Software-Defined Networking for IoT-Based Motor Bearing Fault Diagnosis

Abstract

With the acceleration of industrial intelligence, motor bearings, as the core components of mechanical equipment, are the key to ensuring stable industrial production through real-time and accurate fault diagnosis. This article aims to address the issues of poor stability, weak anti-interference, and insufficient real-time performance in traditional fault diagnosis methods, and develop an innovative solution for motor bearing fault diagnosis that integrates the advantages of the Internet of Things (IoT) and Software Defined Networking (SDN). Build a software defined IoT architecture in terms of methodology, and collect motor bearing operating parameters based on IoT wide area connectivity characteristics. Relying on the centralized control advantage of SDN to enhance diagnostic flexibility, integrating CNN-LSTM model to strengthen feature extraction capability, using Z-Score normalization, 3 σ criterion, etc. to complete data preprocessing, and optimizing model parameters through grid search. The experiment was validated based on the CWRU standard dataset, and the results showed that the proposed model improved prediction accuracy by 13.5 and 10.1 percentage points respectively compared to traditional methods, increased F1 scores by 0.156 and 0.120, decreased MAE by 0.119 and 0.091, reduced inference delay by 58ms and 25ms, and exhibited good scalability in multi motor parallel scenarios. This model effectively achieves real-time monitoring and efficient diagnosis of the operating status of motor bearings, solving the core pain points of traditional methods. It can be seamlessly integrated into industrial predictive maintenance systems and is suitable for multi domain motor operation and maintenance, providing reliable technical support for industrial motor bearing fault diagnosis.