甲醇-柴油双燃料发动机甲醇泄漏故障预诊断研究

甲醇作为发动机的替代燃料被广泛应用,然而甲醇腐蚀性较强,易腐蚀管路导致泄漏。针对现有发动机故障诊断系统无法预测甲醇腐蚀泄漏的问题,提出了基于经验模态分解(EMD)和萤火虫概率神经网络(FAPNN)的故障预诊断方法。对发动机供醇管道振动信号进行EMD分解,并提取能量熵作为信号特征。将能量熵矩阵输入FAPNN模型中,识别供醇管壁厚度并判断腐蚀程度。通过管壁厚度变化曲线推断出供醇管道的剩余寿命。试验结果表明,该方法能有效预测双燃料发动机甲醇泄漏故障并给出故障发生时间,在1200 r/min,1600 r/min,2000 r/min,2400 r/min 4种工况下平均准确率高达97.9%,平均运算时间仅需3.9 s,优于其他算法优化下的神经网络。

Pre-Diagnosis of Methanol Leakage Fault for Methanol-Diesel Dual Fuel Engine

Methanol is widely used as an alternative fuel for engine.However,methanol is highly corrosive and can easily corrode pipelines and cause leakage.Aiming at the problems that the existing engine fault diagnosis system could not predict methanol corrosion leakage,a fault pre-diagnosis method was proposed based on empirical mode decomposition(EMD)and firefly probabilistic neural network(FAPNN).The vibration signal of engine alcohol supply pipeline was decomposed by using EMD and the energy entropy was then extracted as the signal feature.The energy entropy matrix was further input into the FAPNN model to identify the wall thickness of alcohol supply pipeline and judge the degree of corrosion.The remaining life of alcohol supply pipeline hence could be inferred from the thickness change curve of pipe wall.The test results show that the method can effectively predict the methanol leakage failure of dual fuel engine and bring the moment of failure.The mean accuracy is 97.9%and the computing time is 3.9 s under the four conditions of 1200 r/min,1600 r/min,2000 r/min and 2400 r/min,which is better than neural network optimized by other algorithms.