基于压缩词典的北京科技大学故障诊断

Chunsheng Liu, K. Chakrabarty
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引用次数: 7

摘要

我们提出了一种新的生成紧凑词典的技术,用于科学技术中的因果诊断。该方法依赖于三个紧凑字典的使用:(i) D/sub 1/,包含少量高检测概率的模式和故障的紧凑LFSR签名;(ii)基于区间的通过/失败字典D/sub 2/,用于BIST模式和检测概率相对较低的故障;(iii) D/sub 3/,包含用于清理ATPG向量和抗随机故障的紧凑LFSR签名。我们表明,D/sub 2/比最大分辨率通过/失败字典小两个数量级,提供与非压缩字典几乎相同的诊断分辨率。我们还表明,通过对D/sub 1/和D/sub 2/使用16位LFSR签名,我们在字典大小上减少了三个数量级,但在诊断分辨率上几乎没有损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compact dictionaries for fault diagnosis in BIST
We present a new technique for generating compact dictionaries for cause-effect diagnosis in BIST. This approach relies on the use of three compact dictionaries: (i) D/sub 1/, containing compacted LFSR signatures for a small number of patterns and faults with high detection probability, (ii) an interval-based pass/fail dictionary D/sub 2/ for the BIST patterns and for faults with relatively lower detection probability, and (iii) D/sub 3/, containing compacted LFSR signatures for clean-up ATPG vectors and random-resistant faults. We show that D/sub 2/, which is two orders of magnitude smaller than a maximal-resolution pass/fail dictionary, provides nearly the same diagnostic resolution as an uncompacted dictionary. We also show that by using a 16-bit LFSR signature for D/sub 1/ and D/sub 2/, we obtain three orders of magnitude reduction in dictionary size, yet nearly no loss in diagnostic resolution.
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