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@PHDTHESIS{LeamanWeiffenbach:794171,
      author       = {Leaman Weiffenbach, Felix Alberto},
      othercontributors = {Nienhaus, Karl and Seeliger, Andreas},
      title        = {{C}ontributions to the diagnosis and prognosis of ring gear
                      faults of planetary gearboxes using acoustic emissions; 1.
                      {A}uflage},
      volume       = {101},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Stolberg},
      publisher    = {Verlag R. Zillekens},
      reportid     = {RWTH-2020-07453},
      isbn         = {978-3-941277-42-7},
      series       = {Aachener Schriften zur Rohstoff- und Entsorungstechnik des
                      Instituts für Maschinentechnik der Rohstoffindustrie},
      pages        = {1 Online-Ressource (xiv, 156 Seiten) : Illustrationen,
                      Diagramme},
      year         = {2020},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2020},
      abstract     = {Despite the progress made in the last decades in the field
                      of machine condition monitoring, there are still cases where
                      the current state of the art is not enough and new
                      technologies and advanced analysis methods are required to
                      prevent failures. One example are planetary gearboxes (PGs),
                      which are one of the main powertrain components of heavy
                      machinery such as off-highway trucks, electric rope shovels,
                      helicopters and wind turbines. Although those systems are
                      most of the time equipped with vibration, temperature and
                      other sensors to detect faults in mechanical components,
                      these technologies might not be able to perform well under
                      certain circumstances. Therefore, the applied investigation
                      on new monitoring technologies and methods in the field of
                      machine health management is a necessary step. In this work
                      the use of the acoustic emission (AE) technology for the
                      fault diagnosis and prognosis of PGs is addressed. Different
                      signal processing methods are presented and their potential
                      for the analysis of AE signals is discussed. They include
                      methods in the time domain, frequency domain and
                      time-frequency domain such as calculation of statistical
                      features, detection of AE bursts, envelope analysis,
                      empirical mode decomposition (EMD), cyclostationarity and
                      the wavelet transform (WT). The methods are tested with
                      experimental data measured on different PGs, including not
                      only laboratory measurements but also measurements on wind
                      turbine gearboxes in field. Two types of ring gear faults
                      are analyzed, which are worn and cracked teeth. Regarding
                      the fault diagnosis of a worn tooth, the results indicated
                      that it can be detected by the analysis of the envelope
                      spectrum in account of the amplitude modulations that it
                      produces in the measured AE signals. However, due to the
                      complexity of the AE signals those amplitude modulations can
                      be only properly revealed by the use of complementary signal
                      processing techniques to enhance the signal-to-noise ratio.
                      A case study regarding the prognosis of this fault is also
                      presented. Here, a novel feature based on a relative
                      counting of the AE bursts is proposed and its forecasting is
                      carried out by means of a genetically-optimized artificial
                      neural network (ANN).Regarding the fault diagnosis of a
                      cracked tooth, no clear results were obtained with the same
                      approach used for the worn tooth. For this case, a concept
                      based on the the characterization of the AE bursts with
                      respect to their shape and main frequency proved to be
                      successful. An important aspect of the proposed
                      characterization methodology was the calculation of the main
                      frequency of the bursts. Here, due to the overlapping of
                      normal bursts originated from teeth contact and abnormal
                      bursts originated from crack growth, only the wavelet packet
                      decomposition (WPD) could achieve appropriate time and
                      frequency resolutions to perform this task.The results
                      obtained in this research work constitute the basis for the
                      analysis of AE signals to detect faults in PGs. At the end
                      of the work, recommendations for the development of a
                      reliable condition monitoring system based on AE signals are
                      also given.},
      cin          = {513310 / 510000},
      ddc          = {620},
      cid          = {$I:(DE-82)513310_20180515$ / $I:(DE-82)510000_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2020-07453},
      url          = {https://publications.rwth-aachen.de/record/794171},
}