针对实际应用中数据样本较少时柴油机失火故障无法准确诊断的问题，提出一种基于极限学习机（extreme learning machine，ELM）的失火故障诊断方法，并通过SC5S122D柴油机瞬时转速信号对其进行了验证。首先将采集的瞬时转速信号进行时频域特征提取，然后根据皮尔森相关系数进行特征选择，并将筛选出的特征组成特征参数集合用于柴油机失火故障诊断，最终将小样本数据集划分为4种情况并分别用于训练ELM，以此评估该方法在数据样本较少时的诊断效果。同时，对小样本数据进行扩展，并采用ELM在扩展数据集上进行柴油机的失火故障诊断。试验结果分析表明，ELM的失火故障诊断准确性、精确性、召回率和F₁值与概率神经网络（probabilistic neural network，PNN）和反向传播神经网络（back propagation neural network，BPNN）相比具有一定优越性。因此，ELM能够在数据样本不充足时对柴油机失火故障进行准确有效的诊断。

Aiming at the problem that diesel engine misfire faults couldnot be accurately diagnosed when there were fewer data samples in practical applications, a misfire fault diagnosis method based on extreme learning machine (ELM) was proposed, which was verified by SC5S122D instantaneous speed signal of diesel engine. The acquired instantaneous speed signal was extracted in the time-frequency domain, and then the feature selection was carried out according to the Pearson correlation coefficient, and the feature parameter set composed of selected feature was used to diagnose the diesel engine misfire fault. Finally, the small sample dataset was divided into four cases and used to train ELM to evaluate the diagnostic effect of ELM when the data sample was small. At the same time, the small sample data was expanded, and ELM was used to diagnose diesel engine misfire faults on the expanded dataset. Analysis of the experimental results demonstrates that the ELM exhibits superior performance in misfire fault diagnosis compared to both PNN and BPNN, as evidenced by higher accuracy, precision, recall, and F₁ score metrics. Consequently, ELM proves capable of achieving accurate and effective diesel engine misfire diagnosis even with limited training data samples.