Bearing Fault Diagnosis in Induction Motors Using Low-Cost Triaxial ADXL355 Accelerometer and a Hybrid CWT-DCNN-LSTM Model

Abstract

This paper presents a novel approach for bearing fault diagnosis in induction motor utilizing an improved hybrid Continuous Wavelet Transform-Deep Convolutional Neural Network-Long Short-Term Memory (CWT-DCNN-LSTM) model. The vibration data, recorded using an low-cost ADXL355 accelerometer, was preprocessed by converting the one-dimensional (1D) signals into two-dimensional (2D) images using Continuous Wavelet Transform (CWT). The dataset, comprising 13 classes with varying fault conditions, was segmented and shuffled before model training. Three datasets, corresponding to different load conditions (100W, 200W, and 300W), were used to evaluate the model’s performance. Experimental results demonstrated high training accuracy of 100% and validation accuracies of 96.43%, 97.47%, and 95.06% for the 100W, 200W, and 300W load conditions, respectively. Validation losses were recorded at 12.33%, 9.81%, and 20.33% for the respective loads. Furthermore, performance results using accuracy, sensitivity, specificity, balanced accuracy and geometric mean were computed for all three load conditions. The results indicate the robustness and effectiveness of the proposed CWT-DCNN-LSTM model for bearing fault diagnosis of induction motor using low-cost ADXL335 accelerometer, highlighting its potential for real-world industrial applications.