Fault Diagnosis and Analysis of IoT Motor Bearings Based on Software Definition

This paper aims to explore an innovative motor bearing fault diagnosis technology that deeply integrates the advantages of the Internet of Things (IoT) and software-defined technology. Today, with the continuous advancement of industrial intelligence, the accuracy and real-time fault diagnosis of motor bearings, as the core components of mechanical equipment, are particularly important. This paper proposes a software-defined IoT motor-bearing fault diagnosis model, which utilizes IoT technology to collect motor-bearing operation data and combines the flexibility of software-defined technology for fault diagnosis. In terms of methodology, a series of steps including dataset partitioning, model training, validation, testing, and result analysis were used to systematically evaluate the proposed fault diagnosis model. The outstanding advantage of this innovative model lies in its full utilization of the flexibility of software-defined technology and the extensive connectivity of IoT technology, achieving real-time monitoring and efficient analysis of the operating status of motor bearings. By dynamically adjusting the diagnostic algorithm and data processing flow, the model can adapt to the fault diagnosis requirements under different working conditions, improving the accuracy and adaptability of diagnosis. In addition, its powerful data processing capabilities ensure real-time fault diagnosis, providing strong support for timely maintenance measures, avoiding equipment damage and production interruptions.