PLC Control Logic Error Monitoring and Prediction Using Neural Network

Abstract

This paper reviews monitoring and error prediction of PLC-program using Neural Network. In the PLC-device controlled manufacturing line, PLC-program holds place of underlying component. It becomes controlling mechanism. The level of automation in the production line relies on control mechanism practiced. In the modern manufacturing, PLC devices can handle whole production line given that structured and smart PLC-program is executed. In other words, PLC-program can manage whole process structure consisting set of procedures. We present a method to monitor PLC-program and PLC error prediction it using neural network. The neural network method being predictive in nature, it rigorously can monitor process signals from sensors, sensed during operation of PLC devices or execution of PLC-program. Subsequently, a neural network algorithm practiced for the analysis of signals. In this way, thorough monitoring of PLC-program can find possible errors from temporal parameters (e.g. Voltage, bias etc). In addition, possible alterations in program and irregularities can be minimized. That can result, easily to use in fault detection, maintenance, and decision support in manufacturing organization. Similarly, it can lessen down-time of machines and prevent possible risks.