Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

Abstract

The Failure Mode Effect Analysis process (FMEA) is widely used in industry for risk assessment, as it effectively captures and documents domain-specific knowledge. This process is mainly concerned with causal domain knowledge. In practical applications, FMEAs encounter challenges in terms of comprehensibility, particularly related to inadequate coverage of listed failure modes and their corresponding effects and causes. This can be attributed to the limitations of traditional brainstorming approaches typically employed in the FMEA process. Depending on the size and diversity in terms of disciplines of the team conducting the analysis, these approaches may not adequately capture a comprehensive range of failure modes, leading to gaps in coverage. To this end, methods for improving FMEA knowledge comprehensibility are highly needed. A potential approach to address this gap is rooted in recent advances in commonsense knowledge graph completion have illustrated effectiveness of text-aware graph embedding methods. However, the applicability of such methods in an industrial setting is limited. This paper addresses this issue on FMEA documents in an industrial environment. Here, the application of common-sense knowledge graph completion methods on FMEA documents from semiconductor manufacturing is studied. These methods achieve over 20% MRR on the test set and 70% of the top 10 predictions were manually assessed to be plausible by domain experts. Based on the evaluation, this paper confirms that text-aware knowledge graph embedding for common-sense knowledge graph completion are more effective than structure-only knowledge graph embedding for improving FMEA knowledge comprehensibility. Additionally we found that language model in domain fine-tuning is beneficial for extracting more meaningful embedding, thus improving the overall model performance.