TY - GEN
T1 - Aliasing signal separation for superimposition of inductive debris detection using CNN-Based DUET
AU - Li, Tongyang
AU - Wang, Shaoping
AU - Zio, Enrico
AU - Shi, Jian
AU - Yang, Zhe
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Wear debris which contain multiple degrading information are of great interest to the running machines' health management. Among the several kinds of debris detection methods, inductive sensors have shown great potential for the online monitoring applications, along with which the superimposed voltage caused by the debris with short distances becomes a major factor influencing the accuracy of the detection. An improved convolutional neural network (CNN) combined with degenerate unmixing estimation technique (DUET) is proposed in the paper which offers an online solution for the inductive aliasing signal separation. The experimental result shows that the proposed method is effective and provides an alternative online approach of the original two-dimensional weighted histogram method.
AB - Wear debris which contain multiple degrading information are of great interest to the running machines' health management. Among the several kinds of debris detection methods, inductive sensors have shown great potential for the online monitoring applications, along with which the superimposed voltage caused by the debris with short distances becomes a major factor influencing the accuracy of the detection. An improved convolutional neural network (CNN) combined with degenerate unmixing estimation technique (DUET) is proposed in the paper which offers an online solution for the inductive aliasing signal separation. The experimental result shows that the proposed method is effective and provides an alternative online approach of the original two-dimensional weighted histogram method.
KW - Aliasing signal separation
KW - Degenerate unmixing estimation technique
KW - Health management
KW - Inductive debris detection
UR - https://www.scopus.com/pages/publications/85073062974
U2 - 10.1109/ICIEA.2019.8834290
DO - 10.1109/ICIEA.2019.8834290
M3 - 会议稿件
AN - SCOPUS:85073062974
T3 - Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
SP - 211
EP - 215
BT - Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
Y2 - 19 June 2019 through 21 June 2019
ER -