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@ARTICLE{Zhao:840134,
      author       = {Zhao, Xingwang and Xu, Liangfei and Li, Jianqiu and Fang,
                      Chuan and Ouyang, Minggao},
      title        = {{F}aults diagnosis for {PEM} fuel cell system based on
                      multi-sensor signals and principle component analysis
                      method},
      journal      = {International journal of hydrogen energy},
      volume       = {42},
      number       = {29},
      issn         = {0360-3199},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2017-07694},
      pages        = {18524 - 18531},
      year         = {2017},
      abstract     = {Fuel cell vehicles are becoming more popular and attracting
                      more attention from industries, but stability and
                      reliability of the fuel cell system (FCS) are still problems
                      for its commercial progress. Therefore, a fault diagnosis
                      system is essential for a reliable and long working lifetime
                      FCS. In this work, a fault diagnosis method based on
                      multi-sensor signals and principle component analysis (PCA)
                      is proposed to improve FCS performance. By using this
                      method, the correlation among different sensor signals are
                      analyzed based on multi-sensor signals, and a simplified
                      statistic index for fault diagnosis is deduced based on the
                      PCA. The FCS operation conditions are monitored online, and
                      faults in sensor and system levels are diagnosed.
                      Experimental results show that, two typical fault scenarios,
                      i.e., a single sensor fault and a serious system failure,
                      can be successfully diagnosed and distinguished. For the
                      single sensor fault, the sensor signal is reconstructed
                      immediately to ensure that fuel cell vehicles operate
                      normally. For the system failure, the fault can be detected
                      in 17 s and the fault source signals can be located in 31 s,
                      so the fuel cell stack can be protected timely. The main
                      contribution of this work is to deduce a simplified
                      statistic index for fault diagnosis based on multi-sensor
                      signals and PCA method, and to provide an experimental study
                      on identifying faults in sensor and system levels of a PEM
                      fuel cell system.},
      cin          = {IEK-3},
      ddc          = {660},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {135 - Fuel Cells (POF3-135)},
      pid          = {G:(DE-HGF)POF3-135},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000407657900047},
      doi          = {10.1016/j.ijhydene.2017.04.146},
      url          = {https://juser.fz-juelich.de/record/840134},
}