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Abstract The purpose of this study is to investigate crankcase explosion in two-stroke marine diesel engine systematically by using fault tree analysis method in fuzzy environment for system reliability and shipping sustainability. Maritime accidents might cause deadly and costly results in both human lives, commodities, environmental and financial aspects. Crankcase explosion is one of the most dangerous causalities that might possibly damage both ship structure and crew members. In this study, we determine all root causes of crankcase explosion, and calculate the probabilities in a stepwise manner by implementing field expert consultations. As a result, we find that the occurrence probability of the crankcase explosion is 1.249428E-007 which is close to real case. Bearing originated hot spots and personnel failures are also found as the most risky events. Highlights • Root causes and structure of crankcase explosion are investigated in this study. • Fuzzy fault tree analysis method is firstly applied to analyze crankcase explosion. • The probability of crankcase explosion is found as 1.25E-07. • The riskiest event chain is determined as MCS1 (BE11, UE and BE4). • Underlying reasons of crankcase explosion are explained, and suggestions are given. [ABSTRACT FROM AUTHOR]

In this study, the cyclic variability of a diesel engine using diesel fuel and butanol–diesel fuel blends is modeled using an artificial neural network (ANN) method. The engine was operated with ten different engine speeds and full load conditions using six different n-butanol–diesel fuel blends. The coefficient of variation (COV) of the indicated mean effective pressure (IMEP), which is a well-accepted evaluation method, was used to assess the cyclic variability for 100 sequential engine cycles. Results indicated that adding n-butanol to diesel fuel caused an increase. Moreover, the COV imep values exhibited a decreasing trend with an increase in the engine speed for each fuel. The experimental results were used to train the ANN. The ANN network was trained with Levenberg – Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. After training the ANN, it was found that the coefficient of determination (R 2 ) values were in the range of between 0.737 and 0.9677, the mean-absolute-percentage error (MAPE) values were smaller than 8.7 and the mean-square error values (MSE) were smaller than 0.042. The predictions of the developed ANN model showed reasonable consistency with the experimental results. [ABSTRACT FROM AUTHOR]

• Maintenance risks in a two-stroke marine diesel engine were investigated. • FAHP method was used to rank maintenance risks. • Critical maintenances and physical dimensions resulted in higher risk rankings. Maintenance activities in ship machineries are of vital importance due to economical and operational requirements. Most of ships in commercial activities have two-stroke marine diesel engines (MDEs) in the propulsion systems. Durability of the commercial activities depends on the stable operation of the main engine in the ship propulsion system. This phenomenon shows the importance of maintenance activities in the two-stroke MDEs. In this study, 46 identified maintenance activities in the two-stroke MDEs were investigated via fuzzy analytical hierarchy process (FAHP) method considering risk magnitudes. The results revealed that critical maintenance activities and physical dimensions of the parts have higher risk rankings. It was also achieved from the research that top and lowest risky maintenances are the overhaul of the turbocharger (T1) and dismantling and mounting of safety valve (C5), respectively. [ABSTRACT FROM AUTHOR]

The Baltic Dry Index (BDI) is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network (ANN) approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: (i) the past weekly observation of the BDI, (ii) the past two weekly observations of the BDI, and (iii) the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability. [ABSTRACT FROM AUTHOR]