Abstract detail

227 / 2021-04-09 10:16:31
Application of CYCBD for the planetary gearbox fault diagnosis based on encoder information
Encoder signal, Planetary gearbox, Feature enhancement, Maximum second-order cyclostationarity blind deconvolution (CYCBD)
Machine condition monitoring and fault diagnosis
Final Paper
Boyao Zhang / Beihang University;School of Reliability and Systems Engineering
Yonghao Miao / School of Reliability and Systems Engineering, Beihang University, Beijing, China
Jing Lin / School of Reliability and Systems Engineering, Beihang University, Beijing, China
Chenhui Li / School of Reliability and Systems Engineering, Beihang University, Beijing, China
With more dynamic condition information, the built-in encoder signal is more superior and convenient than the traditional vibration signal in the mechanical system fault diagnosis. However, the early incipient fault features are apt to be submerged in the raw encoder signal due to the predominant feature of increasing. In this paper, the raw encoder signal is derived firstly to obtain the instantaneous angular speed. Then, IAS as the input series of the algorithm of maximum second-order cyclostationarity blind deconvolution (CYCBD), is used to enhance the fault features and identify the condition of the planetary gearbox. Through the simulation and experiment case, the feasibility of CYCBD in IAS signal can be verified.

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Important Dates

Abstract Submission Deadline:

 31st March 2021 15th April 2021

Extended Deadline: 1st Aug. 2022

 

Abstract Acceptance:

30th April  2021 Rollover

 

Full Paper Submission Deadline:

30th June 2021  14th July 2021

Extended Deadline: 15th Aug. 2022 

 

Notification of Acceptance:

15th August 2021 1st Sept. 2021

1st Sept. 2022

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