Free-standing NiO/MnO2 core-shell nanoflake structure was deposited on flexible carbon cloth (CC) used as electrode for high-performance supercapacitor (SC). The NiO core was grown directly on CC by hydrothermal process and the following annealing treatment. MnO2 thin film was then covered on NiO structures via a self-limiting process in aqueous solution of 0.5 M KMnO4 and 0.5 M Na2SO4 with a carbon layer serving as the sacrificial layer. Both the core and shell materials are good pseudocapacitive materials, the compounds of binary metal oxides can provide the synergistic effect of all individual constituents, and thus enhance the performance of SC electrode. The obtained CC/NiO/MnO2 heterostructure was directly used as SC electrodes, showing an enhanced electrochemical performance including areal capacitance of 316.37 mF/cm2 and special gravimetric capacitance of 204.3 F/g at the scan rate of 50 mV/s. The electrode also shows excellent cycling stability, which retains 89% of its initial discharge capacitance after 2200 cycles with >97% Coulombic efficiency. The synthesized binder-free hierarchical composite electrode with superior electrochemical properties demonstrates enormous potential in the application of flexible SCs.
Flexible sensing tends to be widely exploited in the process of human–computer interactions of intelligent robots for its contact compliance and environmental adaptability. A novel flexible capacitive tactile sensor was proposed for multi-directional force sensing, which is based on carbon black/polydimethylsiloxane (PDMS) composite dielectric layer and upper and lower electrodes of carbon nanotubes/polydimethylsiloxane (CNTs/PDMS) composite layer. By changing the ratio of carbon black, the dielectric constant of carbon black/PDMS composite layer increases at 4 wt%, and then decreases, which was explained according to the percolation theory of the conductive particles in the polymer matrix. Mathematical model of force and capacitance variance was established, which can be used to predict the value of the applied force. Then, the prototype with carbon black/PDMS composite dielectric layer was fabricated and characterized. SEM observation was conducted and a ratio was introduced in the composites material design. It was concluded that the dielectric constant of carbon sensor can reach 0.1 N within 50 N in normal direction and 0.2 N in 0–10 N in tangential direction with good stability. Finally, the multi-directional force results were obtained. Compared with the individual directional force results, the output capacitance value of multi-directional force was lower, which indicated the amplitude decrease in capacity change in the normal and tangential direction. This might be caused by the deformation distribution in the normal and tangential direction under multi-directional force.
To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequencydomain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybriddomain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper.INDEX TERMS Permutation entropy, variational mode decomposition, locality preserving projection, rolling bearing, fault diagnosis. I. INTRODUCTIONResearch on fault detection of rolling element bearing has drawn much attention in recent years. Rolling element bearings are the major parts of rotating machinery and widely used in many industrial fields. Tiny faults existing in rolling element bearing easily cause the stop running of mechanical system, bring the tremendous economic losses and even give rise to the serious accident and personnel casualties [1]- [5]. Consequently, it is of much concern to explore a novel and The associate editor coordinating the review of this manuscript and approving it for publication was Youqing Wang .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.