Stress prediction of marine diesel engine crankshaft based on digital twin technology

  Objectives  A crankshaft is a key component of marine diesel engines. In order to monitor crankshaft stress and ensure the safe and reliable operation of ships, a crankshaft stress monitoring method based on digital twin technology is proposed.

  Methods  Based on the digital twin concept, a framework for the intelligent operation and maintenance of marine diesel engines is put forward. Taking a certain type of in-line 6-cylinder marine medium-speed diesel engine as the research object, a digital crankshaft health condition assessment system is developed on the basis of available data from real engines. First, based on an RBF neural network, the cylinder head vibration acceleration signal is used to identify in-cylinder pressure, then the load of the crank pin is calculated. Based on the finite element method, the key positions which affect the lifecycle of the crankshaft are obtained after analyzing its stress and fatigue. A BP neural network is used to evaluate the stress of the crankshaft after a reduced-order model of forces and stresses is used to determine real-time performance. A BP neural network is then used to evaluate the stress of the crankshaft.

  Results  The prediction errors of the cylinder pressure and crankshaft stress of the proposed model are both less than 5%, and the stress prediction time is shortened to the second level, realizing the real-time/quasi-real-time update of the twin model.

  Conclusions  The results of this study show that it can be used as a new reference for the real-time monitoring and intelligent operation and maintenance of diesel engine components.