Similarity based method for manufacturing process performance prediction and diagnosis

Authors:
Jianbo Liu;Dragan Djurdjanovic;Jun Ni;Nicolas Casoetto;Jay Lee
Affiliations:
University of Michigan, Department of Mechanical Engineering, 2350 Hayward Street, Ann Arbor, MI 48109-2125, United States;University of Michigan, Department of Mechanical Engineering, 2350 Hayward Street, Ann Arbor, MI 48109-2125, United States;University of Michigan, Department of Mechanical Engineering, 2350 Hayward Street, Ann Arbor, MI 48109-2125, United States;University of Michigan, Department of Mechanical Engineering, 2350 Hayward Street, Ann Arbor, MI 48109-2125, United States;University of Cincinnati, Department of Mechanical, Industrial and Nuclear Engineering, 598 Rhodes Hall, P.O. Box 210072, Cincinnati, OH 45221-0072, United States
Venue:
Computers in Industry
Year:
2007

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Abstract

Full realization of all the potentials of predictive maintenance highly depends on the accuracy of long-term predictions of the remaining useful life of manufacturing equipments. In this paper, we propose a new method that is capable of achieving high long-term prediction accuracy by comparing signatures from any two degradation processes using measures of similarity that form a match matrix (MM). Through this concept, we can effectively include large amounts of historical information into the prediction of the current degradation process. Similarities with historical records are used to generate possible future distributions of features indicative of process performance, which are then used to predict the probabilities of failure over time by evaluating overlaps between predicted feature distributions and feature distributions related to unacceptable equipment behavior. The analysis of experimental results shows that the proposed method can yield a noticeable improvement of long-term prediction accuracy in terms of mean prediction errors over the Elman Recurrent Neural Network (ERNN) based prediction, which was shown in the past literature to predict well behavior of highly non-linear and non-stationary time series.