Prognostic information fusion for constant load systems

This paper describes a process for aggregating different information sources to estimate remaining equipment life. Specifically, the approach presents a rigorous chain of preprocessing, modeling and postprocessing steps that arrive at the desired prognostic result. The preprocessing steps deal with data reduction, filtering, and signature amplification. The prediction model applies adaptive neuro-fuzzy inference system (ANFIS) to the data. The post-processing steps include recursive trending which implicitly forces the prognostic trend to be confirmed before updated estimates are reported. Prognostic false positives and false negatives are introduced as innovative measures that help in assessing the performance of the approach. The method is illustrated using real-life data from industrial Web paper breakage prediction.