Performance Optimization of Intelligent Sensor Monitoring System for Industrial Mechanical Equipment Based on Improved Algorithm

Abstract

With the rapid development of industrial Internet of Things (IIoT) and intelligent manufacturing technology, intelligent sensor monitoring systems have been widely applied in the condition monitoring and fault early warning of industrial mechanical equipment. However, traditional monitoring systems suffer from prominent problems such as low data processing efficiency, high transmission delay, poor anti-interference ability and low fault recognition accuracy in complex industrial working conditions, which seriously restrict the real-time performance and stability of equipment state monitoring. To solve the above defects, this paper proposes an improved hybrid optimization algorithm based on variational mode decomposition (VMD) and adaptive weighted ensemble learning, and applies it to the performance optimization of industrial mechanical equipment intelligent sensor monitoring system. Firstly, the system overall architecture is optimized, and a hierarchical data processing framework of edge acquisition, real-time processing and cloud analysis is constructed to reduce data transmission pressure. Secondly, aiming at the noise interference and redundant data of sensor vibration, temperature and current signals in industrial environments, an improved VMD algorithm with adaptive penalty factor is designed to realize efficient denoising and feature extraction of monitoring data. Finally, an adaptive weighted ensemble fault detection model is established to optimize the data analysis and state recognition performance of the monitoring system. Experimental results show that compared with the traditional system, the improved monitoring system reduces the data processing delay by 41.6%, improves the signal denoising signal-to-noise ratio (SNR) by 3.8 dB, and increases the equipment fault recognition accuracy to 96.2%. The optimized system has better real-time performance, stability and detection accuracy, which can effectively meet the high-precision and real-time monitoring requirements of industrial mechanical equipment under complex working conditions.