Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Abstract

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

“…Moreover, the model effectively classified the faults with higher accuracy up to 100% compared to the models with individual time or frequency features. In [99], authors have used CNN to diagnose the stator winding faults of induction motors. ey have normalized the raw current data and then converted it into a three-dimensional matrix.…”

“…FFT was used to convert vibration data into frequency domain features. Experimental results Rolling bearing fault classification e method effectively classified the fault owing to the 2D-images of the data and regularization Raw current signatures CNN [99] Stator winding fault detection e method can effectively detect stator winding faults from raw current data without any preprocessing demonstrated that the model predicts the conditions of bearing better with frequency domain features. Xiao et al [105] have used deep LSTM to classify various motor faults using vibration data.…”

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

“…The most of DNN-based applications in diagnostic processes are currently associated with the detection of mechanical defects based on a vibration signal [21]. The input data of these systems are obtained as a result of the diagnostic signal analysis [10,21], and can also come directly from the measured signal [20]. Among the currently used deep learning network structures, the most popular are the convolutional neural networks (CNN) [11,20] and autoencoders [22].…”

“…The issue of the direct analysis of signals measured without using analytical methods is currently implemented with application of DNN [20][21][22][23]. DNNs are distinguished by a much more extensive structure compared to the classical shallow NNs.…”

“…The input data of these systems are obtained as a result of the diagnostic signal analysis [10,21], and can also come directly from the measured signal [20]. Among the currently used deep learning network structures, the most popular are the convolutional neural networks (CNN) [11,20] and autoencoders [22]. Apart from these, other techniques used in this research area encompass, for example., deep belief NNs [23], generative adversarial NNs [24] or long short-term memory structures [25,26].…”