Multi-energy hybrid ships are compatible with multiple forms of new energy, and have become one of the most important directions for future developments in this field. A propulsion inverter is an important component of a hybrid DC electrical system, and its reliability has great significance in terms of safe navigation of the ship. A fault diagnosis method based on one-dimensional convolutional neural network (CNN) is proposed that considers the mutual influence between an inverter fault and a limited ship power grid. A tiled voltage reduction method is used for one-to-one correspondence between the inverter output voltage and switching combinations, followed by a combination of a global average pooling layer and a fully connected layer to reduce the model overfitting problem. Finally, fault diagnosis is verified by a Softmax layer with good anti-interference performance and accuracy.
The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.
Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.
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