Solution of Reducer Fault Prediction System
Problems In Reducer equipment
In the reducer rotating equipment industry, the industrial chain consists of reducer manufacturers, OEMs and end users. Generally, reducer manufacturers hope to solve problems related to after-sales operation and maintenance efficiency, maintenance costs, and product market application performance. The reducer OEM hopes to solve the problems related to the maintenance cost of the core components of the host equipment and the operational health of the on-site host equipment. End users hope to solve problems related to downtime and production, real-time operation of equipment, and potential dangers to personal safety caused by abnormal equipment production.
According to the problems encountered in the reducer industry,Witium launched the "reducer fault prediction system solution", which carries out the health status analysis and early warning fault analysis of the reducer and host equipment for the monitoring of the reducer operation status on the user's site, forms a professional reducer fault prediction and health diagnosis report, and gives maintenance suggestions. Witium 's reducer fault prediction solution brings great commercial value to users:
Improve operation and maintenance efficiency and reduce equipment maintenance cost;
Reduce abnormal faults and equipment damage cost;
Predict equipment deterioration and reduce the cost of spare parts;
Reduce failure probability and avoid production line shutdown loss;
Assess equipment risks and prevent potential personal safety hazards;
Apply data analysis to improve equipment quality and process;
The reducer fault prediction solution uses the high-frequency vibration sensor of the Internet of things to collect the vibration waveform samples and surface temperature of the equipment, and generate the vibration characteristic samples of the reducer equipment; The edge computing gateway collects the three-phase winding temperature and lubricating oil temperature of the reducer through the platinum resistance to understand the operating load state of the reducer. Combined with the vibration characteristics of the sensor, the intelligent mechanism model algorithm is used to analyze the big data of the sampled data, so as to realize the prediction of reducer equipment faults such as imbalance, misalignment, looseness, gear wear and bearing aging; Then upload the data samples and analysis learning results to the witcloud Internet of things data cloud platform through mqtt; The SaaS business cloud platform of the solution provides users with status monitoring, scene monitoring, fault management, operation and maintenance management, equipment management, customer management, authority management and other functions; It also provides data interface function to facilitate users to import equipment status into intelligent factory operation and maintenance platform system. At the same time, through big data analysis tools, the business cloud platform provides professional users with operation status analysis and early warning fault analysis of reducer equipment, and forms professional reducer equipment health diagnosis reports and maintenance suggestions.
Witcloud Internet of things data cloud platform
The functions of witcloud cloud platform include: access configuration of vibration sensor and edge computing gateway on reducer equipment (including subject configuration and channel configuration), remote information configuration and equipment firmware upgrade, multiple alarm modes, diary operation and maintenance management, Cloud Architecture on multi-layer edge computing gateway, database backup and retrieval, and management and control of influxdb database.
The powerful system architecture of the witcloud cloud platform has scalability, the ability to access a large number of devices and the million level concurrency ability. It can easily access more than 1 million reducer devices and support the access ability of more than 100000 reducer devices each year.
SaaS business cloud platform for reducer equipment
The SaaS service cloud platform for reducer 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 reducer equipment through Internet of things high frequency vibration sensor and platinum resistance, 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 reducer equipment such as imbalance, misalignment, looseness, gear wear and bearing aging.
Solution Web Side Software Diagram
Solution Mobile Software Diagram
High frequency vibration sensor
MEMS vibration sensor
Medium and high frequency vibration monitoring
The sampling frequency is up to 20khz
Magnetic suction easy installation
Edge computing gateway
Edge AI Calculation
Several vibrating channels
Multiple wireless Transmit
Embedded algorithm model
Number of vibration channels
1-6 Optional Channels
1 Pt100 Breakage Detection
Three winding temperature
3 Pt100 Breakage Detection
1 * power light, 1 * sensor communication light, 1 * network status light
Online update, remote update
Solution Hardware Diagram
Site Map Of Successful Cases