When used separately on the sensor data of the processes like nuclear reactors, the data reconciliation with fault detection and isolation strategy gives noise-corrupted estimates, and the wavelet transformation gives erroneous inferences about the operating point of the process under sensor fault conditions. Aiming to solve these challenging problems, a hybrid multi-scale data reconciliation scheme that combines data reconciliation with the wavelet transform is proposed in this work. The proposed method uses the steady-state data reconciliation framework under the assumption of consistent algebraic relationships among the wavelet coefficient data. The role of multivariate techniques in obtaining the algebraic relationships, online detection and isolation of sensor faults, orthogonal decomposition, and reconciliation of the wavelet coefficients data is demonstrated. It is shown that the reconciled estimates obtained from this method very closely represent the true behavior of the process as problems with respect to random noise, high-frequency components due to process faults, sensor faults, and the influence of sensor faults on the signal estimates are alleviated. The effectiveness of this method is quantitatively established when applied to the ex-core neutron detector data of the advanced heavy water reactor in various simulations.

当在核反应堆之类的过程的传感器数据上单独使用时，具有故障检测和隔离策略的数据协调会产生噪声损坏的估计值，而小波变换会错误地推断出传感器故障条件下过程的工作点。为了解决这些具有挑战性的问题，在这项工作中提出了一种混合多尺度数据协调方案，该方案将数据协调与小波变换相结合。该方法在假设小波系数数据之间具有代数关系的前提下，使用稳态数据和解框架。多元技术在获取代数关系，在线检测和隔离传感器故障，正交分解，并证明了小波系数数据的协调性。结果表明，通过这种方法获得的调节后的估计值非常接近地反映了过程的真实行为，如随机噪声，过程故障导致的高频分量，传感器故障以及传感器故障对信号估计值的影响等问题。被缓解。在各种模拟中将这种方法应用于先进重水反应堆的核外中子探测器数据时，将定量确定该方法的有效性。减轻了传感器故障对信号估计的影响。在各种模拟中将这种方法应用于先进重水反应堆的核外中子探测器数据时，将定量确定该方法的有效性。减轻了传感器故障对信号估计的影响。在各种模拟中将这种方法应用于先进重水反应堆的核外中子探测器数据时，将定量确定该方法的有效性。