A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

Abstract

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.

A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique

“…However, after careful evaluation, ResNet-152 was chosen as it exhibited the best performance. Its deeper architecture and skip connections enable it to capture intricate patterns and features in the data effectively [84]. Therefore, ResNet-152 was selected for the proposed method and subsequently fine-tuned for the ALL dataset.…”

Research on convolutional neural networks in motor fault diagnosis

“…Eduardo and colleagues successfully employed a frequency occurrence plot-based CNN (FOP-CNN) to diagnose motor faults using solely the motor current signature. Kevin et al [16] evaluated the diagnostic capabilities of six different CNN architectures and found that VGG19 performed the best in diagnosing broken bar faults during rotor operation in induction motors. Rahmawan et al [17] utilized CNN to detect misalignment of motor shafts.…”

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM

“…Meanwhile, in [15], the authors presents an in-tegrated design strategy focusing on preserving closed-loop functionality while leveraging active fault diagnostic and tracking control to detect early flaws. Authors in [16] advocate for the utilization of zonotopic observers and MANFIS models to ensure reliable detection of broken rotor bars (BRBs). However, [17] highlights a challenge where the fracture of two bars, situated a pole pitch apart, conceals the fault harmonics, rendering fault detection seemingly impossible.…”