Distributed I/O as a service: A data acquisition solution to Industry 4.0.

Data acquisition is a required step for any industrial application. Industrial automation traditionally relies on Supervisory Control and Data Acquisition (SCADA), and remote I/O is an important element of this infrastructure. The remote I/O is responsible for the data acquisition of sensors and actuators of industrial processes. The distributed I/O represents an evolution of the remote I/O to include processing and support network communication. With the adoption of recent technologies within the industry 4.0, automation solutions are facing servitization and being develop as a service. This paper describes the development of an open-source and low-cost distributed I/O as a service for industrial automation. The solution is based on a single board computer with I/O expansion modules, a microservice framework and a customized software to enable the I/O data acquisition to function in a networked, standardized, and scalable manner. Some application examples of the distributed I/O service are presented such as the I/O redundancy and historian, process control, and IIoT monitoring.

The benefits of soft sensor and multi-rate control for the implementation of Wireless Networked Control Systems.

Recent advances in wireless networking technology and the proliferation of industrial wireless sensors have led to an increasing interest in using wireless networks for closed loop control. The main advantages of Wireless Networked Control Systems (WNCSs) are the reconfigurability, easy commissioning and the possibility of installation in places where cabling is impossible. Despite these advantages, there are two main problems which must be considered for practical implementations of WNCSs. One problem is the sampling period constraint of industrial wireless sensors. This problem is related to the energy cost of the wireless transmission, since the power supply is limited, which precludes the use of these sensors in several closed-loop controls. The other technological concern in WNCS is the energy efficiency of the devices. As the sensors are powered by batteries, the lowest possible consumption is required to extend battery lifetime. As a result, there is a compromise between the sensor sampling period, the sensor battery lifetime and the required control performance for the WNCS. This paper develops a model-based soft sensor to overcome these problems and enable practical implementations of WNCSs. The goal of the soft sensor is generating virtual data allowing an actuation on the process faster than the maximum sampling period available for the wireless sensor. Experimental results have shown the soft sensor is a solution to the sampling period constraint problem of wireless sensors in control applications, enabling the application of industrial wireless sensors in WNCSs. Additionally, our results demonstrated the soft sensor potential for implementing energy efficient WNCS through the battery saving of industrial wireless sensors.