A Study on fault diagnosis of bearing pitting under different speed condition based on an improved inception capsule network

Measurement ◽  
2021 ◽  
pp. 109656
Author(s):  
Xueyi Li ◽  
Xiangwei Kong ◽  
Jiqiang Zhang ◽  
Zhiyong Hu ◽  
Cheng Shi
Measurement ◽  
2013 ◽  
Vol 46 (8) ◽  
pp. 2306-2312 ◽  
Author(s):  
Yu Yang ◽  
Huanhuan Wang ◽  
Junsheng Cheng ◽  
Kang Zhang

Author(s):  
Xuzhu Zhuang ◽  
Chen Yang ◽  
Jianhua Yang ◽  
Chengjin Wu ◽  
Zhen Shan ◽  
...  

The fault characteristic of rolling bearings under variable speed condition is a typical non-stationary stochastic signal. It is difficult to extract due to the interference of strong background noise makes the applicability of traditional noise reduction methods less. In this paper, an aperiodic stochastic resonance (ASR) method is proposed to study the fault diagnosis of rolling bearings under variable speed conditions. Based on numerical simulation, the effect of noise intensity and damping coefficient on the ASR of the second-order underdamped system is discussed, and an appropriate damping coefficient is found to reach the optimal ASR. The proposed method enhances the fault characteristic information of bearing fault simulation signal. Corresponding to rising-stationary and the stationary-declining running conditions, the method is verified by both simulated and experimental signals. It provides reference for fault diagnosis under variable speed condition.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Keon Kim

A planetary gearbox is one of the most important components in rotating machinery. In construction equipment, it is mainly used for swing and traveling devices due to huge torque capability and good torque transmission. Unexpected failure of the planetary gearbox can cause increased idle time, unnecessary cost due to inability of the construction site and safety hazards such as an operator isolation in a remote area. For these reasons, the need for timely prediction of unexpected failure becomes increasingly important in the field of construction machinery. In general, studies on fault diagnosis and condition monitoring of planetary gearboxes have been carried out in fields such as aircraft, wind-turbines and power plants. Researches in the above-mentioned fields usually require high performance computing power, and the burden of cost for diagnosis is relatively small. In addition, construction machinery also faces difficulties due to various uncertainties such as uncertain operating conditions that affect the vibration characteristics of gearboxes. This study focuses on an approach to the vibration-based fault diagnosis methodology that can distinguish gear faults in a planetary gearbox in an excavator using vehicle-based test. First, we analyze the types of gear faults and the parts where failures occur mainly through the database of field failures. From this result, several fault types to be used in the experiment are selected, and an arbitrary fault is applied to gears. Secondly, since the vibration data is acquired directly from the excavator, the signal processing method that can remove the noise as much as possible and distinguish the fault is selected. Finally, optimized features are selected to minimize the uncertainty impacts that cannot be eliminated or unknown. Through this study, we confirmed the effect of the signal processing method which can be used in the planetary gearbox of the excavator as follows: 1) Several kinds of fault can be distinguished. 2) Faults and methodologies that can be distinguished in the constant speed condition 3) Faults and methodologies that can be distinguished in the transition speed condition.


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