---
res:
  bibo_abstract:
  - This paper presents a comparison of a number of prognostic methods with regard
    to algorithm complexity and performance based on prognostic metrics. This information
    serves as a guide for selection and design of prognostic systems for real-time
    condition monitoring of technical systems. The methods are evaluated on ability
    to estimate the remaining useful life of rolling element bearing. Run-to failure
    vibration and temperature data is used in the analysis. The sampled prognostic
    methods include wear-temperature correlation method, health state estimation using
    temperature measurement, a multi-model particle filter approach with model parameter
    adaptation utilizing temperature measurements, prognostics through health state
    estimation and mapping extracted features to the remaining useful life through
    regression approach. Although the performance of the methods utilizing the vibration
    measurements is much better than the methods using temperature measurements, the
    methods using temperature measurements are quite promising in terms of reducing
    the overall cost of the condition monitoring system as well as the computational
    time. An ensemble of the presented methods through weighted average is also introduced.
    The results show that the methods are able to estimate the remaining useful life
    within error bounds of +-15\%, which can be further reduced to +-5\% with the
    ensemble approach.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: James Kuria
      foaf_name: Kimotho, James Kuria
      foaf_surname: Kimotho
  - foaf_Person:
      foaf_givenName: Walter
      foaf_name: Sextro, Walter
      foaf_surname: Sextro
      foaf_workInfoHomepage: http://www.librecat.org/personId=21220
  bibo_volume: 6
  dct_date: 2015^xs_gYear
  dct_language: eng
  dct_subject:
  - ensemble methods
  - combined prognostics
  - data fusion
  dct_title: Comparison and ensemble of temperature-based and vibration-based methods
    for machinery prognostics@
...