摘要:针对基于曲轴瞬时角加速度的一般失火故障诊断算法能诊断失火故障,但未能有效区分故障模式的缺陷,提出了一种基于做功时间和BP神经网络的失火诊断算法.根据不同模式下各缸做功时间的波动,提取诊断循环内各缸做功时间信号的特征参数,结合BP神经网络模式识别功能,实现不同模式下的失火故障诊断.通过台架试验,测试了在正常工作、第3缸单次失火、第2缸和第3缸连续失火及第3缸连续失火4种不同模式下的失火诊断情况,结果表明,这种诊断算法能够有效识别不同失火故障模式和定位失火气缸.%In view of the defect of general misfire diagnosis algorithm based on instantaneous crankshaft angular acceleration that it can diagnose misfire, but can't effectively identify misfire fault mode, a new misfire diagnosis algorithm is proposed based on the time of work and BP neural network. According to the fluctuation of the time of work in each cylinder under different modes, the characteristic parameters of the time of work signal in each cylinder in a diagnostic cycle are extracted; and combining with the identifying function of BP neural network, misfire diagnosis in different fault modes is realized. Bench tests are carried out to detect the misfire diagnosis situation at four different modes, i.e. normal operation, single misfire in 3rd cylinder, successive misfire in both 2nd and 3rd cylinders and successive misfire in 3rd cylinder. The results shows that the diagnosis algorithm presented can identify different misfire fault modes and locate the misfired cylinder effectively.