98. Locomotive drive system fault diagnosis based on dynamic self-adaptive blind source separation
Bin Ren1, Rujiang Hao2, Shaopu Yang3
Shijiazhuang Tiedao University, Hebei, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com
Received 29 May 2016; received in revised form 1 July 2016; accepted 13 July 2016
Abstract. Drive system is one of most important key equipment to guarantee safe and stable operation in locomotive. With time variation, unpredictability and nonstationary, fault source of drive system is not obtained by traditional fault diagnosis method. Blind source separation is a kind of method on source signals separation under transmission channel unknown instance. The method of Blind source separation based on variable metric empirical mode decomposition is proposed. Intrinsic mode function is built, redundancy factors are reduced, and recurrent neural network is used to adaptive blind separation. The method is verified by data analysis of on‑line measuring. The results show that separation efficiency is improved and unaffected with iteration time in the process of fault information separation, which will be better for further fundamental research and provide technique support for the locomotive.
Keywords: drive system, adaptive blind separation, fault diagnosis, locomotive running gear.
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Cite this article
Ren Bin, Hao Rujiang, Yang Shaopu Locomotive drive system fault diagnosis based on dynamic self‑adaptive blind source separation. Journal of Measurements in Engineering, Vol. 4, Issue 3, 2016, p. 140‑147.
Journal of Measurements in Engineering. September 2016, Volume 4, Issue 3
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