LiteDiagNet: Enhancing noise-resistant fault diagnosis in rotating machinery via IoT-Based cloud-edge collaboration

Rapid advances in the intelligent Internet of Things (IoT) have significantly enhanced sensor technology and edge computing across various monitoring systems. This study focuses on the seamless integration of intelligent IoT with vibration sensor technology and edge computing to effectively detect faults in rotating machinery amidst noisy environments. We introduce an innovative framework for fault diagnosis through cloud-edge collaboration, aimed at broadening the applications of intelligent IoT. Central to our approach is LiteDiagNet, a novel, lightweight Convolutional Neural Network (CNN) tailored to identify bearing faults in noise-affected signals. LiteDiagNet features a cutting-edge architecture with base blocks and a distinctive “LiteBlock” that employs multiscale, depth-wise convolutions to significantly improve performance. We evaluated our model’s efficacy against several lightweight networks using public datasets and verified its robustness in real-world scenarios by simulating two types of noise at five different intensity levels. The results demonstrate that LiteDiagNet achieves superior accuracy with fewer parameters and reduced computational demands, thereby enhancing efficiency. This research not only offers a groundbreaking solution for fault diagnosis in rotating machinery but also advances the intelligent and efficient application of industrial IoT technologies.