Abstract

Fault Tolerance (FT) enables system to continue operating despite in the event of failures. Therefore, FT serves as a backup component or procedure that can immediately play its role to minimize any service lost. FT exists in many forms, where it can either be in the software form or hardware form or both hardware and software form. Fault Tolerance is an umbrella term for fault detection, fault isolation, fault identification and fault solving. To better visualize the fault detection and isolation process, a two wheel robot is used in this study to represent the complex system. The aim of this research is to construct and design a Fault Tolerance algorithm considered to speed up the fault isolation procedure and it might identify multiple fault with the same static fault signature. The Finite State Machine (FSM) model, a wide library of reusable model for the fault tolerant is used in this study to solve the fault in actuator or in the sensor by resetting and adjusting it to the correct position. Using the system sensors or actuators, the technique used is able to recognize the fault from its data. This FSM method is capable to avoid, replace, reset and recover any possible faults occurred in the system, offering an innovative solution to identify and solve a fault immediately. Keywords: Kalman filter; Artificial Neural Network; Fault tolerant; Fault detection; Fault Isolation

Highlights

  • Fault tolerance (FT) is a subsystem of the control system, which serves as a condition solution to solve the fault or failure in the system

  • We have presented an FT algorithm to construct a set of a fault solution by decomposition of the fault tolerant algorithm which is intended to be integrated into real-time environment

  • The FT system is capable to avoid, replace, reset and recover any possible fault occurred in the system

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Summary

Introduction

Fault tolerance (FT) is a subsystem of the control system, which serves as a condition solution to solve the fault or failure (hardware or software) in the system. Most researches focused on how to identify the system fault, including failure of the actuator and sensor as well as the error of the system. Researchers have proposed various solutions to the problem including (Magni, Scattolini, & Rossi, 2000) the Finite State Machine (FSM) method to identify the type of fault, (Apley, Shi, & Arbor, n.d., 1998) the GLRT mathematical method to identify the fault, (Bouhouche, Lahreche, Ziani, & Bast, 2005) artificial neural network, (Lee, Alena, & Robinson, 2005) fault decision method, (Bai, Dick, Dinda, & Chou, 2011) Fault-Aware Code Transformation. The fault that has been identified would require human to take action to fix the system

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