Abstract
Mechanical vibration monitoring for rotating mechanical equipment can improve the safety and reliability of the equipment. The traditional wired monitoring technology faces problems such as high-frequency signal pickup and high-precision data collection. Therefore, this paper proposes optimization techniques for mechanical vibration monitoring and signal processing based on wireless sensor networks. First, the hardware design uses high-performance STM32 as the control center and Si4463 as the wireless transceiver core. The monitoring node uses a high-precision MEMS acceleration sensor with a 16-bit resolution ADC acquisition chip to achieve high-frequency, high-precision acquisition of vibration signals. Then, the bearing vibration signal optimization method is studied, and the sparse Bayes algorithm is proposed as a compressed sensing reconstruction algorithm. Finally, the difference in reconstruction accuracy between this method and the traditional reconstruction algorithm is compared through experiments and the effect of this method on the reconstruction performance is analyzed when different parameters are selected.
Highlights
1 Introduction Vibration fault monitoring technology is to understand the state of the overall mechanical equipment or local mechanical parts during operation by analyzing the mechanical vibration signals collected by the sensors
5 Discussion The wireless vibration fault monitoring technology is to analyze the mechanical vibration signal collected by the sensor to understand the status of the rotating mechanical equipment during operation, and transmit the monitoring information through the wireless sensor network
This paper analyzes the problems that the wireless sensor network needs to solve in the application of mechanical vibration monitoring, and designs a set of wireless sensor network vibration monitoring system suitable for rotating machinery to initially realize the status monitoring of mechanical equipment
Summary
Vibration fault monitoring technology is to understand the state of the overall mechanical equipment or local mechanical parts during operation by analyzing the mechanical vibration signals collected by the sensors. This technology is a technology used to discover the early failure of mechanical equipment or predict the development trend of mechanical equipment failure [1]. The health of rolling bearings greatly affects the operating state of the entire mechanical equipment [3]. When the rolling bearing fails, it will directly reduce the stability of the entire mechanical equipment and affect the working efficiency, and even a serious production accident occurs [4]. It is very important to monitor the running status of bearings in real time through the mechanical equipment status monitoring system
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