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首页> 外文会议>Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV >Sensor and system health management simulation
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Sensor and system health management simulation

机译：传感器和系统健康管理模拟

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The health of a sensor and system is monitored by information gathered from the sensor. First, a normal mode of operation is established. Any deviation from the normal behavior indicates a change. Second, the sensor information is simulated by a main process, which is defined by a step-up, drift, and step-down. The sensor disturbances and spike are added while the system is in drift. The system runs for a period of at least three time-constants of the main process every time a process feature occurs (e.g. step change). The wavelet Transform Analysis is performed on three sets of data. The three sets of data are: the simulated data described above with Poisson distributed noise, real Manifold Pressure data, and real valve data. The simulated data with Poisson distributed noise of SNRs ranging from 10 to 500 were generated. Due to page limitations only the results of SNR of 50 is reported. The data are analyzed using continuous as well as discrete wavelet transforms. The results indicate distinct shapes corresponding to each process.
机译：传感器和系统的运行状况由从传感器收集的信息进行监控。首先，建立正常的操作模式。与正常行为的任何偏离都表明发生了变化。其次，传感器信息是通过主过程模拟的，该主过程由逐步，漂移和逐步降低定义。当系统处于漂移状态时，会添加传感器干扰和尖峰信号。每次出现过程功能（例如，步骤更改）时，系统都会在主过程的至少三个时间常数内运行。小波变换分析是对三组数据进行的。这三组数据是：上面描述的带有Poisson分布噪声的模拟数据，实际歧管压力数据和实际阀门数据。产生了具有泊松分布的SNR范围为10到500的模拟数据。由于页面限制，仅报告SNR结果为50。使用连续以及离散小波变换来分析数据。结果表明对应于每个过程的不同形状。

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