Adaptive learning for event modeling and characterization

作者:

Highlights:

摘要

Adaptive learning of specific patterns or events of interest has been an area of significant research for various applications in the last two decades. In developing diagnostic evaluation and safety monitoring applications of a propulsion system, it is critical to detect, characterize and model events of interest. It is a challenging task since the detection system should allow adaptive characterization of potential events of interest and correlate them to learn new models for future detection for online health monitoring and diagnostic evaluation. In this paper, a novel framework is established using a hierarchical adaptive clustering approach with fuzzy membership functions to characterize specific events of interest from the measured and processed features. Raw engine measurement data is first analyzed using the wavelet transform to provide features for localization of frequency information for use in the classification system. A method combining hierarchical and fuzzy k-means clustering is then applied to a set of selected measurements and computed features to determine the events of interest during engine operations. Experimental results have shown that the proposed approach is effective and computationally efficient to detect, characterize and model new events of interest from data collected through continuous operations.

论文关键词:Hierarchical clustering,Fuzzy k-means clustering,Adaptive learning,Event modeling

论文评审过程:Received 27 December 2005, Revised 27 July 2006, Accepted 15 September 2006, Available online 15 November 2006.

论文官网地址:https://doi.org/10.1016/j.patcog.2006.09.018