A WAV-ICA Based On-Line Fault Diagnosis Method for Redundant Sensors in Nuclear Power Plants
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摘要: 针对核动力装置传感器的在线故障诊断问题,提出一种基于小波独立成份分析(WAV-ICA)的冗余传感器故障在线诊断方法。利用小波分解对测量信号进行高频滤波,消除电子噪声和传输噪声的影响,选取所关注的独立成分进行参数估计,并进行独立成分分析(ICA),从而实现对固定偏差、突变故障以及漂移故障的在线检测;利用稳压器5路冗余压力测量数据对该方法的故障检测效果进行验证,并与简单分析法(SA)和直接ICA方法进行对比分析。结果表明,该方法能有效地在线检测固定偏差和漂移故障,具有误诊率低、鲁棒性强、受单通道故障影响小的特点。Abstract: A WAV-ICA based on-line fault diagnosis method for redundant sensors in nuclear power plants is proposed in the paper.In order to eliminate the effect of the electric noise and transmission noise,the high-frequency noise in redundant sensors signal is filtered with wavelet decomposition method at first.Then the Independent Component Analysis（ICA） is performed and the component with interest is selected for reconstructing the estimated signal,so as to detect the fixed bias and abrupt faults of the sensors,as well as their drift failures.A history data set from five redundant pressurizer pressure sensors in a NPP is adopted to validate the proposed method.The result shows that compared with the Simple Analysis（SA） method and direct ICA method,the proposed method can detect the fixed bias and drift faults of the sensors on-line effectively,with lower rate of misdiagnosis and better robustness.
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