PHM Solution of Air Compressor
Problems In Air Compressor equipment
In recent years, air compressors have become indispensable power equipment in industrial plants. However, air compressors will inevitably generate a large amount of after-sales maintenance and troubleshooting during long-term use. At present, most companies adopt manual periodic inspection and processing methods, which have poor immediacy and low efficiency. Therefore, air compressor equipment manufacturers hope to solve problems related to after-sales operation and maintenance efficiency, maintenance costs, product market application performance, and on-site air compressor equipment health. The end user hopes to avoid the loss caused by the production line shutdown due to the abnormal failure of the air compressor equipment, understand the operating health of the equipment in real time, effectively carry out the inspection and maintenance, and shorten the troubleshooting time of the air compressor equipment.
According to the problems encountered in the air compressor industry,Witium launched the "air compressor fault prediction solution", which carries out the health status analysis and early warning fault analysis of the air compressor and host equipment for the monitoring of the operation status of the air compressor on the user's site, forms a professional air compressor fault prediction and health diagnosis report, and gives maintenance suggestions. Witium 's air compressor 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 air compressor fault prediction solution uses the Internet of things high-frequency vibration sensor to collect the equipment vibration waveform samples and surface temperature, and generate the vibration characteristic samples of the air compressor equipment; The edge computing gateway collects the three-phase winding temperature and lubricating oil temperature of the air compressor through the platinum resistance to understand the operating load state of the air compressor. 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 air compressor 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, the business cloud platform provides professional users with operation status analysis and early warning fault analysis of air compressor equipment through big data analysis tools, and forms professional air compressor 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, alarm configuration, journal recording and viewing, multi-layer edge computing gateway upload, database backup and retrieval, 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 air compressor equipment
The SaaS service cloud platform for air compressor 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 air compressor 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 algorithm 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 air compressor 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