Neural Net-Based Industrial Control System Fault Detection and Diagnosis

Abstract

Industrial control systems (ICS) are essential to the reliable and effective operation of complicated operations. Faults, however, can result in disruptions, malfunctions, and jeopardised security. Conventional failure detection and diagnosis (FDD) techniques in industrial control systems (ICS) frequently depend on rule-based methodologies, which may make it difficult to adjust to dynamic and nonlinear system behaviours. In this paper, a new method for FDD in industrial control systems based on neural network approaches is presented.

The suggested approach makes use of artificial neural networks (ANNs) capacity to recognise abnormal patterns linked to different failure scenarios and to capture complex relationships within the dynamics of the system. The purpose of the multi-layered neural network architecture is to process sensor data and distinguish between optimal and unsatisfactory operating conditions. Using historical data that includes both fault-free and fault-ridden cases, the neural network is trained, allowing the model to learn the intricate relationship between input variables and fault categories.  The created neural network-based FDD system's performance is assessed using in-depth simulations and experiments on actual industrial processes. The outcomes show how well the method works to precisely identify and diagnose errors even when noise and uncertainty are present. Moreover, the suggested approach demonstrates a strong degree of flexibility for various industrial uses, indicating its potential for broad adoption. By utilising neural networks, this research advances fault detection and diagnostic methods in industrial control systems (ICS) and opens the door to increased operational safety, less downtime, and improved system reliability.