Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single-channel data. However, single-channel vibration signal cannot reflect the operating information of the diesel engine comprehensively because diesel engine vibration is coupled by multiple source signals. This paper proposes the MVMD band energy method for fault diagnosis by four channels of vibration signals. First, the original multivariate signals are decomposed adaptively by MVMD, which obtains a series of components with modal alignment. Then, the band energy values of each measuring point are calculated as the fault characteristics. Finally, SVM is used to realize the diagnosis and identification of diesel engine misfire. The working conditions have a great influence on the vibration signal of the cylinder. In order to obtain the best diagnostic working conditions, six working conditions are set for testing. The result shows that the fault identification rate is highest under the 1500 rpm and 50% load working condition. The fault recognition rate of this method reaches more than 99%, which is superior to the other four common methods.

中文翻译：

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基于MVMD和带能的柴油机故障诊断新方法

振动信号作为柴油机状态检测和故障诊断的重要手段，已引起人们的广泛关注。在传统的振动信号分析中，大多数处理方法都是针对单通道数据的。但是，单通道振动信号不能完全反映柴油机的运行信息，因为柴油机振动是由多个源信号耦合而成的。提出了通过四通道振动信号进行故障诊断的MVMD带能量方法。首先，通过MVMD自适应地分解原始的多元信号，从而获得一系列具有模态对齐的分量。然后，将每个测量点的带能量值计算为故障特性。最后，利用支持向量机实现柴油机失火的诊断与识别。工作条件对气缸的振动信号有很大的影响。为了获得最佳的诊断工作条件，设置了六个工作条件进行测试。结果表明，在1500 rpm和50％负载工作条件下，故障识别率最高。该方法的故障识别率达到99％以上，优于其他四种常用方法。