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Two improvements of similarity-based residual life prediction methods

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Abstract

The similarity-based residual life prediction (SbRLP) approach is an emerging technique and occupies a significant place in remaining useful life (RUL) prediction. Researches on (a) considering different operating conditions; and (b) considering maintenance are rare. But aforesaid factors have great influence on effective utilization of the SbRLP method. In this article, improvements are implemented from two above perspectives and thus a novel weight function and a fresh similarity measurement are advanced. Afterwards, a case study of the gyroscope’ RUL estimation demonstrates the reasonability and effectiveness of the proposed weight function and similarity measurement through comparisons with the classical SbRLP method. Meanwhile, the investigation results reveal that the performance of the SbRLP method with the recommended weight function improves fast with the increment of available reference systems, which have different operating conditions with the operating systems. And with the increase of maintenance frequency, the difference between the local performance of the SbRLP method with the introduced similarity measurement and that of the classical SbRLP method decreases gradually, which is just the opposite of the difference between their overall performances.

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, College of Mechanical Engineering, Chongqing University, Chongqing, China

    Mengyao Gu & Youling Chen

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  1. Mengyao Gu
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  2. Youling Chen
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Correspondence to Mengyao Gu.

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Gu, M., Chen, Y. Two improvements of similarity-based residual life prediction methods. J Intell Manuf 30, 303–315 (2019). https://doi.org/10.1007/s10845-016-1249-3

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  • DOI: https://doi.org/10.1007/s10845-016-1249-3

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