Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest.

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

Effect and mechanism of Mitomycin C combined with recombinant adeno-associated virus type II against glioma.

The effect of chemotherapy drug Mitomycin C (MMC) in combination with recombinant adeno-associated virus II (rAAV2) in cancer therapy was investigated, and the mechanism of MMC affecting rAAV2's bioactivity was also studied. The combination effect was evaluated by the level of GFP and TNF expression in a human glioma cell line, and the mechanism of MMC effects on rAAV mediated gene expression was investigated by AAV transduction related signal molecules. C57 and BALB/c nude mice were injected with rAAV-EGFP or rAAV-TNF alone, or mixed with MMC, to evaluate the effect of MMC on AAV-mediated gene expression and tumor suppression. MMC was shown to improve the infection activity of rAAV2 both in vitro and in vivo. Enhancement was found to be independent of initial rAAV2 receptor binding stage or subsequent second-strand synthesis of target DNA, but was related to cell cycle retardation followed by blocked genome degradation. In vivo injection of MMC combined with rAAV2 into the tumors of the animals resulted in significant suppression of tumor growth. It was thus demonstrated for the first time that MMC could enhance the expression level of the target gene mediated by rAAV2. The combination of rAAV2 and MMC may be a promising strategy in cancer therapy.