Fault Diagnosis in Dynamic Systems Using Distributed Neural Networks

Abstract

This paper presents a novel approach for Fault detection and diagnosis (FDD) using distributed Neural Networks (DNN) to enhance system performance. FDD plays an important role in industrial applications, to guarantee system performance by detecting and analyzing faults to prevent system failures. Various approaches have been adopted for FDD, among which Artificial Intelligence (AI) has shown promising results. Artificial Neural Networks (ANN), a key AI approach, are extensively utilized nowadays in FDD to monitor system efficiency. This paper introduces a DNN approach that determines the model structure and estimates the results using the Back Propagation (BP) Algorithm. The DNN where used in this work not only to identify multiple faults for DC motors (e.g., broken bar, dynamic strangeness… etc.) but also to determine fault type and location inside a distributed drive network. Several tests of DC engine faults were conducted to assess the execution of the proposed approach. The results revealed a significant improvement in system performance using the proposed approach compared to the multilayer perceptron (MLP) method. The proposed approach enhanced the system's stability and performance by reducing faults from the default value.