A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.

中文翻译：

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基于瞬态综合特征的随机森林技术的三相PWM整流器故障诊断数据驱动设计

当绝缘栅双极型晶体管（IGBT）发生开路故障时，三相脉冲宽度调制（PWM）整流器通常可以保持运行，这将导致系统不稳定和不安全。针对该问题，本研究提出了一种基于瞬态综合特征的随机森林，提出一种基于数据的在线故障诊断方法，能够及时有效地定位IGBT的开路故障。首先，通过分析三相PWM整流器中IGBT的开路故障特征，发现故障特征的发生与故障位置和时间有关，并且随着故障的发生。其次，对不同数据驱动的故障诊断方法进行比较和评估，随机森林算法的性能优于支持向量机或人工神经网络。同时，瞬时综合特征训练的故障诊断分类器的准确性高于原始特征训练的分类器的准确性。另外，由乘法特征训练的随机森林故障诊断分类器最好，故障诊断准确率可达98.32％。最后，进行了在线故障诊断实验，结果证明了该方法的有效性，该方法可以准确定位IGBT中的开路故障，同时确保系统安全。乘性特征训练的随机森林故障诊断分类器最好，故障诊断准确率可达98.32％。最后，进行了在线故障诊断实验，结果证明了该方法的有效性，该方法可以准确定位IGBT中的开路故障，同时确保系统安全。乘性特征训练的随机森林故障诊断分类器最好，故障诊断准确率可达98.32％。最后，进行了在线故障诊断实验，结果证明了该方法的有效性，该方法可以准确定位IGBT中的开路故障，同时确保系统安全。