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.
Highlights
Gear transmission is a commonly used transmission method in mechanical equipment, in which planetary gear transmission has the advantages of large transmission ratio, strong carrying capacity, high transmission efficiency, etc., which is commonly used in the transmission system of mechanical equipment [1]
This paper presents a planetary gear fault diagnosis method based on permutation entropy of
This presentsThe a planetary fault diagnosis method based on permutation entropy
Summary
Gear transmission is a commonly used transmission method in mechanical equipment, in which planetary gear transmission has the advantages of large transmission ratio, strong carrying capacity, high transmission efficiency, etc., which is commonly used in the transmission system of mechanical equipment [1]. The method of gear fault diagnosis proposed in this paper is based on the analysis of the vibration signals which are collected by an acceleration sensor. A fault diagnosis method based on CEEMDAN-permutation entropy ANFIS planet gear is proposed to solve the problem that the state of the planetary gear fault is difficult to identify under the condition of constant load. The third part presents experiments using DDS comprehensive mechanical fault simulation bench and equipment which are acceleration sensors for collecting vibration signals of planetary gear. The collected vibration signals are decomposed into a series of IMFs by CEEMDAN, and the permutation entropy of each IMF is extracted as the fault feature. The last part puts forward the conclusion of this paper
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