Translated Title

The Application Effect of Fault Prediction Model for High Value Parts of Medical Accelerator Based on One-Dimensional Convolutional Neural Network

Translated Abstract

Objective　To　construct　a　high-value　component　fault　prediction　model　for　medical　accelerators　to　achieve　prediction　of　high-value　component　faults.Methods　With　the　selection　of　60　sets　of　381　maintenance　record　data　from　January　2013　to　December　2017　for　the　Elekta　Synergy　medical　accelerator,these　data　were　randomly　assigned　to　the　training　set(42　groups)and　the　testing　set(18　groups)in　a　7‥3　ratio.With　a　one-dimensional　convolutional　neural　network　used　for　binary　classification　modeling,30　sets　of　data　were　randomly　selected　as　the　validation　set　to　evaluate　the　performance　of　the　model,and　the　prediction　effect　of　the　model　was　tested　by　using　the　test　set　data.Results　With　a　maximum　training　and　learning　times　of　120　times,when　the　actual　number　of　training　exceeds　80　times,the　data　tended　to　be　stable.In　addition,the　accuracy　of　the　training　and　validation　sets　remained　stable　at　around　90%,while　the　accuracy　of　the　test　set　data　was　above　96%,indicating　good　model　convergence.Conclusion　The　predicted　failure　times　of　high　value　parts　of　medical　accelerators　were　close　to　the　actual　situation,which　provided　reliable　data　support　for　preventive　maintenance　and　warranty　service　procurement.

Translated Keyword

Fault prediction model
High-value parts
Medical accelerator
One dimensional convolutional neural network

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WF:perioarticalylzb202414007

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Chinese

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