Fusing Spectral Correlation Density Imaging with Deep Learning for Intelligent Fault Diagnosis in Rotating Machinery
This provides a robust, cost-effective solution for condition monitoring in industrial settings, though it's incremental as it applies existing deep learning methods to a new signal processing approach.
This study tackled bearing fault diagnosis in rotating machinery by combining Spectral Correlation Density imaging with deep learning, achieving classification accuracies up to 96.58% on a public dataset with varying load conditions.
Bearing fault diagnosis in rotating machinery is critical for ensuring operational reliability, therefore early fault detection is essential to avoid catastrophic failures and expensive emergency repairs. Traditional methods like Fast Fourier Transform (FFT) often fail to capture the complex, non-stationary nature of vibration signals. This study leverages the cyclostationary properties of vibration data through Spectral Correlation Density (SCD) images to enhance fault detection and apply deep learning for classification. Using a publicly available dataset with bearing faults seeded in two distinct housings (A and B) under varying load conditions (0 Nm, 2 Nm, 4 Nm), we processed vibration signals into 2D SCD images to reveal fault-specific periodicities, such as broadband spectra (2000--8000 Hz) for larger faults. Three convolutional neural network (CNN) models, Custom CNN, ResNet152V2, and EfficientNetB0, were developed to classify seven bearing conditions. The custom CNN achieved the highest accuracies of 96.58\% and 94.95\% on Housing A and B, respectively, followed by ResNet152V2 at 96.49\% and 95.35\%, and EfficientNetB0 at 94.16\% and 91.65\%, respectively. The models' high accuracies across different housings demonstrate a robust solution suitable for cost-effective condition monitoring deployable near sensing platforms, contributing to applied machine learning for edge intelligence and showcasing effective signal processing strategies for handling complex, potentially large-scale vibration data.