The key function of rotating machine condition monitoring (CM) is to detect structural changes during machine operations. This paper presents a novel statistical time-frequency analysis method for this purpose. In particular, frequency spectrum is extracted from the machine condition signals based on periodogram estimation. Undirected weighted graph is then constructed from the resulting periodograms, where the so-called median graph is introduced and adopted to describe the normal machine status. Statistical analysis is performed to investigate newly observed data with respect to the median graph for change decision making. The proposed method has been applied to three different engineering applications to evaluate its effectiveness: load CM; early bearing failure detection; and speed CM. The results were compared with some benchmark methods reported in the literature, where significant improvements of the proposed method were demonstrated, indicating its good potentials in engineering applications.

中文翻译：

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用于旋转机械状态监测的新型统计时频分析


旋转机械状态监测 (CM) 的关键功能是检测机器运行期间的结构变化。为此，本文提出了一种新颖的统计时频分析方法。特别地，基于周期图估计从机器状况信号中提取频谱。然后根据所得周期图构建无向加权图，其中引入并采用所谓的中值图来描述正常机器状态。进行统计分析是为了调查新观察到的数据相对于中值图的变化决策。所提出的方法已应用于三种不同的工程应用以评估其有效性：负载CM；早期轴承故障检测；和速度CM。结果与文献中报道的一些基准方法进行了比较，证明了该方法的显着改进，表明其在工程应用中具有良好的潜力。