Research on Active Monitoring System of Power Supply Service Demand Based on Computer Big Data

Ding, Maomao; Zhang, Li; An, Yunzhu; Xu, Shihui; Wang, Ying

doi:10.1109/eebda53927.2022.9744998

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more efficient data processing method is needed. The set and data sharing requirements are higher [16,17]. In the field of condition monitoring systems, the research on algorithms for mining deep hidden information combined with deep learning methods has become a hot topic.…”

Section: B Development Status Of Condition Monitoring and Fault Detec...mentioning

confidence: 99%

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Liu

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system's reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

show abstract

Section: B Development Status Of Condition Monitoring and Fault Detec...mentioning

confidence: 99%

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Liu

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Research on Active Monitoring System of Power Supply Service Demand Based on Computer Big Data

Cited by 1 publication

References 6 publications

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Contact Info

Product

Resources

About