Rotating Machinery Fault Prediction System

  The functions of witcloud cloud platform include: access configuration of vibration sensors and edge computing gateways on rotating devices (including subject configuration and channel configuration), remote information configuration and device firmware upgrade, alarm configuration, journal recording and viewing, uploading of multi-layer edge computing gateways, database backup and retrieval, and managing and controlling the influxdb database;
  The powerful system architecture of witcloud cloud platform makes it scalable, capable of accessing a large number of devices, and capable of millions of concurrency. It can easily access more than 1 million rotating devices, and can support the access capacity of more than 100000 new devices every year.

  SaaS service cloud platform for rotating equipment fault prediction provides users with platform functions such as status monitoring, fault analysis, scene monitoring, fault management, operation and maintenance management, equipment management, customer management and permission management, as well as platform data reading interface function. Through big data analysis tools, SaaS business cloud platform provides professional users with equipment operation status analysis, early warning fault analysis, and forms professional industrial equipment health diagnosis reports and maintenance suggestions. Fault prediction algorithm model：Firstly, the system collects the vibration characteristics and load temperature characteristics of rotating equipment through the high-frequency vibration sensor and platinum resistance of the Internet of things, and forms a vibration characteristic sample library; Then, the intelligent algorithm (machine learning algorithm library) is used to carry out machine self-learning on these original data samples, generate the corresponding fault prediction algorithm model, and form the fault prediction model library; The fault prediction algorithm model is used to analyze and process the real-time collected data to realize the fault prediction and diagnosis of rotating equipment such as imbalance, misalignment, looseness, gear wear, bearing aging and so on.