基于DPC-GMM算法的船舶燃油系统故障诊断

Fault diagnosis of ship fuel system based on DPC-GMM algorithm

    摘要
  • 摘要:
      目的  传统的高斯混合模型（GMM）算法存在收敛速度较慢的固有缺陷，容易产生过拟合现象，导致参数计算陷入局部最优，不能很好地用于船舶燃油系统的故障诊断。
      方法  首先，分析GMM算法及参数估计算法，结合密度峰值聚类（DPC）算法，提出一种基于DPC-GMM算法的船舶燃油系统故障诊断方法；然后，通过训练船舶燃油系统状态所对应的高斯混合模型参数，实现对船舶燃油系统故障的无监督诊断；最后，基于获取的船舶燃油系统故障数据，验证该方法的有效性。
      结果  实验结果表明，采用基于DPC-GMM算法的故障辨识准确率高、识别速度快，优于传统的反向传播（BP）神经网络和支持向量机（SVM）诊断算法。
      结论  研究结果对船舶燃油系统的故障诊断有重要的指导意义。

     

    Abstract:
      Objectives  The traditional Gaussian Mixture Model(GMM)has the inherent shortcoming of slow convergence which can easily lead to over-fitting and cause the parameter calculation to fall into a local optimum. As such, it is not suitable for the fault diagnosis of marine fuel systems.
      Methods  A fault diagnosis method for a ship fuel system based on the DPC-GMM algorithm is proposed. First, the GMM and parameter estimation algorithms are analyzed. Combined with the Density Peaks Clustering(DPC) algorithm, GMM parameters corresponding to the state of the fuel system of ship are calibrated to achieve the unsupervised diagnosis of the failure of a ship's fuel system. Based on the obtained fuel system failure data, the proposed method is verified.
      Results  The experimental results show that this method has higher recognition accuracy and faster recognition speed than the traditional Back Propagation(BP)neural network and Support Vector Machine(SVM) diagnosis algorithm.
      Conclusions  The analysis results have important guiding significance for the fault diagnosis of marine fuel systems.

     

    • HTML全文
    • 参考文献(16)
    • 相关文章
    • 施引文献
  • 资源附件(0)

/

返回文章
返回