One of our approaches is to compute empirical correlation integrals of this multidimensional state space and compare conditions with a nonparametric test such as the Kolmogorov-Smirnov statistic. We test correlation integrals from unknown fluidization conditions against a library of correlation integrals from known fluidization conditions and obtain a quantification of and statistical confidence on the degree of similarity. Another novel test statistic examines the full geometry of the signal in state space, which yields even more precise diagnostics, at the cost of somewhat increased computational effort. For classification, we first generate a library of processed signals from a range of fluidization conditions from minimum fluidization to vigorous complex slugging. We then record new signals from several test conditions and demonstrate that the algorithms accurately classify the fluidization regime and reject incorrect alternatives at a high statistical confidence level.
Power spectra are far less useful for classification.
For diagnosis, we record differential pressure signals from the bed of PE particles with and without an agglomerated mass in the bed. We show that the advanced test detects the influence of the agglomerate at a highly significant level over a range of fluidization conditions.