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AuthorICP DAS

ICP DAS was established in 1993 and strongly focuses on innovation and the enhancement of industrial automation technology. ICP DAS continuously endeavors to develop a comprehensive selection of products ranging from remote I/O controllers, distributed I/O modules, I/O data acquisition boards, programmable automation controllers, industrial communication modules, web-related products, motion control systems, SCADA/HMI software to automation solutions for applications critical to energy management, motion automation, smart factories, intelligent buildings, and smart cities. Our ambition is to provide a wide range of high-quality products and versatile applications, together with prompt and efficient services, that can be implemented to assist in the continued success of our clients worldwide.

Saving Water and Maintaining a Constant Temperature –ICP DAS Application Scenario for Smart Aquaculture

  Two types of remote I/O module are implanted in this scenario, including the tM-P4C4 module and the tM-DA1P1R1 module, connected to field sensors and devices. Using ZT-2550 and ZT-2551 modules, which are based on ZigBee wireless communication, allow … Continue Reading Saving Water and Maintaining a Constant Temperature –ICP DAS Application Scenario for Smart Aquaculture

Zhangbei National Wind and Solar Energy Storage and Transmission Demonstration Project (China)

A monitoring system that provides scalability, expandability and high stability is established to monitor wind power generation, solar power generation and energy storage by adopting a battery information concentrator (VP-25W1) … Continue Reading Zhangbei National Wind and Solar Energy Storage and Transmission Demonstration Project (China)

How to Increase the Efficiency and Transparency of Predictive Maintenance

ICP DAS (by Marketing Team) Predictive maintenance (PdM) techniques are designed to determine the condition of in-service equipment in order to estimate and schedule the timing of maintenance, an approach … Continue Reading How to Increase the Efficiency and Transparency of Predictive Maintenance