---
res:
  bibo_abstract:
  - "\r\nPredictive Maintenance as a desirable maintenance strategy in industrial
    applications relies on suitable condition monitoring solutions to reduce costs
    and risks of the monitored technical systems. In general, those solutions utilize
    model-based or data-driven methods to diagnose the current state or predict future
    states of monitored technical systems. However, both methods have their advantages
    and drawbacks. Combining both methods can improve uncertainty consideration and
    accuracy. Different combination approaches of those hybrid methods exist to exploit
    synergy effects. The choice of an appropriate approach depends on different requirements
    and the goal behind the selection of a hybrid approach.\r\n\r\n \r\n\r\nIn this
    work, the hybrid approach for estimating remaining useful lifetime takes potential
    uncertainties into account. Therefore, a data-driven estimation of new measurements
    is integrated within a model-based method. To consider uncertainties within the
    system, a differentiation between different system behavior is realized throughout
    diverse states of degradation.\r\n\r\nThe developed hybrid prediction approach
    bases on a particle filtering method combined with a machine learning method,
    to estimate the remaining useful lifetime of technical systems. Particle filtering
    as a Monte Carlo simulation technique is suitable to map and propagate uncertainties.
    Moreover, it is a state-of-the-art model-based method for predicting remaining
    useful lifetime of technical systems. To integrate uncertainties a multi-model
    particle filtering approach is employed. In general, resampling as a part of the
    particle filtering approach has the potential to lead to an accurate prediction.
    However, in the case where no future measurements are available, it may increase
    the uncertainty of the prediction. By estimating new measurements, those uncertainties
    are reduced within the data-driven part of the approach. Hence, both parts of
    the hybrid approach strive to account for and reduce uncertainties.\r\n\r\n \r\n\r\nRubber-metal-elements
    are employed as a use-case to evaluate the developed approach. Rubber-metal-elements,
    which are used to isolate vibrations in various systems, such as railways, trucks
    and wind turbines, show various uncertainties in their behavior and their degradation.
    Those uncertainties are caused by diverse inner and outer factors, such as manufacturing
    influences and operating conditions. By expert knowledge the influences are described,
    analyzed and if possible reduced. However, the remaining uncertainties are considered
    within the hybrid prediction method. Relative temperature is the selected measurand
    to describe the element’s degradation. In lifetime tests, it is measured as the
    difference between the element’s temperature and the ambient temperature. Thereby,
    the influence of the ambient temperature on the element’s temperature is taken
    into account. Those elements show three typical states of degradation that are
    identified within the temperature measurements. Depending on the particular state
    of degradation a new measurement is estimated within the hybrid approach to reduce
    potential uncertainties.\r\n\r\nFinally, the performance of the developed hybrid
    method is compared to a model-based method for estimating the remaining useful
    lifetime of the same elements. Suitable performance indices are implemented to
    underline the differences between the results.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Amelie
      foaf_name: Bender, Amelie
      foaf_surname: Bender
      foaf_workInfoHomepage: http://www.librecat.org/personId=54290
  - foaf_Person:
      foaf_givenName: Walter
      foaf_name: Sextro, Walter
      foaf_surname: Sextro
      foaf_workInfoHomepage: http://www.librecat.org/personId=21220
  bibo_doi: 'https://doi.org/10.36001/phme.2021.v6i1.2843 '
  bibo_issue: '1'
  bibo_volume: 6
  dct_date: 2021^xs_gYear
  dct_language: eng
  dct_subject:
  - Hybrid prediction method
  - Multi-model particle filtering
  - Uncertainty quantification
  - RUL estimation
  dct_title: Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering
    Uncertainties@
...