Abstract The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched.

中文翻译：

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基于规则算法与BNs/BPNNs相结合的柴油机故障检测与诊断方法

摘要 柴油机的稳定运行对工业的正常生产至关重要，故障的预防、监测和识别具有重要意义。目前，对柴油机的故障研究还存在一些缺陷，如识别出的故障诊断类型较少，故障诊断的准确率还较低，故障识别定位速度固定。提出了一种将基于规则的算法与贝叶斯网络（BN）或反向传播神经网络（BPNN）相结合的柴油机故障检测和诊断方法。信号经过小波阈值去噪和集成经验模态分解处理。从分解的本征模式函数中提取信号敏感特征值。使用基于规则的算法粗略识别七个故障，并使用 BN 或 BPNN 精细识别。结果表明，当 BN 和 BPNN 的训练数据来自固定速度时，所提出的故障诊断方法在较宽的转速范围内具有良好的诊断性能。此外，还研究了分解信号的层次、传感器噪声和外部激励干扰对故障诊断性能的影响。