机械设备振动监测的自适应变采样算法研究

Research on Adaptive Variable Sampling Algorithm for Vibration Monitoring of Mechanical Equipment

采样频率设置过高将会增加数据冗余与系统能耗。根据机械设备振动信号的非平稳性设计了一种自适应数据采集算法。该算法通过自适应脉冲采样获取当前信号,并从时、频域2方面分析信号,再利用突变检测模型对分析结果进行处理,以指导自适应变采样,随后利用插值算法对数据进行重构。最后基于MATLAB平台对算法进行了实现,并以离心泵正常和故障下信号的仿真结果验证了该算法的有效性。

High sampling frequency setting would increase data redundancy and system energy consumption.An adaptive data acquisition algorithm based on the nonstationarity of mechanical equipment vibration signals was designed.The algorithm acquired the current signal by adaptive pulsed-sampling and the data was analyzed in both time and frequency domains,the results of analysis was processed by mutation detection model for guiding the variable sampling,then the data would be reconstructed by interpolation algorithm.Finally,the algorithm was implemented based on MATLAB,and the simulation results of the normal and fault signals of the centrifugal pump show the effectiveness of the algorithm.