@INPROCEEDINGS{Kimotho2015,
  author = {Kimotho, James Kuria AND Sextro, Walter},
  title = {Comparison and ensemble of temperature-based and vibration-based
	methods for machinery prognostics},
  booktitle = {Annual Conference of the Prognostics and Health Management Society
	2015},
  year = {2015},
  volume = {6},
  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.},
  bdsk-url-1 = {http://www.phmsociety.org/node/1781/},
  file = {Kimotho2014.pdf:Kimotho2015.pdf:PDF},
  keywords = {ensemble methods,combined prognostics,data fusion},
  url = {https://www.phmsociety.org/node/1781}
}