Ship Machinery Systems Research Articles

A hybrid predictive maintenance approach for ship machinery systems: a case of main engine bearings

A hybrid maintenance strategy is proposed, combining reliability-centered and condition-based maintenance strategies to prevent engine failures. A case study on the lubrication deficiency of a ship's main engine bearings was conducted. The reliability-centered maintenance phase is based on reliability-increasing rules, equipment facts, and a knowledge base. Then, a strategy is developed where system parameters are continuously monitored. These parameters are evaluated in the inference engine, and maintenance recommendations are provided to the ship operator. The investment cost of th developed system is discussed using a real engine failure case. The study shows that the strategy can improve maintenance on existing ships and system reliability at low costs. The methodology offers a cost-effective approach for the sustainability of the main engine, as demonstrated by a real crankshaft failure report. Unlike existing practices, it provides influential maintenance decisions based on evaluating system parameters within four operation ranges. Simulations show it detects insufficient lubrication faults at low engine loads, superior to current applications. The proposed approach suits different types of existing and newly built ships, engines like dual-fuel, and technologies such as semi-autonomous ships.

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships’ (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship’s configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems’ availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

Design of a decision support system to achieve condition-based maintenance in ship machinery systems

A decision support system is proposed for the condition-based maintenance of ship machinery systems based on the adaptive neuro-fuzzy inference system (ANFIS) approach. A case study is conducted for a container ship's main diesel engine. Within the scope of the methodology, the main engine power is predicted based on exhaust gas outlet temperatures of cylinders and the main engine shaft RPM. In this regard, firstly, two different strategies such as the creation of the cylinder-basis model and the development of the overall system model are developed to determine the ANFIS model structure for the analysis. Then, comparative analyses are carried out to select a suitable ANFIS structure and its specific membership functions. In addition, the estimation process is also performed by the artificial neural network (ANN) model, and its results are compared with the findings of the best ANFIS structure. The success of the constructed models is evaluated by some error metrics. The overall ANFIS model with 5 membership functions is determined as the best approach by scores of 0.9806 for R2, 1.6588 MW for RMSE, and 3.2703 for MAPE. As a result of the estimation procedure, a decision support system to assist marine operators in maintenance operations is developed. The proposed strategy can be applied to different types of systems in the ship engine room such as the fuel oil system and can be improved by including more parameters obtained from the system's significant points in the analysis.

Review of maintenance strategies for ship machinery systems

This study presents the state-of-art and future trends of the maintenance approaches to ship machinery systems. A systematic review process is conducted to identify relevant literature. Firstly, literature is examined by bibliometric analyses based on the determination of the distribution of papers by years, the top authors, papers, journals, and organisations. Then, studies in the literature are reviewed and presented. In addition, the advantages and difficulties of each maintenance strategy are discussed. Finally, an effective maintenance policy that can implement in new ship concepts such as autonomous marine vessels is introduced in line with the information from the literature. In the future, it is believed that maintenance practices will be performed via the condition-based approach when the evolution of followed maintenance operations in ship machinery systems is examined. However, it should be underlined that more effective and powerful maintenance management can be achieved by supporting the system performance monitoring with reliability and risk approaches.

Risk assessment of sea chest fouling on the ship machinery systems by using both FMEA method and ERS process

ABSTRACT Sea pollution has negative consequences and has an impact on the marine ecosystem and ship machinery processes. The main risk of sea pollution on ship is based on sea chests that are used for ballast water and firefighting. In addition to this, sea chest fouling, which primarily forms as a result of pollution, has an impact on the ship machinery, navigation in waterways, and the environment. The possibility of failure related to sea chest fouling issues in seawater cooling systems used to cool ship machinery parts were investigated in this study. Significant defects in the cooling and related engine systems were determined using both a full-mission Kongsberg engine room simulator (ERS) process and failure modes and effects (FMEA) technique, which relied on expert judgments within the cause–effect relationship. According to the findings, sea chest pollution has a direct impact on the ships’ cooling water systems and other related components, causes power losses, and leads the main engine to shut down, resulting in the ship losing maneuverability. As a result of these circumstances, the risk of catastrophic events such as grounding, contact, collision, flooding, fire explosion, and others was determined.

Fault Analysis of Ship Machinery Using Machine Learning Techniques

Maintenance and repair of ship systems and prediction of fault probability have become an important issue for dynamic ship systems recently. Increasing the usability of systems by detecting the fault analysis is one of the current work areas. In recent years, the rapid development of information technologies and the machine learning approaches developing accordingly have made it possible to integrate machine learning techniques into ship systems. The use of machine learning in the studies has enabled this method to be tested in the areas of maintenance, repair, and fault analysis. To be able to predict the fault that may occur in the ship machinery systems and prevent the fault accordingly, can increase the lifespan of ship machinery's. In this study, the data obtained from an LM-2500 type ship engine were analyzed by regression and Artificial Neural Networks (ANN) algorithms to predict the fault of ship machinery. The results were compared for linear regression, decision tree regression, k nearest neighbours’ regression, random forest regression, bayesian ridge regression, extra tree regression, linear SVR regression and ANN algorithms. As a result of the analysis, it was revealed that the ANN method gave better results in machine fault prediction compared to the regression methods.

Comparative Analysis of Linear and Non-Linear Programming Techniques for the Optimization of Ship Machinery Systems

The selection of a proper machinery system is one of the primary decisions to be taken during the ship design phase. Nonetheless, this selection is made challenging by the presence of a variety of alternatives, and by the limited data availability at the early stages of the design phase. An optimization framework is presented in this paper, supporting decision making at the earliest stages of the ship design process. The framework is suitable to perform the screening and the selection of optimal machinery configurations for a predefined ship operational profile, and it includes both linear and non-linear optimization routines. The results of the linear and the non-linear approaches are compared, and indications on what conditions are the most suitable for the application of one or the other approach are provided. Both approaches are tested for two case studies, a bulk carrier and a small cruise ship. The results indicate that both optimization approaches lead to the same layout of the machinery system, but to slightly different unit scheduling. This suggests that the use of the linear approach is suitable for design purposes, but less appropriate for operational optimization. In addition, the findings of the work suggest that the trade-off between fuel consumption and volume of the engines should be considered when selecting the machinery system for a ship.

Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine

Sensor data from marine engine systems can be used to detect changes in performance in near real time which may be indicative of an impending failure. Thus sensor-based condition monitoring can be important for the reliability of ship machinery systems and improve maritime safety. However, there is a need for efficient and robust algorithms to detect such changes in the data streams. In this paper, sensor data from a marine diesel engine on an ocean-going ship are used for anomaly detection. The focus is on unsupervised methods based on clustering and the idea is to identify clusters in sensor data in normal operating conditions and to assess whether new observations belong to any of these clusters. The anomaly detection methods presented in this paper are applied to sensor data with no known faults. Being fully unsupervised, however, they do not rely on the assumption that all measurements are fault free as long as the amount of faulty data is small. The methods explored in this study include K-means clustering, Mixture of Gaussian models, density-based clustering, self-organising maps and support vector machines. These could be used separately or in combination to provide an efficient initial screening of the data and decide whether more detailed analysis is required. The performance of the various methods is generally found to be good, also in comparison with other methods. Overall, cluster-based methods are found to be promising candidates for online anomaly detection and condition monitoring of ship machinery systems based on sensor data.

Determining shipboard integration requirements of maintenance 4.0 concept in marine engineering

Ship machinery maintenance is a core technical aspect to achieve expected reliability, availability and efficiency in system level. Since maintenance practiceon-board ships is so critical, the integrity of planning, coordination and execution stages are expected to be well functioned. At this insight, this study considers the implementation potential of maintenance 4.0, relatively a new concept defined within industrial 4.0 initiative, in marine engineering field. In detail, the generic design and implementation requirements of maintenance 4.0 including key opportunities and challenges are identified. Moreover, a comprehensive requirement analysis is conducted to adopt maintenance 4.0 concept into ship machinery systems of existing and newbuilding merchant ships.

Exergy analysis and optimisation of a marine molten carbonate fuel cell system in simple and combined cycle configuration

In this paper we present the exergy analysis and design optimisation of an integrated molten carbonate fuel cell (MCFC) system for marine applications, considering waste heat recovery options for additional power production. High temperature fuel cells are attractive solutions for marine energy systems, as they can significantly reduce gaseous emissions, increase efficiency and facilitate the introduction of more environmentally-friendly fuels, like LNG and biofuels. We consider an already installed MCFC system onboard a sea-going vessel, which has many tightly integrated sub-systems and components: fuel delivery and pre-reforming, internal reforming sections, electrochemical conversion, catalytic burner, air supply and high temperature exhaust gas. The high temperature exhaust gasses offer significant potential for heat recovery that can be directed into both covering the system’s auxiliary heat requirements and power production. Therefore, an integrated systems approach is employed to accurately identify the true sources of losses in the various components and to optimise the overall system with respect to its energy efficiency, taking into account the various trade-offs and subject to several constraints. Here, we present a four-step approach: a. dynamic process models development of simple and combined-cycle MCFC system; b. MCFC components and system models calibration via onboard MCFC measurements; c. exergy analysis, and d. optimisation of the simple and combined-cycle systems with respect to their exergetic performance. Our methodology is based on the thermofluid and chemical reactions modelling of each component, via our in-house ship machinery systems modelling framework, DNVGL COSSMOS. For the major system components spatially distributed exergy balances are considered in order to capture the coupling of the local process phenomena and exergy destruction with component design characteristics. Exhaust heat recovery is considered using a steam turbine combined-cycle module integrated with the rest of the MCFC system. Both the simple and combined cycle MCFC systems are optimised with respect to their overall exergetic efficiency subject to design, technical, operational and space constraints. The exergy analysis identified and ranked the sources of exergy destruction and the subsequent optimisation yielded significant improvement potential for both systems. The simple MCFC system optimisation yielded an exergy efficiency improvement of 7% with 5% more power produced. Heat recovery in the combined cycle MCFC resulted in 40% more power produced, with a 60% overall exergy efficiency (relative increase of 45%). Both MCFC systems outperform conventional dual-fuel engines with respect to efficiency, having also a positive impact on CO2 emissions with a relative reduction of about 30%.

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.

Structuring ship design project approval mechanism towards installation of operator–system interfaces via fuzzy axiomatic design principles

Managing the verification of primary design projects for ship machinery systems is one of the crucial stages in ship building processes. In particular, the design of operator–system interfaces such as remote controls, displays, alarms, workstations, and labels requires a high level of technical expertise and systematic control. This paper focuses on structuring a ship design project approval mechanism (SDPAM) based on fuzzy axiomatic design (FAD) methodology which is concerned with the system design of relevant interfaces on an engine room control console. Design characteristics such as accessibility and operational requirements are treated as “hard” constraints being encountered in this complex problem. Hence, the proposed methodology requires performance assessment using fuzzy values. This study provides original contributions to the existing approval procedures of classification societies, which are non-governmental construction and classification organizations in the shipping industry. Furthermore, the study proposes a new approach to handle uncertainty and vagueness by an axiomatic design extension to ship project execution problems.

Integrated Ship Machinery Systems Revisited

ABSTRACTIn the late 1980s, the world of U.S. Navy surface combatants is confronted with three new needs: to reduce ship signatures by factors of 10 to 1000; to provide ten‐gigawatt power pulses to new combat systems; and to reduce the high cost of hull, mechanical and electrical (HM&E) systems by taking advantage of newly‐available technology. These needs have caused an active response of the surface‐ship community to the many apparent benefits of integrated machinery systems.The acoustic signature problem accelerated interest in integrated machinery systems. A substantial reduction in noise at cruise speeds requires the elimination of propeller cavitation and requires reductions in machinery noise. The use of contrarotating tractor propellers driven by bicoupled epicyclic gears and an alternating‐current electric motor in a pod which faces directly into the flow stream is potentially capable of such performance; even better would be a contrarotating superconductive electric motor in the pod. No other concept seems even remotely competitive with these. The major reductions in installed power, fuel consumption, and reduced displacement reported in the April 1980 Naval Engineers Journal are retained. Large reductions in infrared signature and further reductions in fuel consumption are provided by intercooled recuperated gas turbines.An integrated electric propulsion system provides the opportunity to temporarily “borrow” power from the propulsion system and transform it into pulses for advanced combat systems. Not only the power of the turbines, but also the kinetic energy of the ship are available.

INTEGRATED SHIP MACHINERY SYSTEMS WHICH RESULT IN SMALL, EFFICIENT DESTROYERS

ABSTRACTThe use of properly seiected integrated ship machinery systems can sharply reduce the size, installed power, and fuel consumption of future Destroyers without reducing payload, speed, range, margins, or stability. One properly chosen subsystem opens the way to using a second superior subsystem, thus a third one, et cetera, forming a sort of beneficient chain reaction. The superior subsystems themselves provide highly leveraged effects on the displacement of the ship. Synergism of this entire chain can result in ship with markedly reduced initial and operating costs. The most essential elements of this system are: aircraft derivative gas turbines; compact, lightweight electric transmissions; large battery energy storage systems; and contrarotating propellers. Adoption of these systems permit secondary high‐leverage subsystems to be used, including efficient ship service power from propulsion turbines and light‐weight maintainable propulsion pods. These changes make total ship rearrangement possible, resulting in major decreases in turbine ducting, propulsion shafting, electric power distribution, and propulsion auxiliaries.Similar benefits in the auxiliary machinery system result from adoption of reverse‐osmosis fresh water production, heat pumps for space heating, glass‐reinforced plastic piping, controllable‐speed, high‐efficiency pump motors, et cetera.