---
_id: '17810'
abstract:
- lang: eng
  text: In all fields, the significance of a reliable and accurate predictive model
    is almost unquantifiable. With deep domain knowledge, models derived from first
    principles typically outperforms other models in terms of reliability and accuracy.
    When it may become a cumbersome or an unachievable task to build or validate such
    models of complex (non-linear) systems, machine learning techniques are employed
    to build predictive models. However, the accuracy of such techniques is not only
    dependent on the hyper-parameters of the chosen algorithm, but also on the amount
    and quality of data. This paper investigates the application of classical time
    series forecasting approaches for the reliable prognostics of technical systems,
    where black box machine learning techniques might not successfully be employed
    given insufficient amount of data and where first principles models are infeasible
    due to lack of domain specific data. Forecasting by analogy, forecasting by analytical
    function fitting, an exponential smoothing forecasting method and the long short-term
    memory (LSTM) are evaluated and compared against the ground truth data. As a case
    study, the methods are applied to predict future crack lengths of riveted aluminium
    plates under cyclic loading. The performance of the predictive models is evaluated
    based on error metrics leading to a proposal of when to apply which forecasting
    approach.
author:
- first_name: Osarenren Kennedy
  full_name: Aimiyekagbon, Osarenren Kennedy
  id: '9557'
  last_name: Aimiyekagbon
- first_name: Amelie
  full_name: Bender, Amelie
  id: '54290'
  last_name: Bender
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: 'Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting
    approaches for the reliable crack length prediction of riveted aluminium plates
    given insufficient data. In: <i>PHM Society European Conference</i>. Vol 5. ;
    2020.'
  apa: Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (2020). Evaluation of time
    series forecasting approaches for the reliable crack length prediction of riveted
    aluminium plates given insufficient data. <i>PHM Society European Conference</i>,
    <i>5</i>(1).
  bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time
    series forecasting approaches for the reliable crack length prediction of riveted
    aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM
    Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
    Amelie and Sextro, Walter}, year={2020} }'
  chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation
    of Time Series Forecasting Approaches for the Reliable Crack Length Prediction
    of Riveted Aluminium Plates given Insufficient Data.” In <i>PHM Society European
    Conference</i>, Vol. 5, 2020.
  ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting
    approaches for the reliable crack length prediction of riveted aluminium plates
    given insufficient data,” in <i>PHM Society European Conference</i>, 2020, vol.
    5, no. 1.
  mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting
    Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates
    given Insufficient Data.” <i>PHM Society European Conference</i>, vol. 5, no.
    1, 2020.
  short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference,
    2020.'
date_created: 2020-08-11T13:32:40Z
date_updated: 2023-09-22T09:13:16Z
department:
- _id: '151'
intvolume: '         5'
issue: '1'
keyword:
- PHM 2019
- crack propagation
- forecasting
- unevenly spaced time series
- step ahead prediction
- short time series
language:
- iso: eng
publication: PHM Society European Conference
quality_controlled: '1'
status: public
title: Evaluation of time series forecasting approaches for the reliable crack length
  prediction of riveted aluminium plates given insufficient data
type: conference
user_id: '9557'
volume: 5
year: '2020'
...