EffiMultiOrthoBearNet: An Efficient Lightweight Architecture for Bearing Fault Diagnosis

Abstract

Amidst the advent of Industry 4.0 and the rapid advancements in smart manufacturing, the imperative for developing resource-efficient condition monitoring and fault prediction technologies tailored for industrial equipment in resource-limited settings has become increasingly evident. This study puts forward EffiMultiOrthoBearNet, an innovative, lightweight, deep learning model specifically designed for the accurate identification and classification of bearing faults. Central to EffiMultiOrthoBearNet’s architecture is the integration of multi-scale convolutional layers and orthogonal attention mechanisms—key innovations that significantly enhance the model’s performance. Leveraging advanced feature extraction capabilities, EffiMultiOrthoBearNet meticulously processes Continuous Wavelet Transform (CWT) images from the CWRU dataset, ensuring the precise delineation of essential bearing signal traits through its multi-scale and attention-enhanced mechanisms. Optimized for supreme operational efficiency in resource-deprived environments, EffiMultiOrthoBearNet achieves unmatched classification accuracy—up to 100% under ideal circumstances and consistently above 90% amidst significant noise and operational complexities. Demonstrating remarkable adaptability and efficiency, EffiMultiOrthoBearNet provides a pioneering and practical fault diagnosis solution for industrial machinery across a wide range of application scenarios, even under stringent resource limitations.