Self-powered fault diagnosis of rolling bearings based on triboelectric effect

2022 ◽  
Vol 166 ◽  
pp. 108382
Qinkai Han ◽  
Ziyuan Jiang ◽  
Xueping Xu ◽  
Zhuang Ding ◽  
Fulei Chu
Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

MeiYing Qiao ◽  
XiaXia Tang ◽  
YuXiang Liu ◽  
ShuHao Yan

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 337
Navneet Soin ◽  
Sam J. Fishlock ◽  
Colin Kelsey ◽  
Suzanne Smith

The use of rapid point-of-care (PoC) diagnostics in conjunction with physiological signal monitoring has seen tremendous progress in their availability and uptake, particularly in low- and middle-income countries (LMICs). However, to truly overcome infrastructural and resource constraints, there is an urgent need for self-powered devices which can enable on-demand and/or continuous monitoring of patients. The past decade has seen the rapid rise of triboelectric nanogenerators (TENGs) as the choice for high-efficiency energy harvesting for developing self-powered systems as well as for use as sensors. This review provides an overview of the current state of the art of such wearable sensors and end-to-end solutions for physiological and biomarker monitoring. We further discuss the current constraints and bottlenecks of these devices and systems and provide an outlook on the development of TENG-enabled PoC/monitoring devices that could eventually meet criteria formulated specifically for use in LMICs.

2021 ◽  
pp. 107413
Dawei Gao ◽  
Yongsheng Zhu ◽  
Zhijun Ren ◽  
Ke Yan ◽  
Wei Kang

2017 ◽  
Vol 17 (19) ◽  
pp. 6418-6430 ◽  
Tao Wang ◽  
Yigang He ◽  
Qiwu Luo ◽  
Fangming Deng ◽  
Chaolong Zhang

Nano Energy ◽  
2021 ◽  
Vol 89 ◽  
pp. 106448
Minchang Kim ◽  
Yoonsang Ra ◽  
Sumin Cho ◽  
Sunmin Jang ◽  
Dongik Kam ◽  

Sign in / Sign up

Export Citation Format

Share Document