Academic and industrial studies on smart systems, which have entered all areas of our lives, continue to their rapid developments along with gaining momentum with Industry 4.0. Especially, many of the different production devices with their molding, printing, shaping, and cutting capabilities have a certain level of automation. They work together with the material information they process, and the compatibility with other machines to make the whole production system work effectively and properly. While the abundance of data acquired is an important source for better analytics, obtaining information for business purposes from this data and helping decision support systems is the most important task expected from Information Technology and Systems in the organization. In this paper, we propose an FPGA-based edge information infrastructure to evaluate critical data from the production devices, distributed sensors, and other ISs in any industrial environment to increase the utilization and performance of the total machinery. This study helps the predictive maintenance decision for a sample plastic injection molding device according to our industrial scenario. A sample data set downloaded from the Internet with the factors like speed, vibration, and the temperature was used. An FPGA (Field Programmable Gate Array) design that will run the necessary ML algorithms with the sensor data and existing information system inputs (ERP, MES) has been carried out by using Xilinx Design Tools and Vitis IDE 2020.2. In this study, the ANFIS (Adaptive Network-Based Fuzzy Inference System) system, which is an approach consisting of the integration of artificial neural networks and Fuzzy Logic, has been chosen as an Artificial Intelligence application. The estimation results obtained were evaluated over the accuracy rates achieved in similar studies in the literature.

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Importance of Edge Computing in Critical Manufacturing Systems: FPGA Implementation

Applications related with WSNs may include thousands of separate sensor nodes, production and control data for different industrial sectors. It is important to manage these applications, monitor the network and reprogram the nodes to avoid operational problems. In this study, we propose a smart wireless sensor network using a reconfigurable embedded system of Field-Programmable Gate Arrays (FPGAs) with a soft-core processor. This processor can be programmed dynamically and synthesized to implement the preprocessing of sensed data by ensemble Hybrid Neuro-Fuzzy algorithms such as Adaptive Neuro-Fuzzy Inference System (ANFIS). The first part of the proposed work is based on Matlab software to develop and train the ANFIS algorithm. Two different types of data sets (temperature and humidity) downloaded from Internet have been used in order to make a comparison between the Matlab Toolbox and modified ANFIS algorithm with momentum factor. The results obtained in this study have shown that the modified ANFIS algorithm is the convenient choice in terms of speed, accuracy.

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Khazal Ahmed

Papers

ANFIS Analysis of Wireless Sensor Data with FPGA