Enabling IIoT connectivity to perform remote monitoring and predictive maintenance, and transform your business
In order to achieve optimized results for predictive maintenance, it is critical to leverage edge computing capabilities to preprocess data from a diverse set of data sources. This data is acquired through a variety of sensors added to key components. Managers can implement appropriate measures in good time, be prepared for any event, and autonomously perform maintenance before machine failure truly occurs. Find out how to address the challenges in performing diverse data acquisition and deploying edge intelligence for predictive maintenance.
Adding more sensors nearby key components to acquire big data can increase predictive accuracy. However, data acquisition gets complicated because of the large number of different protocols and interfaces used by the different sensors.
Connecting different sensors with different interfaces and protocols
Separating controlling and connectivity
Sending all raw sensor data to the cloud is the best approach to do predictive analysis. However, deploying edge computing in multiple sites for data preprocessing saves you more on network bandwidth and allows you to anticipate and preempt machine failure. What if we can simplify your large-scale edge computing deployment?
Deploying edge computing at each site
Easy distribution of cloud technology to the edge in large-scale applications
Traditional machine tool builders are now willing to invest in new IIoT trends so that they can add more value to their products and improve the quality of their post-sales management and services. The UC-8100 Series embedded computer collects proprietary machine status data from different brands of PLCs (e.g., Mitsubishi, Delta, and Allen-Bradley), sends the data to the backstage control server, and displays the data on a dashboard remotely and locally.