Intelligent Fault Diagnosis System Based on Vibration Signal Edge Computing

With the advent of 5G, the amount of data generated by the Internet of Things (IoT) will explode, and the amount of information processed by prognostics health management (PHM) will also increase dramatically. The traditional cloud computing framework will generate requirements of higher network bandwidth and real-time data processing. This paper studies the system requirement analysis, system structure and function in detail, and an intelligent fault diagnosis system based on the edge computing of vibration signals is proposed for the equipment faults of rotating components such as gears and bearings, and this paper studies the system requirement analysis, system structure and function in detail. The edge of the system implements online diagnostics based on the real-time equipment data and the downloaded cloud model, and preprocesses the vibration signal at the edge. The cloud uses the data reported by the edge to train the model. In this paper, we use wavelet packet transform, Fisher discrimination criterion, combined with the edge of the Support Vector Machine (SVM) and Relevance Vector Machine (RVM) proved in this paper, computing framework can be real-time processing of sensor data, reduces network bandwidth resource consumption and latency, and can continuously update the computing model with the latest data sets. The intelligent fault diagnosis system based on vibration signal edge computing has application potential in highly time-sensitive fault diagnosis of rotating components as well as in vibration signal monitoring systems with large data volumes.