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Technologies for Forming Equivalent Noises of Noisy Signals and Their Use

机译:形成嘈杂信号等同噪声的技术及其使用

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摘要

It is shown that in controlled objects noisy signals are formed by useful signals and noises. In this case, it is usually impossible to isolate the noise from the noisy signal. For this reason, the estimates of the statistical characteristics of noisy signals are calculated using traditional technologies contain significant errors. At the same time, separate processing of the useful signal and the noise makes it possible to identify moments of change in the current state of a technical object, as well as to extract the necessary diagnostic information contained in the noise characteristics. Therefore, algorithms and technologies are developed for forming, from samples of the noisy signal, samples of the useful signal and samples of the noise separately, which are called equivalent samples of the useful signal and equivalent samples of the noise. It is shown that despite the difference in the values of real and equivalent samples, the characteristics of these signals practically coincide. This allows, firstly, increasing the accuracy of estimates of the correlation and spectral characteristics of noisy signals in comparison with estimates obtained by traditional technologies. Secondly, the same characteristics of the equivalent useful signal and the equivalent noise obtained separately allow controlling the beginning of the latent period of the initiation of malfunctions of objects under investigation. In addition, it is also found that the estimates of the cross-correlation function between the useful signal and the noise contain diagnostic information. Computational experiments are carried out. For this, the useful signal and the noise with set characteristics are modeled, and the noisy signal is generated. Equivalent samples of the noise and the useful signal are determined. The characteristics of the generated useful signal and the noise, as well as the equivalent useful signal and equivalent noise, are calculated by the traditional algorithms. A comparative analysis is carried out. To this end, the relative errors of the characteristics of the generated and equivalent useful signals and noise are determined. The calculation results show that the characteristics of the generated useful signal and interference, as well as the equivalent useful signal and noise practically coincide. It is shown that the developed algorithms and technologies can be used in monitoring and control systems to improve the accuracy of the analysis of noisy signals received at the outputs of the sensors. These technologies can also be used in information measuring and other measuring complexes and systems, which will significantly improve their efficiency.
机译:结果表明,在受控物体中,通过有用的信号和噪声形成噪声信号。在这种情况下,通常不可能与噪声信号隔离噪声。因此,使用传统技术计算噪声信号统计特征的估计包含显着的错误。同时,单独处理有用信号和噪声使得可以识别技术对象的当前状态的变化的时刻,以及提取包含在噪声特性中的必要诊断信息。因此,开发了算法和技术用于从嘈杂信号的样本,有用信号的样本和分别的噪声样本的样本,这些样本称为有用信号的等效样本和噪声的等效样本。结果表明,尽管实际上是实际和等效样本的值的差异,但这些信号的特性实际上是重合的。这允许首先,与传统技术获得的估计相比,增加了噪声信号的相关性和光谱特性的准确性。其次,相同的有用信号的特征和单独获得的等效噪声允许控制在调查中对物体发生故障的启动潜在的开始。另外,还发现有用信号与噪声之间的互相关函数的估计包含诊断信息。进行计算实验。为此,建模有用信号和具有设定特性的噪声,并产生噪声信号。确定噪声的等效样本和有用信号。通过传统算法计算所产生的有用信号和噪声以及等效的有用信号和等效噪声的特性。进行比较分析。为此,确定所生成的特征的相对误差和等效的有用信号和噪声的相对误差。计算结果表明,生成的有用信号和干扰的特性,以及等效的有用信号和噪声实际上是重合的。结果表明,发达的算法和技术可用于监测和控制系统,以提高在传感器的输出处接收的噪声信号分析的准确性。这些技术也可用于信息测量和其他测量复合物和系统,这将显着提高其效率。

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