@INPROCEEDINGS{Kimotho2017a,
  author = {Kimotho, James Kuria AND Tobias Hemsel AND Walter Sextro},
  title = {Estimation of Remaining Useful Lifetime of Piezoelectric Transducers
	Based on Self-Sensing},
  booktitle = {IEEE Transactions on Reliability},
  year = {2017},
  pages = {1 - 10},
  abstract = {Piezoelectric transducers are used in a wide range of applications.
	Reliability of these transducers is an important aspect in their
	application. Prognostics, which involve continuous monitoring of
	the health of technical systems and using this information to estimate
	the current health state and consequently predict the remaining useful
	lifetime (RUL), can be used to increase the reliability, safety,
	and availability of the transducers. This is achieved by utilizing
	the health state and RUL predictions to adaptively control the usage
	of the components or to schedule appropriate maintenance without
	interrupting operation. In this work, a prognostic approach utilizing
	self-sensing, where electric signals of a piezoelectric transducer
	are used as the condition monitoring data, is proposed. The approach
	involves training machine learning algorithms to model the degradation
	of the transducers through a health index and the use of the learned
	model to estimate the health index of similar transducers. The current
	health index is then used to estimate RUL of test components. The
	feasibility of the approach is demonstrated using piezoelectric bimorphs
	and the results show that the method is accurate in predicting the
	health index and RUL.},
  comment = {Design for X (DfX), Product modelling / models, Robust design, Systems
	Engineering (SE),
	
	Reliability},
  doi = {10.1109/TR.2017.2710260},
  file = {Kimotho2017preprint.pdf:Kimotho2017preprint.pdf:PDF},
  keywords = {Estimation of Remaining Useful Lifetime of Piezoelectric Transducers
	Based on Self-Sensing},
  owner = {ekubi},
  timestamp = {2017.06.26},
  url = {http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4378406}
}