PHM Solution of Blower
Problems In Blower equipment
In the large-scale fan rotating equipment industry, the industrial chain consists of large-scale Blower manufacturers, OEMs and end users. Generally, blower manufacturers hope to solve problems related to after-sales operation and maintenance efficiency, maintenance costs, product market application quality, and on-site health of equipment. The OEM hopes to solve 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 avoid the losses caused by the production line shutdown due to the abnormal failure, understand the operating health of the equipment in real time, avoid the losses caused by the production line shutdown due to the abnormal equipment failure, and the safety issues of abnormal equipment production.
According to the problems encountered in the fan industry, Witium launched the "blower prediction system solution", which carries out the health status analysis and early warning fault analysis of blower for the monitoring of the operation status of large blower on the user's site, forms a professional blower prediction and health diagnosis report, and gives maintenance suggestions. Witium's blower 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 fault prediction solution of large-scale fan 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 large-scale fan equipment; The edge computing gateway collects the three-phase winding temperature and lubricating oil temperature of large fan through platinum resistance to understand the operating load state of large fan. Combined with the vibration characteristics of the sensor, the intelligent mechanism model algorithm is used to analyze the sampled data, so as to predict the faults of large fan equipment 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, the business cloud platform provides professional users with operation status analysis, early warning fault analysis of large-scale fan equipment through big data analysis tools, and forms professional large-scale fan 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 large fan 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 million level concurrency. It can easily access more than 1 million large-scale fan equipment, and support the access ability of more than 100000 new large-scale fan equipment every year.
SaaS business cloud platform for large fan equipment
The SaaS business cloud platform for large-scale fan 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 authority 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 large rotary fan through high-frequency vibration sensor and platinum resistance of 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 large fan equipment such as imbalance, misalignment, looseness, gear wear, bearing aging and so on.
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 26khz
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