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Home > Archive>Volume 52, Issue 1, 2024 >35-40. DOI:10.11908/j.issn.0253-374x.23387
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PCA-gcForest-based Fault Diagnosis of S700K Switch Machine
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1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804， China;2.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety， Tongji University， Shanghai 201804， China

Clc Number:

U284.92

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    Abstract:

    To overcome the shortage of the existing fault diagnosis methods such as low accuracy and efficiency， a fault diagnosis method based on principal component analysis （PCA） and multi-grain cascade forest （gcForest） algorithm was proposed. PCA was used to simplify the current eigenvalue for 11 fault modes of S700K switch machine. And an improved gcForest model with the simpler eigenvalue embedded was used to improve the data processing capability and enhance the inherent feature representativeness of the model. The experimental results show that the fault diagnosis accuracy of the improved gcForest model is 97.62%， which verifies the effectiveness and superiority of the method.

    Reference
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HU Xiaochen, GUO Ning, SHEN Tuo, DONG Decun. PCA-gcForest-based Fault Diagnosis of S700K Switch Machine[J].同济大学学报(自然科学版),2024,52(1):35~40

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  • Received:April 11,2023
  • Online: January 27,2024
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