ArticleJournal of applied clinical medical physics2022
Topic modeling of maintenance logs for linac failure modes and trends identification.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging Active Learning for Failure Mode Acquisition.Sensors (Basel, Switzerland) · 2023Article
- Topic modeling of maintenance logs for linac failure modes and trends identification.Journal of applied clinical medical physics · 2022Article
- Statistical Analysis of Corrective Maintenance in Linear Accelerators Used in Radiotherapy.Journal of medical physicsArticle
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Authors and funding
3 authors.
Funding
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Abstract
purposeMedical linear accelerators (linacs) can fail in a multitude of different manners due to complex structures. An unclear identification of failure modes occurring constantly is a major obstacle to maintenance arrangements, thereby may increasing downtime. This study aims to use natural language processing techniques to deal with the unformatted maintenance logs to identify the linac failure modes and trends over time. MATERIALS AND
methodsThe data used in our study are unformatted narrative maintenance logs recording linac conditions and repair actions. The latent Dirichlet allocation-based topic modeling method was used to identify topics and keywords regarding the failure modes. The temporal analysis method was applied to examine the variation of failure modes over 20 years.
resultsBased on the output of the topic modeling, 28 topics and keywords with frequency ranking were generated automatically. The latent failure modes in topics were identified and classified into six main subsystems of linacs. Furthermore, by using the temporal analysis method, the trends of all failure modes over 20 years were illustrated. Half of the topics demonstrated variations with three different patterns, namely periodic, increasing, and decreasing.
conclusionsThe results of our study validated the effectiveness of using the topic modeling method to automatically analyze narrative maintenance logs. With domain knowledge, failure modes of linacs can be identified and categorized quantitatively.
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