The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy.

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings.

Next-generation 5G fusion-based intelligent health-monitoring platform for ethylene cracking furnace tube.

This study aimed to develop a 5G + "mixed computing" + deep learning-based next-generation intelligent health-monitoring platform for an ethylene cracking furnace tube based on 5G communication technology, with the goal of improving the health management level of the key component of ethylene production, that is, the cracking furnace tube, and focusing on the key common technical difficulties of ethylene production of tube outer-surface temperature sensing and tube slagging diagnosis. It also integrated the edge-fog-cloud "mixed computing" technology and deep learning technology in artificial intelligence, which had a higher degree in the research and development of automation and intelligence, and was more versatile in an industrial environment. The platform included a 5G-based tube intelligent temperature-measuring device, a 5G-based intelligent peep door gearing, a 5G-based edge-fog-cloud collaboration mechanism, and a mixed deep learning-related application. The platform enhanced the automation and intelligence of the enterprise, which could not only promote the quality and efficiency of the enterprise but also protect the safe operation of the cracking furnace device and lead the technological progress and transformation and upgrading of the industry through the application.

[Detection of Pneumocystis carinii DNA in rats by PCR-ELISA].

OBJECTIVE: To establish a PCR-ELISA and evaluate its use in detecting DNA of Pneumocystis carinii (P. c) in rat model. METHODS: SD rats and Wistar rats were used in the experiment. P. c DNA from rat lung tissue and BALF was amplified by PCR. The amplified products were visualized by ethidium bromide (EB) staining after agarose gel electrophoresis or detected by ELISA. The results were compared with that by Giemsa stain. RESULTS: The positive rate in the two species of rats by the two methods was 96.4% and 100% in lung tissue, 96.4% and 100% in BALF, respectively, with no significant difference (P>0.05). Giemsa positive samples were all positive by PCR-ELISA. The negative control group had one positive by ELISA in lung tissue and BALF respectively. CONCLUSION: PCR-ELISA shows a high sensitivity and specificity in detecting the DNA of Pneumocystis carinii, which is a secure and easy use method.