Predictive Maintenance

With the maturity of technologies such as IoT and big data, predictive maintenance has emerged. It can not only monitor equipment in real time, perform big data analysis, and sense equipment failures in advance; it can also provide remote services and troubleshoot hidden faults in advance, making maintenance more intelligent, operations more reliable, and costs lower. Predictive maintenance has become a general trend in the industry. At the same time, predictive maintenance technology is an important component of future smart factories and the source of a new corporate profit model - based on value-added services, which allows OEM manufacturers to extend their services to production and bring additional value to users. Therefore, Gain market competitiveness.

Figure 1. Development route of equipment maintenance

The premise of predictive maintenance is condition monitoring. Condition monitoring is the process of monitoring mechanical condition parameters (vibration, humidity, pressure, temperature, etc.) in order to identify differences that indicate the development of faults. It is a key factor in effective predictive maintenance. Condition monitoring has the unique advantage of detecting conditions that typically shorten normal life, allowing users to resolve these conditions before they develop into major failures. Condition monitoring technology is commonly used in rotating equipment, auxiliary systems and other machinery (compressors, pumps, electric motors, internal combustion engines, presses, etc.). Thanks to the interplay of PC control technology, smart sensors, networks, wireless transmission and visualization, condition monitoring can not only be carried out continuously but also regardless of geographical location.

Figure 2. Typical nodes for status detection

Germany's Beckhoff Company is the global leader in PC control technology. Its open and flexible software and hardware platform enables the realization of the idea of intelligent predictive maintenance. It has rich application experience and cases in offshore wind turbines, automobile production lines and other fields. In the Beckhoff predictive maintenance system, we choose the efficient and reliable PC controller CX5130, which is equipped with Intel Atom dual-core low-power CPU, 4G memory, and a maximum of 160G of available storage space. The unique TwinCAT3 development platform integrates Beckhoff's independently owned Condition Monitoring component. Customers can freely develop CMS software based on the CM component. The data acquisition part uses the EL3632 module, which is a 2-channel IEPE interface acquisition module with 16-bit sampling accuracy and a maximum sampling frequency of 50Ksps.
For specific parameters, please refer to www.beckhoff.com

Figure 3, Germany’s Beckhoff MW-class wind turbine CMS condition monitoring system

Figure 4: Introduction to the German Beckhoff condition monitoring CM library

Advantages of Germany's Beckhoff condition monitoring system:
1. Significantly reduce hardware costs
2. Use Beckhoff’s existing CM library to greatly shorten the development cycle
3. Based on EtherCAT, flexible configuration, simple upgrade, and convenient management
4. Easy to integrate into existing systems
5. Mature applications in wind power generation, automobiles and other industries