Fault diagnosis of electronic systems using intelligent techniques: a review

Abstract

In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of fault diagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the industrial acceptance of these techniques, particularly in cost-sensitive areas, has not been high. This paper reviews this research, primarily covering rule-based, model-based, and case-based approaches and applications. Future research directions are finally examined, with a concentration on issues, which may lead to a greater acceptance of automated diagnosis.

Knowledge-Driven Board-Level Functional Fault Diagnosis

“…Rule-based diagnosis methods take the form "IF syndrome(s), THEN fault(s)" to locate a fault [23], as shown in Figure 1.3. Hundreds or thousands of rules may be required to represent all the relevant knowledge for the system under diagnosis.…”

“…A number of board-level fault diagnosis techniques have been presented in the literature [9,12,15,[23][24][25][26][27][28][29].…”

“…More reasoning-based diagnosis systems can be found in the literature [23,25,33,[39][40][41], where machine learning techniques have been widely used.…”

“…Reasoning-based methods are promising because a detailed system model is not needed to construct the diagnosis system [2,23]. The diagnosis engine is incrementally built based on the database of successfully repaired faulty boards.…”

“…Reasoning-based methods have recently become popular since they overcome the knowledge-acquisition bottleneck during volume production and they can automatically generate an intelligent diagnostic system from existing resources [23,78]. However, knowledge acquisition is a major problem for a reasoning-based method at the initial product ramp-up stage.…”