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1
Content available Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis
Shi Qingguo, Hu Yihuai, Yan Guohua
Polish Maritime Research
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2023
|
nr 1
98--111
EN
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.
2
Content available Fault diagnosis of me marine diesel engine fuel injector with novel IRCMDE method
Shi Qingguo, Hu Yihuai, Yan Guohua
Polish Maritime Research
|
2023
|
nr 3
96--110
EN
As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter (FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.
3
Content available Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN
Yan Guohua, Hu Yihuai, Shi Qingguo
Polish Maritime Research
|
2023
|
nr 3
132--141
EN
Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.
4
Content available Experimental study of fuel combustion and emission characteristics of marine diesel engines using advanced fuels
Changxiong Li, Hu Yihuai, Yang Zy, Guo Hao
Polish Maritime Research
|
2023
|
nr 3
48--58
EN
In order to explore the potential application of oxygenated fuels, polyoxymethylene dimethyl ethers (PODE), as an alternative fuel for marine diesel engines, the fuel combustion performance and gas emission characteristics of pure diesel oil, diesel-blended PODE, and pure PODE were tested on a marine diesel engine under different running conditions. The experimental results indicate that oxygen consumption can be reduced by diesel-blended PODE and pure PODE. The in-cylinder pressure and exothermic curve were consistent with the trend of diesel oil. Also, the ignition delay of diesel-blended PODE and pure PODE decreased, and the diffusion rate was accelerated, which helped to improve the combustion performance of diesel engines. Diesel blended PODE and pure PODE reduced the particulate matter (PM) emissions by up to 56.9% and 86.8%, respectively, and CO emissions by up to 51.1% and 56.3%, respectively. NOx emissions were gradually decreased with engine load. CO2 emissions were slightly increased, and the effective fuel consumption was increased up to 48% and 132%, respectively. It was shown that PODE could provide comparable power in a marine diesel engine and improve the fuel combustion and gas emission of the engine as a clean alternative fuel for marine diesel engines.
5
Content available Deep learning-based fault diagnosis for marine centrifugal fan
Li Congyue, Hu Yihuai, Jiang Jiawei, Yan Guohua
Polish Maritime Research
|
2023
|
nr 1
112--120
EN
Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudocolour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.
6
Content available A novel fault diagnosis method for marine blower with vibration signals
Yan Guohua, Hu Yihuai, Jiang Jiawei
Polish Maritime Research
|
2022
|
nr 2
77--86
EN
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.
7
Content available A convolutional neural network-based method of inverter fault diagnosis in a ship’s DC electrical system
Yan Guohua, Hu Yihuai, Shi Qingguo
Polish Maritime Research
|
2022
|
nr 4
105--114
EN
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.
8
Content available Wind tunnel experiment of multi-mode arc sail device
Zhang Huawu, Hu Yihuai, He Jianhai
Polish Maritime Research
|
2021
|
nr 4
20--29
EN
A ship’s wind energy utilization device with multi-mode arc-shaped sails is designed, which have different working modes for sail-assisting or wind power generation according to the ship’s navigation. The structural characteristics and working principles of this device are firstly described in this paper. Three sets of arc-shaped sails with different thickness (4.5 cm, 11.3 cm, 21.7 cm) were designed. Wind tunnel experiments were carried out in the respects of sail-assisting performance and wind-power generation to determine the best sail blade shape and to verify the energy-saving effect of this device. Experiments show that the sail with the smallest thickness (4.5 cm) has a better boosting effect than others, and the sail with the largest thickness (21.7 cm) has the best wind power generation performance. Considering the lateral force and the structural strength of the support, in the case of the comprehensive evaluation for the boosting and power generation performance, it is considered that the intermediate thickness (11.3 cm) is the best choice. The device has a good comprehensive energy utilization effect and has development and application value.
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