In technical domains, particularly asset management and maintenance, vast amounts of valuable information are captured in unstructured text formats such as work orders, maintenance logs, and inspection reports [1, 4, 28, 30]. Extracting actionable insights from this "technical language" is challenging using traditional methods [1, 15]. Concurrently, Failure Mode and Effect Analysis (FMEA) is a widely adopted systematic approach for identifying potential failure modes within a system, analyzing their causes and effects, and prioritizing risks [2, 6]. While powerful, traditional FMEA is often a manual and labor-intensive process, relying on expert knowledge and structured data [8]. This article explores the integration of FMEA principles with Technical Language Processing (TLP) techniques to automate and enhance the extraction, analysis, and utilization of failure-related information from unstructured technical text. We propose a conceptual framework where TLP methods are employed to identify potential failure modes, effects, and causes within technical documentation. These extracted elements can then be structured and analyzed using FMEA principles to provide data-driven insights into asset reliability and maintenance needs [5]. This integration has the potential to improve the efficiency and effectiveness of FMEA, unlock hidden knowledge within maintenance data, and support proactive maintenance strategies.

Failure Mode and Effect Analysis (FMEA), Technical Language Processing (TLP), Natural Language Processing (NLP)

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