Detection of Inter-Turn Short Circuits in Induction Motors under the Start-Up Transient by Means of an Empirical Wavelet Transform and Self-Organizing Map

Abstract

Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and affects induction motors (IMs), leading to catastrophic damage. Therefore, this work proposes the use of the empirical wavelet transform to characterize the time frequency behavior of the IM combined with a self-organizing map (SOM) structure to perform an automatic detection and classification of different severities of ITSC. Since the amount of information obtained from the empirical wavelet transform is big, a genetic algorithm is implemented to select the modes that allow a reduction in the quantization error in the SOM. The proposed methodology is applied to a real IM during the start-up transient considering four different fundamental frequencies. The results prove that this technique is able to detect and classify three different fault severities regardless of the operation frequency.

Incipient Inter-Turn Short Circuit Detection in Induction Motors Using Cumulative Distribution Function and the EfficientNetv2 Model

“…According to this table, it is possible to note that the ANNs at different levels of complexity are applied to the incipient ITSC fault diagnosis. While other techniques present effectiveness in lower levels of ITSC damage with diverse levels of mechanical load [10,24,25] or under a single or no mechanical load [7,11,23,26], they apply elaborated preprocessing stages, which increase their computational complexity and could limit their implementation in an industrial process. In this regard, for example, Guedidi et al [25] investigate a SqueezeNet model, a lightweight CNN network, for detecting ITSCs in an IM, reaching high accuracy rate.…”

“…In this sense, much of the research is focused on the current signal analysis due to the direct relationship between the current behavior with the presence of a short circuit in the motor stator, giving important contributions to the motor current signal analysis (MCSA) [5,6]. According to the above, the statistical features [7] and the wavelet technique for ITSC fault detection are widely applied due to the effectiveness of the signal extraction features in the time domain [8][9][10][11]. Besides that, approaches with Fast Fourier transforms (FFTs) have been implemented to search for spectral components related to the failure [12,13].…”

“…In this work, Skowron and collaborators use the instantaneous symmetrical components of the stator current spectra (ISCA) to classify, among others, ITSC faults as minimal one-short-circuited turns (SCTs). On the other hand, Saucedo-Dorantes [11] integrate a SONN method with empirical wavelet transform and statistical indicators to diagnose an IM with various levels of ITSC. Babanezha et al [23] introduce a technique using a Probabilistic Neural Network (PNN) to estimate turn-to-turn faults in IMs, evaluating the negative sequence current to estimate up to 1 SCT.…”

Evaluation of Entropy Analysis as a Fault-Related Feature for Detecting Faults in Induction Motors and Their Kinematic Chain

“…The general weaknesses of these techniques are the processing of a high number of features, the estimation of redundant or correlated information in the feature calculation stage, the technique overfitting, the adequate selection of the mechanisms for feature extraction, the classifier configuration, and the optimal values of the techniques hyperparameters, to mention a few [21]. Additionally, a subset of the SML approaches is the Deep Learning (DL) techniques, which are schemes that take advantage of new reinforced structures based on neural networks such as Sparse Auto-encoder (SAE), Convolutional Neural Networks (CNN), Self-organizing Maps (SOM), Generative Adversarial Networks (GAN), Expandable Continuous Learning (ECL), and Transfer Learning (TL), among others [22][23][24][25][26]. These structures are capable of learning features, patterns, profiles, and clues with the aim of directly classifying the faults from the signals in the time, frequency, and time-frequency domains, or from features computed from any of these domains.…”