Blar i Institutt for havromsoperasjoner og byggteknikk på tidsskrift "IEEE Sensors Journal"

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    • Fault Detection with LSTM-Based Variational Autoencoder for Maritime Components 

      Han, Peihua; Ellefsen, Andre; Li, Guoyuan; Holmeset, Finn Tore; Zhang, Houxiang (Journal article; Peer reviewed, 2021)
      Maintenance routines on ships today follow either a reactive maintenance (RM) or preventive maintenance (PvM) approach. RM can be regarded as post-failure repair, which might create large costs. PvM uses predetermined ...

    • Fault Prognostics Using LSTM Networks: Application to Marine Diesel Engine 

      Han, Peihua; Ellefsen, Andre; Li, Guoyuan; Æsøy, Vilmar; Zhang, Houxiang (Peer reviewed; Journal article, 2021)
      Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive ...

    • A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine 

      Tian, Weiwei; Cheng, Xu; Li, Guoyuan; Shi, Fan; Chen, Shengyong; Zhang, Houxiang (Peer reviewed; Journal article, 2021)
      Blade icing detection becomes increasingly significant as it can avoid revenue loss and power degradation. Conventional methods are usually limited by additional costs, and model-driven methods heavily depend on prior ...

    • A multiple-output hybrid ship trajectory predictor with consideration for future command assumption 

      Kanazawa, Motoyasu; Skulstad, Robert; Li, Guoyuan; Hatledal, Lars Ivar; Zhang, Houxiang (Peer reviewed; Journal article, 2021)
      Onboard sensors contribute to data-driven understanding of complex and nonlinear ship dynamics in real time. By using sensors, precise ship trajectory prediction plays a key role in intelligent collision avoidance. A hybrid ...