Caterpillar—an adaptive algorithm for detecting process changes from acoustic emission signals Original Research Article

An adaptive algorithm for detecting process changes in a process monitored by acoustic emission is presented as an alternative to traditional modelling techniques based on fixed or static models. This approach significantly reduces the need to remodel the process as operating conditions change. The central idea is that two moving windows are moved through the data side by side. The signal variation in one of them is modelled by a principal component analysis (PCA) model, and the samples in the other window are compared to the critical borders of the PCA model. Significant differences are interpreted as a process change, i.e. the acoustic emission from the process has changed. In this work acoustic emission data from a fluidised bed is analysed. Optimal settings for the algorithm are proposed and the robustness towards noise and other signal degradations is shown to be good. The algorithm seems to be batch independent, which means that regular re-calibration (blank runs) which is needed by reference model approaches can be avoided.