Sensor Mathematical Model Data Fusion Biobjective Optimization

Abstract

Sensors are an important tool to quantify the changes and an important part of the information acquisition system; the performance and accuracy of sensors are more strictly desired. In this paper, a highly sensitive fiber optic sensor for measuring temperature and refractive index is prepared by using femtosecond laser micromachining technology and fiber fusion technology. The multimode fiber is first spliced together with single-mode fiber in a positive pair, and then, the multimode fiber is perforated using a femtosecond laser. The incorporation of data model sensors has led to a rapid increase in the development and application of sensors as well. Based on the design concept and technical approach of the wireless sensor network system, a general development plan of the indoor environmental monitoring system is proposed, including the system architecture and functional definition, wireless communication protocols, and design methods of node applications. The sensor has obvious advantages over traditional electrical sensors; the sensor is resistant to electromagnetic interference, electrical insulation, corrosion resistance, low loss, small size, high accuracy, and other advantages. The upper computer program of the indoor environment monitoring system was developed in a Visual Studio development environment using C# language to implement the monitoring, display, and alarm functions of the indoor environment monitoring system network. The sensor-data model interfusion with each other for mutual integration performs the demonstration of the application.

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

“…However, the working environment of most bearings is complex, and the collected signals contain a lot of noise and harmonic components, which cause obstacles for Processes 2022, 10, 1734 2 of 25 the accurate extraction of bearing faults [3]. Many methods have been applied to improve the accuracy of fault diagnosis and remaining life prediction in complex environments, such as dynamic model analysis based on the physical characteristics of the bearing itself [4][5][6], signal analysis methods in the time-frequency domain [7][8][9], methods based on entropy of the information contained in the signal [10,11], end-to-end methods of the neural network [1,12,13], and the method of model data fusion [14,15].…”