Fault Diagnosis of Electronic Circuits Using Cellular Automata Based Pattern Classifier

Introduction

This chapter formulates fault diagnosis in electronic circuits as a pattern classification problem. The proposed pattern classification scheme employs the computing model of a special class of sparse network referred to as cellular automata (CA). A particular class of CA referred to as multiple attractor CA (MACA) has been projected as a classifier of faulty response-pattern of a circuit. The genetic algorithm (GA) is employed to synthesize the desired CA required for diagnosis of a circuit under test (CUT). The CUT is assumed to have a network of large number of circuit components partitioned into a number of sub-circuits referred to as modules. Introduction of GA significantly reduces the design overhead of the MACA based classifier that supports:

• low memory overhead for diagnosis - reduction of one to two order of magnitude of memory overhead has been achieved over that required for conventional fault dictionary based diagnosis scheme;

• excellent diagnostic resolution and low diagnostic aliasing;and

• low cost hardware of a generic fault diagnosis machine (FDM) with simple, regular, modular, and cascadable structure of CA that suits ideally for very large scale integration (VLSI) implementation.