Wear Debris Identification with Neural Networks

Joichi Sugimura, Akihiko Umeda, Yuji Yamamoto

    研究成果: ジャーナルへの寄稿学術誌査読

    5 被引用数 (Scopus)


    A feedforward neural network is applied to identification of wear debris generated under different sliding conditions. In order to describe characteristics of debris of various shapes and sizes, four representative parameters for groups of randomly sampled wear debris, i. e. 50% volumetric diameter, average elongation, average roundness and average reflectivity, are used as inputs to the network. Debris sampled in five steel sliding experiments are chosen as examples. It is shown that identification results depend on the ranges of these parameters, and that the ranges are determined by the sample size used for averaging. In the present case, a sample size of fifty pieces of debris provides a satisfactory identification result with less than ten percent error. It is also demonstrated that use of data for larger particles leads to better results. We discuss how the network determines difference in debris features, and how this approach can be applied to diagnosis of sliding surfaces.

    ジャーナルtransactions of the japan society of mechanical engineers series c
    出版ステータス出版済み - 1995

    !!!All Science Journal Classification (ASJC) codes

    • 材料力学
    • 機械工学
    • 産業および生産工学


    「Wear Debris Identification with Neural Networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。