基于AVL BOOST的船用柴油机典型故障仿真及其数据分析

AVL BOOST-based typical fault simulation and data analysis of marine diesel engine

  • 摘要:
      目的  大型船用柴油机故障类型的数据通过台架试验或者实船来获取存在许多不利因素,因此针对柴油机的故障仿真数值计算就显得尤为重要,同时对故障排除及数据驱动的智能故障诊断系统的构建也具有重要意义。
      方法  基于AVL BOOST软件和台架试验数据,建立柴油机仿真模型,验证4种负荷工况下仿真模型需满足的精度要求;基于100%负荷工况模型,采用控制变量法模拟柴油机发火点提前、单缸停油及曲轴箱窜气这些典型故障,并分析计算得到的数据。
      结果  结果表明:发火点提前5°时,缸内最高燃烧压力提高了17.4%;第1缸停缸后,有效油耗率上升近15%;对于不同气缸停油情况,第2号和3号气缸停油时的特征参数变化幅度较小;随着活塞有效窜气间隙的增加,各特征参数基本上呈线性扩大趋势,在窜气间隙值为0.04 mm时,部分特征参数急剧增加,例如油耗率增加了近40%。
      结论  所得结果可作为柴油机故障状态识别及智能故障诊断系统构建的重要依据,为探索船舶柴油机智能故障诊断技术提供新的途径。

     

    Abstract:
      Objectives  Due to many disadvantages in obtaining the fault type data of large marine diesel engine through bench testing or real ship, the numerical calculation of diesel engine fault simulation is particularly important, which is of great significance to the construction of diesel engine fault troubleshooting and data-driven intelligent fault diagnosis systems.
      Methods  Based on AVL BOOST software and bench test data, the diesel engine simulation model was established to verify that the simulation model meets the accuracy requirements under four load conditions. Based on the model of full load condition, the control variable method was used to simulate the typical faults of a diesel engine, such as combustion start angle, single cylinder stoppage and crankcase gas escape, and the calculated data results were analyzed.
      Results  The results show that, the maximum combustion pressure in the cylinder increases by 17.4% when the start angle of combustion is advanced by 5°. After No.1 cylinder is stopped, the effective fuel consumption rate increases by nearly 15%. Compared with different cylinders, the change range of the characteristic parameters of No.2, No.3 cylinders is small. With the increase of effective piston channeling air clearance, the characteristic parameters basically show a linear expansion trend. When the channeling air clearance is 0.04 mm, some characteristic parameters drastically increase, such as the fuel consumption rate increasing by nearly 40%.
      Conclusions  The results obtained by the simulation can be used as an important basis for the recognition of fault states and diesel engine intelligent fault diagnosis system construction, to explore a new approach for marine diesel engine intelligent fault diagnosis technology.

     

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