Print Email Facebook Twitter Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS Title Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS Author Kuai, Moshen (China University of Mining and Technology) Cheng, Gang (China University of Mining and Technology) Pang, Y. (TU Delft Transport Engineering and Logistics) Li, Yong (China University of Mining and Technology) Date 2018 Abstract For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively. Subject planetary gearfault diagnosisCEEMDANpermutation entropyANFIS To reference this document use: http://resolver.tudelft.nl/uuid:bbc1fef3-a45a-457b-bd7e-890f8c66f04e DOI https://doi.org/10.3390/s18030782 ISSN 1424-8220 Source Sensors, 18 (3) Part of collection Institutional Repository Document type journal article Rights © 2018 Moshen Kuai, Gang Cheng, Y. Pang, Yong Li Files PDF sensors_18_00782.pdf 1.11 MB Close viewer /islandora/object/uuid:bbc1fef3-a45a-457b-bd7e-890f8c66f04e/datastream/OBJ/view