Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks

In this paper, both the design and hardware of Fault Detection (FD) in Wireless Sensor Network (WSN) was implemented using FPGA NI myRIO kit, wireless temperature sensors network with small size, low cost, and low power consumption. Work data processing was performed using pattern recognition methods to detect residual generation. LabVIEW software environment was employed for system performance. In this paper. The design of the hardware circuit NI myRIO kit received temperature from the sensors. The examined system showed an ability to monitor and track any fault or fire that may occur; based on the results, if collected data is exceeded predetermined threshold, then the system is responding, a direct connection is using WIFI to process this data by LabVIEW.

A Neural-Fuzzy Approach for Fault Diagnosis of Hybrid Dynamical Systems: Demonstration on Three-Tank System

This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment.

Intelligence Decision Making of Fault Detection and Fault Tolerances Method for Industrial Robotic Manipulators

FD methods are usually based on the residual generation and analysis concept. A mathematical model is used to reproduce the dynamic behavior of the fault-free system; the deviation of the output predicted by the model from actual output measurements forms the so-called residuals. Which, when properly analyzed, provides valuable information about failure. Based on the failure an intelligent decision is taken with the help of the neuro fuzzy fault diagnosis system. The main aim of this work is the introduction of a new algorithm for robots fault detection which forms part of a proposed intelligent decision making framework for fault tolerance in robotic manipulator. In developing the model, this work explores the affects of failures in an example robot using a technique called Neuro-Fuzzy Approach. The robot components critical to fault detection are revealed using a Neuro-Fuzzy (NF) approach. To evaluate our NF based fault detection and tolerance method we performed an extensive simulation study with a Scorbot ER 5u plus robot manipulator. In this research work we considered all faults possible to occur in robot manipulator. The Scorbot ER 5u plus model was developing in robotics toolbox for MATLAB using the NF algorithms