状态监测(CM)是预测和健康管理(PHM)的基础,这在工业界越来越重要。CM,这是指在操作过程中跟踪工业设备的健康状况,戏剧,事实上,在可靠性方面的重要作用,安全,和工业运营效率。本文提出了一种数据驱动的CM方法,该方法基于对采集的传感器数据进行自回归(AR)建模及其在频率子带内的分析。带的数量和大小是通过微不足道的人为干预来确定的,仅分析正常系统操作条件下感兴趣信号的时频表示。特别是,该方法利用同步压缩变换来改善信号在时频平面上的能量分布,定义基于AR功率谱密度和对称Itakura-Saito光谱距离的多维健康指标。所描述的健康指示器证明能够检测由于故障的发生而引起的信号频谱的变化。在初始定义频带和计算标称AR频谱的特性之后,该程序无需进一步干预,可用于在线状态监测和故障诊断。由于它是基于不同操作条件下的光谱比较,它的适用性既不取决于所获取信号的性质,也不取决于要监视的特定系统。作为一个例子,使用凯斯西储大学(CWRU)轴承数据中心的实际数据,一个广为人知和使用的基准。
Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment\'s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.