Solution of Machine Tool Fault Prediction
Problems In Machine Tool equipment
In the industrial machine tool industry, the industrial chain is mainly composed of upstream raw materials, midstream various machine tool products and downstream application services. In this industry, the operation of various functions of machine tools and the completed operations are key factors in the production process. Therefore, in the stage of innovation and upgrading of our country's machine tool industry, machine tool manufacturers hope to improve the efficiency of after-sales operation and maintenance, reduce maintenance costs, ensure the market application performance of their products, and predict the health of customer on-site machine tools. End users hope to avoid the loss of production line shutdown due to abnormal machine tool equipment, understand the operating health of machine tool equipment in real time, effectively carry out inspection and maintenance of machine tool equipment, and shorten the troubleshooting time.
According to the problems encountered in the machine tool industry, Witium launched the "machine tool fault prediction system solution", which collects and monitors the cutting tools, motors, reducers and other units in the machine tool equipment, obtains the health status of the equipment, forecasts and alarms, forms the health diagnosis report of the machine tool equipment sub units, and gives maintenance suggestions. This set of fault prediction system solutions can bring great business 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 machine tool fault prediction solution uses the Internet of things high-frequency vibration sensor to collect the vibration waveform samples and surface temperature of machine tool equipment, and generate the vibration characteristic samples of machine tool tools and other units; The edge computing gateway collects the three-phase winding temperature and lubricating oil temperature of the machine tool through the platinum resistance to understand the operating load state of the machine tool equipment. Combined with the vibration characteristics of the sensor, the intelligent mechanism model algorithm is used to analyze the big data of the sampled data, and the roughness is detected according to the product indication, so as to realize the gradual deterioration in the cutting process of the equipment, such as tool wear, plastic deformation Fault prediction and diagnosis of poor cutting and vibration intensification; 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 machine tools and equipment through big data analysis tools, and forms professional machine tool 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 machine tool 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 machine tools and 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, so as to realize the fault prediction and diagnosis of machine tools and equipment, such as accurate prediction of tool life, process optimization of cutting conditions and tool geometry, and effective improvement of production efficiency.
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