@INPROCEEDINGS{Kimotho2014,
  author = {Kimotho, James Kuria AND Sextro, Walter},
  title = {An approach for feature extraction and selection from non-trending
	data for machinery prognosis},
  booktitle = {Proceedings of the Second European Conference of the Prognostics
	and Health Management Society 2014},
  year = {2014},
  volume = {5},
  abstract = {With the paradigm shift towards prognostic and health management (PHM)
	of machinery, there is need for reliable PHM methodologies with narrow
	error bounds to allow maintenance engineers take decisive maintenance
	actions based on the prognostic results. Prognostics is mainly concerned
	with the estimation of the remaining useful life (RUL) or time to
	failure (TTF). The accuracy of PHM methods is usually a function
	of the features extracted from the raw data obtained from sensors.
	In cases where the extracted features do not display clear degradation
	trends, for instance highly loaded bearings, the accuracy of the
	state of the art PHM methods is significantly affected. The data
	which lacks clear degradation trend is referred to as non-trending
	data. This study presents a method for extracting degradation trends
	from non-trending condition monitoring data for RUL estimation. The
	raw signals are first filtered using a discrete wavelet transform
	(DWT) denoising filter to remove noise from the acquired signals.
	Time domain, frequency domain and time-frequency domain features
	are then extracted from the filtered signals. An autoregressive model
	is then applied to the extracted features to identify the degradation
	trends. Features representing the maximum health information are
	then selected based on a performance evaluation criteria using extreme
	learning machine (ELM) algorithm. The selected features can then
	be used as inputs in a prognostic algorithm. The feasibility of the
	method is demonstrated using experimental bearing vibration data.
	The performance of the method is evaluated on the accuracy of RUL
	estimation and the results show that the method can be used to accurately
	estimate RUL with a maximum error of 10\%.},
  bdsk-url-1 = {https://www.phmsociety.org/node/1201},
  file = {Kimotho2014.pdf:Kimotho2014.pdf:PDF},
  keywords = {autoregressive model ELM feature extraction feature selection non-trending
	Remaining useful Life},
  owner = {tobiasm},
  timestamp = {2014.07.14},
  url = {https://www.phmsociety.org/node/1201}
}