Design of an Improved Model for Gear Fault Diagnosis Using Acoustic Data and EfficientNet-Based Deep Learning Process

Identifying and categorising gear faults forms an important aspect in predictive maintenance and industrial safety. Traditional methods of fault detection, such as vibration-based analysis, are restricted in terms of sensor placement, high sensitivity to environmental noise, and sheer incapacity to identify subtle gear anomalies. To overcome these challenges, the present study employs acoustic data for gear fault diagnosis, transforming temporal sound pressure signals into image representations. Hence, this proposed method gives a systematic analysis of various gear conditions, including cracks, misalignment (under load and no-load conditions), broken teeth, and normal operational conditions, under varying RPM and load conditions. The methodology consists of converting acoustic time-series data to two-dimensional arrays and normalizing them to 8-bit grayscale images, after which the data are categorized based on types of fault. Data diversity is enhanced on these augmented images through image data augmentation using resizing, rotation, flipping, color jittering, and normalization. It is then used to train deep learning models, EfficientNetB0 and EfficientNetB3 that are superior on feature-extraction and computational efficiency. The comparative analysis indicates that EfficientNetB3 outperforms EfficientNetB0 based on all four metrics of accuracy, precision, recall, and overall classification performance. Model validation is conducted using k-fold cross Validation to ensure robustness and generalizability. This research proves that acoustic-based fault analysis combined with advanced deep learning models achieves capture of efficiency, compared to conventional vibration-based diagnostics. The proposed method improves early fault detection, provides an accurate classification coupled with a non-intrusive, scalable solution for industrial gear health monitoring and thus takes a step forward toward advancement in predictive maintenance strategies in mechanical systems.