1. A method of processing at least one data base obtained from at least one of an industrial process and at least a first and second sensor for determining fault conditions of data characteristic of a process, comprising the steps of: collecting data from at least a first and second sensor to redundantly accumulate data from at least one physical variable of the industrial process to provide a first data signal from said first sensor and a second data signal from said second sensor, each said data signal being characteristic of the one physical variable; obtaining a difference function of the data characteristic of the arithmetic difference pairwise between said first data signal and said second data signal at each of a plurality of different times of sensing the one physical variable; obtaining a frequency domain transformation of said first difference function to procure Fourier coefficients corresponding to Fourier frequencies; generating a composite function over a time domain using the Fourier coefficients; obtaining a residual function over time by determining the arithmetic difference between the difference function and the composite function, the residual function having reduced serially correlated noise; operating on the residual function using computer means for performing a statistical analysis technique to determine whether alarm condition data is present in at least one of the data base from the industrial process and from the at least first and second sensor, the residual function including white noise characteristics of an uncorrelated function of reduced skewness relative to the difference function and input to the statistical analysis technique; and producing said at least one data base having data with identified alarm conditions associated therewith, allowing separation of said data with identified alarm conditions.