@PHDTHESIS{Kimotho2017b,
  author = {James Kuria Kimotho},
  title = {Development and Performance Evaluation of Prognostic Approaches for
	Technical Systems},
  school = {Universit{\"a}t Paderborn},
  year = {2017},
  type = {Dissertation, SHAKER VERLAG, ISBN:978-3-8440-5447-7, Herausgeber:
	Prof.Dr.-Ing.habil Walter Sextro Paderborn},
  abstract = {As the need to improve reliability, availability and safety of technical
	systems increases, a large number of proactive maintenance strategies
	have been proposed. Of greater interest is the development of prognostic
	and health management strategies where maintenance is scheduled based
	on the current and the predicted future health state of a technical
	system. In addition, prognostic information can be used to control
	the reliability of intelligent mechatronic systems to ensure their
	mission objective is achieved. Therefore methodologies for estimating
	these current and future health states reliably and accurately are
	imperative. With the advancement in sensor technology, majority of
	the present day technical systems are installed with a network of
	sensors for condition or performance monitoring. This has led to
	the increased application of machine learning algorithms in condition
	monitoring. Depending on the sensor data available, different approaches
	for utilizing the data with machine learning algorithms can be applied.
	However, a guide for selecting the appropriate approach for a given
	system is either lacking or has not been explored extensively. Therefore,
	this work aims at providing a guide for selecting suitable approaches
	and machine learning algorithms for a given system depending on the
	available sensor data. Five approaches for prognostics and an ensemble
	of the best performing approaches are presented. Since the performance
	of machine learning algorithms is highly dependent on the input features,
	methods for feature extraction and selection are also presented.
	The approaches are evaluated and validated with run-to-failure condition
	monitoring data of actual systems.},
  keywords = {condition monitoring; prognostic and health management; feature extraction;
	machine learning},
  owner = {ekubi},
  timestamp = {2017.09.04},
  url = {https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-5447-7&search=yes}
}