Fault tree analysis and artificial neural network modelling for establishing a predictive ship machinery maintenance methodology

A dynamic fault tree model for a ship main engine is developed in order to analyse and identify critical systems/components of the main engine. The identified most critical systems are then used as input in an artificial neural network. An autoregressive dynamic time series neural network modelling approach is examined in a container ship case study, in order to monitor and predict future values of selected physical parameters of the most critical ship machinery equipment obtained from the fault tree analysis. The case study results of the combination of the fault tree analysis and artificial neural network model demonstrated promising prospects for establishing a dense methodology for ship machinery predictive maintenance by successfully identifying critical ship machinery systems and accurately forecasting the performance of machinery parameters

Maintenance/repair and production-oriented life cycle cost/earning model for ship structural optimisation during conceptual design stage

The aim of this paper is to investigate the effect of the change in structural weight due to optimisation experiments on life cycle cost and earning elements using the life cycle cost/earning model, which was developed for structure optimisation. The relation between structural variables and relevant cost/earning elements are explored and discussed in detail. The developed model is restricted to the relevant life cycle cost and earning elements, namely production cost, periodic maintenance cost, fuel oil cost, operational earning and dismantling earning. Therefore it is important to emphasise here that the cost/earning figure calculated through the developed methodology will not be a full life cycle cost/earning value for a subject vessel, but will be the relevant life cycle cost/earning value. As one of the main focuses of this paper is the maintenance/repair issue, the data was collected from a number of ship operators and was solely used for the purpose of regression analysis. An illustrative example for a chemical tanker is provided to show the applicability of the proposed approac

Ship machinery and equipment wireless condition monitoring system

Condition based maintenance (CBM) is a maintenance approach that has been proven to provide significant reduction in maintenance cost and associated risk. Several industries such as aerospace and power generation have incorporated CBM and have been driving the developments in progressively better diagnostic and prognostic maintenance management. Other benefits that can be linked with appropriate use of CBM include better management of the operational characteristics of the vessel, reduction of emissions and energy efficiency. However in the maritime industry this is not the case. Less than 2% of the global fleet of vessels is utilising CBM (Shorten, 2012). This can be associated with several factors that inhibit the implementation of CBM in vessels. The most important of those are the cost of installation, the capital investment in training staff and the lack of trust in the prediction capabilities of the technology. This paper presents a novel method based on wireless data transmission which can demonstrate reduced installation costs. Moreover, as part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) EU FP7 project, this paper presents a novel decision support system (DSS) solution that can be used onboard a ship with minimal initial training. The reliable user friendly graphical interface (GUI) developed in Java language provides relevant and on-time information for maintenance decision support. The combination of the developed hardware and software give a complete solution that can be applied to vessels while minimising investment costs and training

Ship machinery condition monitoring using performance data through supervised learning

This paper aims to present a methodology for intelligent monitoring of marine machinery using performance data. Monitoring of machinery condition is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. The proposed methodology will train models pertinent to specific machinery components using pre-classified performance data and then classify new data points using the models developed. For this, measurements are suitably analysed and processed to retain most of the information (variance) of the original dataset while minimising number of required dimensions. Finally, new data are compared against the models developed to evaluate their condition. The above will provide a flexible but robust framework for the early detection of emerging machinery faults. This will lead to minimisation of ship downtime and increase of the ship’s operability and income through operational enhancement. Case studies that show initial results obtained through main engine data are included

Predictive maintenance decision support system for enhanced energy efficiency of ship machinery

A decision support system (DSS) is an application that analyses data and presents results to users. DSS rapidly shift through huge amount of available data and thus allowing for faster analysis of condition monitoring data early detection of faults and improved allocation of resources. DSS can also predict and plan for future ship operators’ needs in order to optimize ship machinery operations. Such a system can provide substantial benefits to the maritime industry in terms of energy efficiency as the operation of the vessel can be optimised towards this end. As part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) EU FP7 project, this paper presents a novel DSS solution which interrogates data from dynamic condition monitoring and compares them with historic data to present decision support information onboard a ship. To provide for Condition Based inspection and criticality based maintenance for ship machinery, data is acquired and stored for analysis through the DSS. Moreover surveys involving off-line and real time on-line measurement approaches are combined to provide a more complete monitoring method. The result is a reliable user friendly graphical interface (GUI) developed in Java language that can be employed onboard any vessel and can provide relevant and on-time information. The proposed actions from the DSS target energy efficient operation and reduction of fuel consumption and ship emissions. Moreover, a major factor taken into account through the prediction mechanism of the DSS is to assist in better spare parts scheduling and prioritizing ship inspection, maintenance and repairs towards enhanced and efficient ship operations

Numerical study of a marine dual-fuel four-stroke engine

Continuously increasing environmental demands in conjunction with the planned strong penetration of the LNG, render the use of LNG as an attractive alternative marine fuel. In this framework, the traditional ship propulsion plants based on Diesel engines running with HFO, should be revisited and compared to the more efficient and environmentally friendly propulsion systems that use dual fuel engines. The present study deals with the computational investigation of a marine four-stroke dual fuel (DF) engine, in both diesel and DF mode operation. The engine model was set up in a commercial software and used to compare the performance and emissions of the investigated engine operation at steady state conditions. The engine diesel mode was initially set up and the model was calibrated to adequately represent the engine operation. Subsequently, the engine dual fuel model was set up by considering the injection of two different fuels; methane and pilot diesel fuel. The derived results were analysed for revealing the differences of the engine performance and emissions at each engine mode. In addition, the turbocharger matching at each mode is investigated revealing the challenges due to the completely different air-fuel ratio strategies used in diesel and dual fuel modes, re-spectively

Wave-induced vertical bending moment estimation by onboard tiltmeters units on container ship

Full-scale measurements in oceangoing ships have shown that the relationship between bending moment with the curvature curve of hull girder. As part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) EU FP7 project, this paper carried out an estimation of wave-induced vertical bending moment for cargo hold of the 4250 TEU container ship, based on the data of pitch angles processing from the Tiltmeter units placed on board. The results are enable to be processed to the Decision Support System (DSS), in order to assist to monitoring and risk analysis for ship structure and machinery the towards enhanced and efficient ship operations (Konstantinos, et al., 2015). The prediction values also provide a reference for the trend analy-sis of the past record signals (Ulrik Dam et al, 2015) for evaluation of longitudinal strength of container ship. The advance in different pitch angle response (deformation curvature) of hull girder can be as a development of modern decision support systems for guidance to the ship's master (Lloyd's Register, 2016

Tanker ship structural analysis for intact and damage cases

This paper presents the work carried out to assess the structural calculation of a tanker ship in intact and damage conditions, in order to know the areas of the central cargo ship exposed to greater stresses. Analysing the results obtained from the intact condition and damage conditions due to grounding. The method selected to simulate the damage conditions has been done applying a change in the mechanical properties of the material; reductions of 40, 60 and 80 % of Young Modules were applied. The validation of the results was made following the guidelines "Common Structural Rules for Bulk Carriers and Oil Tankers" from IACS. The finite element method and finite element analysis software (Ansys®) were used to analyse intact and ground-ing cases. For intact case only one scenario was done, full load condition. For grounding, three scenarios were done. The results presented correspond to the validation of the finite element model, and the results concern-ing the maximum value of Von Mises Stress for each load condition, verifying if the permissible stress has been exceeded in each of the conditions analysed

Analysis of the wave-induced vertical bending moment and comparison with the class imposed design loads for 4250 TEU container ship

The long-term predictions of vertical wave bending moment are made for the extreme design values on ship. As part of the INCASS (Inspection Capabilities for Enhanced Ship Safety), this paper carried out a short-term estimation of wave loads for 4250 TEU container ship by the hydrodynamic analysis software of ANSYS-Aqwa based on three-dimensional linear potential flow theories. Based on the short-term predic-tion and the wave statistic of the North Atlantic Ocean, a long-term prediction of vertical wave bending mo-ment is obtained. The results are required and processed to the Decision Support System (DSS), in order to assist to monitoring and risk analysis for ship structure and machinery the towards enhanced and efficient ship operations (Konstantinos, et al., 2015). The prediction values also provide a reference for the trend analysis of the past record signals (Ulrik Dam et al, 2015) for evaluation of longitudinal strength of container ship

Wind and wave directional transit time model for offshore wind operation and maintenance

Uncertainty in operation and maintenance costs of offshore renewable installations can be incurred through failure to properly account for marine conditions. One such area, vessel utilisation scheduling, requires accurate forecasts of wind and wave conditions to minimise charter costs as well as plant downtime. Additionally, fuel usage and auxiliary costs will increase with longer transfer times. Exploiting auxiliary offshore measurement data and its relation to accessibility constraints could reduce idle charter periods by allowing operatives to better anticipate prevailing site conditions. Existing models omit the effect of direction on operations and fail to account for the complex relations between dependent environmental variables which can impact on operations such as crew transfers, lifting and jacking operations. In this paper, a methodology for improving the forecasting of offshore conditions through incorporating distributed meteorological and marine observations at multiple timescales is presented. Advancing towards a demonstration of a strategic maintenance approach of this kind will assist in both reducing direct costs and associated initial project finance. The developed model will be beneficial to developers and operators as better forecasting of when conditions are suitable for maintenance could reduce costs, lost earnings and improve mobilisation of vessels and technicians