Multivariate signal analysis for process noise source identification

Abstract

In the present work sponsored by Swedish National Board for Energy Source Development a method for identification of noise sources has been used — the multivariate auto regressive process (AR). Analyses were performed using simulated noise and operating nuclear power plant process noise. The simulated noise signals are generated mathematically in the same way as the system structure identified by the AR-method. This fact makes the interpretation of the result easy. The analysis of the simulated noise shows convincingly the possibility of the method to identify a simulated stochastic system and to clarify the influence of the different noise sources. Auto power spectrum of nuclear power plant signals calculated by the AR-method are in good agreement with FFT (Fast Fourier Transform) calculations. The contributions to the noise from the different sources calculated by the AR-program are in agreement with expected characteristics from physical considerations. With the aid of power plant signals examples are given how the result of the analyses are influenced by the number of measurement signals. Analyses of 3 and 1 signals are given for comparison. Given examples also emphasize the importance of chosing a suitable selection of measured signals. Experience from the test runs shows that non stationary noise signals or unsuitably chosen order may cause the computation not to converge. This can in general be overcome by choosing a different order of measured signals and/or increase the number of time series data used. The analyses of simulated noise were performed using 500 time series data while for the power plant noise 2 000 – 3 000 data were required to reach convergence in the calculations. The difference in requirements of time series data shows that the power plant signals do not have the same ideal statistics as simulated noise.