Knowledge discovery and knowledge transfer in board-level functional fault diagnosis
Knowledge discovery and knowledge transfer in board-level functional fault diagnosis
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板级功能故障诊断中的知识发现和知识迁移
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Xinli Gu
中科院分区：
文献类型：
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作者：
Fangming Ye;Zhaobo Zhang;K. Chakrabarty;Xinli Gu
板级功能故障诊断对于提高产品良率和降低制造成本至关重要。推理技术提高了基于成功修复电路板的历史的功能故障诊断的准确性。然而，根据产品的复杂性，通常需要几个月的时间来积累足够的数据库，以训练基于推理的诊断系统。在最初的产品爬坡阶段，基于推理的诊断对于良率学习是不可行的，因为所需的数据库由于缺乏容量而不可用。我们提出了一个知识发现方法和知识转移方法，以促进板级功能故障诊断。首先，使用基于机器学习的分析技术从症状中发现知识，这些知识可用于训练诊断引擎。其次，来自用于早期产品的诊断引擎的知识可以通过基于关键字和电路板结构相似性的根本原因映射和综合征映射来自动传输。两个复杂的板在批量生产，并与一个成熟的诊断系统，和三个新的板在爬坡阶段，被用来验证所提出的知识发现和知识转移的方法，在使用新的诊断系统获得的诊断精度。
Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. Reasoning techniques increase the accuracy of functional-fault diagnosis based on the history of successfully repaired boards. However, depending on the complexity of the product, it usually takes several months to accumulate an adequate database for training a reasoning-based diagnosis system. During the initial product ramp-up phase, reasoning-based diagnosis is not feasible for yield learning, since the required database is not available due to lack of volume. We propose a knowledge-discovery method and a knowledge-transfer method for facilitating board-level functional fault diagnosis. First, an analysis technique based on machine learning is used to discover knowledge from syndromes, which can be used for training a diagnosis engine. Second, knowledge from diagnosis engines used for earlier-generation products can be automatically transferred through root-cause mapping and syndrome mapping based on keywords and board-structure similarities. Two complex boards in volume production and with a mature diagnosis system, and three new boards in the ramp-up phase, are used to validate the proposed knowledge-discovery and knowledge-transfer approach in terms of the diagnosis accuracy obtained using the new diagnosis systems.