Knowledge extraction from textual data and performance evaluation in an unsupervised context

Chasseray, Yohann; Barthe‐Delanoë, Anne‐Marie; Negny, Stéphane; Lann, Jean-Marc Le

doi:10.1016/j.ins.2023.01.150

Cited by 4 publications

(1 citation statement)

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“…In more detail, it is designed to identify elements of interest (e.g., types of phenomena described and types of SSCs affected), extract temporal and location attributes, understand the nature of the reported event, and extract causal or temporal relationships between events. This type of NLP analysis has especially been applied in the medical field as shown in [25,26]. However, recent interest has also emerged in other fields including energetic [27], chemical [28,29], bioinformatics [30,31], material science [32], arts and humanities [33], and patent [34] analysis.…”

Section: Introductionmentioning

confidence: 99%

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Wang,

Mandelli,

Cogliati

2024

Energies

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Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

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