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 1 200 r/min,1 600 r/min,2 000 r/min and 2 400 r/min, which is better than neural network optimized by other algorithms.