嵌入式智能仪器仪表弱故障信号的有效检测

Weak Fault Signal Detection in Embedded Intelligent Instrument and Meter

研究嵌入式智能仪器仪表弱故障信号检测方法,提高检测的准确性。嵌入式仪器仪表设备的结构复杂,故障信号出现的频率快,周期短,很难形成长时间的可识别特征,传统的故障检测模型在进行故障信号检测过程中,对短促的故障信号很难有效提取,不能组成有意义的故障识别模型,造成识别困难。为此,提出了一种基于连续模糊动态模型的嵌入式智能仪器仪表弱故障信号的检测方法。根据提升小波变换阀值去噪的相关理论,去除采集的嵌入式智能仪器仪表故障信号的噪声,提高信号的质量。建立连续模糊动态模型,从而完成嵌入式智能仪器仪表故障信号的检测。实验结果表明,利用动态模型算法进行嵌入式智能仪器仪表弱故障信号检测,能够提高检测的准确性,保证嵌入式智能仪器仪表的正常运行。

In order to improve the accuracy of detection, the weak fault signal detection method for embedded intelligent instrumentation was studied in this article. Since the structure of embedded instrumentation is complex, the frequency of the fault signal occurrence is fast and cycle is short, it is difficult to form long recognizable features. To this end, we proposed a method for the weak fault signal detection of embedded intelligent instrumentation based on a dynamic model of continuous fuzzy. According to the theory of lifting wavelet transform threshold, the acquisition fault signal noise of embedded smart instrumentation can be removed. Then a continuous fuzzy dynamic model was created to complete the fault signal detection of embedded smart instrumentation. Experimental results show that the dynamic model algorithm for weak fauh signal detection of embedded smart instrumentation can improve the accuracy of detection and ensure the normal operation of embedded intelligent instrumentation.