A Similarity-based Prognostics Approach for Remaining Useful Life Prediction



Published Jul 8, 2014
O. F. Eker F. Camci I. K. Jennions


Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition.

How to Cite

Eker, O. F., Camci, F., & Jennions, I. K. (2014). A Similarity-based Prognostics Approach for Remaining Useful Life Prediction. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1479
Abstract 421 | PDF Downloads 398



Data-driven prognostics, anomaly detection, similarity-based modelling, multivariate analysis

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