Beyond condition monitoring in the maritime industry

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2 Beyond condition monitoring in the maritime industry

EXECUTIVE SUMMARY Smart organizations know they can no longer afford to see maintenance as just an expense. Rather, maintenance must be integrated within the business cycle in order to guarantee predictability, growth and increase the overall quality of operations. Moving from a regime of scheduled rule-based maintenance via on-condition maintenance and ultimately to a data-driven risk-based regime can lead to more accurate and timely maintenance tasks. This smarter view of maintenance allows for achieving many practical advantages leading to lower costs and increased safety and availability of ship systems.

Contact Details: Prepared by: Knut Erik Knutsen, Senior Researcher Gabriele Manno, Senior Researcher Bjørn Johan Vartdal, Senior Principal Engineer

Beyond condition monitoring in the maritime industry

CONTENT INTRODUCTION 4 CONDITION MONITORING The basic principle for applying Condition monitoring Methods for condition monitoring

6 7 9





What is lacking as compared with aviation Challenges and potential benefits

THE WAY FORWARD Enabling Technologies The stages of implementation Real-time risk based approaches New business opportunities

21 22

26 26 27 28 30



4 Beyond condition monitoring in the maritime industry

INTRODUCTION In the past 70 years expectations associated with maintenance have grown considerably. Maintenance has evolved from a reactive process, performed after a functional failure, to a preventive activity where items are overhauled or discarded according to a time-schedule. Preventive maintenance is based on the assumption that a component has a defined lifetime, after which its failure rate increases. However, estimates of lifetime often have large uncertainties. Hence, scheduled maintenance is often performed too early or too late, resulting either in high costs due to unnecessary replacements or functional failures, respectively. Even worse, a component that cannot be inspected from the outside is often disassembled and inspected on schedule, with the risk of introducing faults during inspection or re-assembly, thereby leading to failure shortly afterwards. In other words, infant mortality is sometimes introduced to components in mid-life due to invasive inspection or calendar maintenance. In order to reduce the uncertainty resulting from preventive maintenance, new approaches, based on the assessment of asset condition, have emerged. These approaches can be distinguished from previous ones due to their predictive nature (Figure 1). These developments track back to the 1960s, when it was discovered that a large

percentage of failures in complex aircraft were not age-related, but random, and therefore were not adequately addressed by preventive (scheduled) maintenance. In order to address these failures regular inspections, and later continuous monitoring using sensors, were used to determine if a failure was about to happen. This led to the development of a new maintenance framework called Reliability Centered Maintenance (RCM) [1], which comprises the adoption of condition monitoring (CM) as one of the means by which safety and availability can be increased in a cost-effective manner. This position paper aims to introduce the principles associated with the state of the art in condition monitoring for predictive maintenance and to discuss the potential benefits of introducing new maintenance strategies into the shipping industry, based on the experiences in the aviation industry. Possible barriers to implementation in the shipping industry are reviewed and options on how these barriers may be overcome are suggested. The possible future impact on the shipping industry, resulting from full-scale implementation of such maintenance strategies, is also discussed.

Beyond condition monitoring in the maritime industry

Predictive Maintenance Preventive Maintenance Run to Failure 1950 Figure 1.  Evolution of Maintenance practices.




6 Beyond condition monitoring in the maritime industry

CONDITION MONITORING The motivation for introducing condition monitoring is to increase the overall safety level in order to reduce the risk of loss of life and property, whilst also minimizing the costs associated with maintenance of the component or system being monitored. This can be achieved through increased reliability of the component or system being monitored and reduced consequences of failures. Reliability is improved by close monitoring of possible failure mechanisms and


Smooth operation

taking decisive actions to avoid the development of failure mechanisms through operational measures in the short-term and through maintenance in the long-term. The failure mechanisms may therefore not be able to develop, leading to the avoidance of a potential breakdown of the component or system (Figure 2).

First sign of trouble

Time to failure Warning time

time Figure 2.  The basic concept of condition monitoring.


Beyond condition monitoring in the maritime industry

Age related = 11%

Random = 89%

Break in period= 7%

Failure rate

Bathtub = 4%

Wear out = 2%

Random = 14%

Fatigue = 5%

Infant mortality = 68%


Figure 3.  The 6 possible failure rates patterns.

THE BASIC PRINCIPLE FOR APPLYING CONDITION MONITORING Nowlan and Heap [1] discovered that traditional preventive maintenance was insufficient to address random failures. They analysed failures of equipment installed on board aircraft and discovered that failures of equipment fall into one of six pattern groups, each of which has a different curve pattern characteristic of the failure rate vs. age of the equipment (Figure 3). The real surprise was the large proportion of failures, 89%, that were not age-related, and therefore would not be addressed by the traditional preventive maintenance techniques. This proportion has been shown to be similar for marine vessels, where the

relative number of random failures was slightly lower, 77% according to the MSP study (1982) by the US Navy and 71% according to the SUBMEPP study (2001) on US Navy submarines [2]. The increase in age-related failure is mainly attributed to greater degradation by corrosion when components are in a saline environment. However, the failure patterns are still dominated by random failures, and these can only be addressed by detecting them before they occur; that is, by adopting a predictive maintenance strategy. It is assumed that some physical change occurs in the component or system before a failure mode occurs. Assuming that such a change can be detected using the appropriate sensor(s) and


8 Beyond condition monitoring in the maritime industry


SENSE & PROCESS Monitoring signals





Figure 4.  High level representation of a condition based maintenance framework.

Model of the system (physical behaviour)

Operating system







Analyze Diagnostics Prognostics

Correct / Maintain


Figure 5.  High level representation of a Model-based condition monitoring maintenance framework.

Beyond condition monitoring in the maritime industry

data collection interval, measurement data can be recorded and stored for real-time or historical data analysis to uncover impending faults or to correlate previous failures with changes in recorded measurement data.

METHODS FOR CONDITION MONITORING Condition monitoring is the process of monitoring a set of parameters of condition in a system in order to identify a significant change that is indicative of a developing fault. The analysis of measured data consists of a pre-processing step that validates sensor output and extracts features like temperature gradients and vibration modes. Using the extracted features, a diagnostics algorithm can be used to determine the health state of the component or system. If an impending failure is detected, the operator is alerted (Figure 4). Diagnostics and prognostics algorithms for implementing a condition monitoring system are based on one of two main approaches: a model-based (first principles physical

model) approach or a data-driven (statistical and data-mining) approach [3]. Model based diagnostics The model-based approach requires detailed knowledge of the physics and function of the component or system, and these may not be readily available. The physical degradation mechanisms, such as crack propagation and spall growth, can be modelled and the models updated using measurement data. Valuable insights into failure modes can be obtained through simulations of the system, by integrating material-level, componentlevel and system-level models, and these may also be used to estimate how the component and system performance is affected by degradation. Based on comparison of the simulation and the actual measurements from the physical component, it may be possible to diagnose the component by altering the model until correspondence between the model and the measurements is achieved. This approach does not require large amounts of previous failure


10 Beyond condition monitoring in the maritime industry

Historical operations

Actual operation system

Sense & collect

Sense New observations

Database collect Data Base Model of the system (statistical, regressive, clustering...) Train & update the model

Inputs to run the model

Analyze Diagnostics Prognostics


Correct / Maintain

Figure 6.  High level representation of a data-driven condition based maintenance framework.

data to identify an imminent failure, and therefore may be most suitable for newly developed equipment with good computer models and limited failure statistics (Figure 5). Data-driven diagnostics The data-driven approach utilizes previous failure data and correlates measured condition data with known failures using pattern recognition techniques like clustering and neural networks. One substantial advantage compared with the model-based approach is that there is less need for detailed physical knowledge of the monitored

components. However, detailed statistics and measurements related to previous failures are necessary, and therefore this approach may be most suitable for legacy equipment with good statistical data on failures, with no need for developing a model of the physics. A disadvantage compared with physical model-based approaches is that the statistical models assume an underlying stability in the monitored system. Changes in load conditions or operating profiles may lead to inaccuracies because historical data is relied upon to diagnose a fault (Figure 6).

Beyond condition monitoring in the maritime industry

Prognostics Prognostics extend the results of diagnostics by estimating the future behaviour of systems. Based on the knowledge of the current health state, prognostics provide predictions of the future health state and determine the remaining useful life (RUL) of the component or system. As with diagnostics, both model-based and data-driven approaches may be implemented. Model-based prognostics may take into account physical degradation mechanisms like crack propagation or fatigue. However, these models are uncertain, and this uncertainty must be taken into account in the prognostics algorithm. The advantage is that the prognosis made is ‘knowledge-rich’, meaning that is based on actual physical changes that can be traced to a specific part or region of the component. Data-driven prognostics make use of historical run-to-failure data if available. The historical data are used to train a prognostic algorithm, e.g. artificial neural networks, support vector machines or hidden Markov models. The measurement data from the component in service is then fed to the trained algorithm that takes the currently determined health state of the component and projects the health state into the future. When the projected health state degrades beyond a pre-determined threshold, the component is considered to have failed, and the estimated time until failure is the RUL. Having a reliable RUL of each component is extremely valuable for maintenance planning as maintenance intervals can be extended and several maintenance tasks can be combined to reduce downtime.

A closer look at Reliability Centered Maintenance Reliability centered maintenance (RCM) is a taskoriented programme where tasks are selected for each functional failure or failure mode. Tasks are selected based on several criteria like difficulty and cost, where simple and cheap tasks are preferable, and must also satisfy the criteria of applicability and effectiveness. The consequences of failure must also be considered when choosing the task or tasks. There are five different types of consequences to consider: safety, operational, economic, hidden safety, and hidden non-safety. Logic diagrams are used to determine the most appropriate task based on these criteria. If a failure can be predicted and prevented using condition monitoring of the component, and this improves safety and/or costefficiency, then condition monitoring should be applied. Ultimately, if no effective maintenance action can be found, redesign of the component or system must be considered (Figure 7). Condition monitoring and on-condition maintenance is a central part of RCM. However, within the RCM framework further improvements to safety, availability and costs can be achieved, as it takes a systematic approach to finding the most efficient solution. The most safety-critical and cost-effective measures receive most attention, while least critical components can be subject to reactive maintenance (run-to-failure).


Failure Modes and Criticality Identification

Predict ?


Prevent ?


Run to Failure ?

Redesign Figure 7.  Simplified RCM decision logic.


On condition




12 Beyond condition monitoring in the maritime industry








Fatalities/year moving average (bold)


1000 Passengers carried exp. fit (bold)




MSG-3 1980



MSG-2 1970




Passengers carried [Millions]





In the 1960s, it was discovered that a large percentage of failures for complex aircraft were not age-related, but random, and therefore were not adequately addressed by preventive (scheduled) maintenance. This led to the development of a new maintenance strategy called Reliability Centered Maintenance (RCM) [1]. RCM is a process for 3000 determining a maintenance strategy that takes into account safety, operational benefitShip (availability), years 2500 and economy. RCM advocates the implementation of a predictive maintenance scheme in which the 2000

condition of in-service equipment is monitored in order to predict when maintenance should be performed. Figure 8 gives an indication of the effect of these developments. The figure shows the number of lives lost per passenger carried on aircraft. The total number of lives lost in passenger aircraft per year 7000 peaked between 1970 and 1980 and has been more than halved since then, while the number of6500 passengers has more than tripled. 6000





500 0

Fatalities/year moving average (bold)

4500 4000

Ship yearss


Figure 8.  Fatalities per year in passenger aircraft (left axis), and passenger carried (right axis). Data from the World Bank [4], ACRO [5].

Beyond condition monitoring in the maritime industry

In the time-period before the peak in lives lost, the maintenance paradigm was one of performing maintenance on hard-time (running hours) or calendar schedule. In an attempt to increase reliability, maintenance intervals were shortened, but with little or no effect. In some cases, the only reported effect was one of increased infant mortality of the components in question. The findings of Nowlan and Heap, based on failure statistics collected by United Airlines prior to 1968, indicated that only about 11% of failures were age-related, and this discovery led to a shift in the maintenance paradigm towards on-condition maintenance. In 1968, MSG-1 (Maintenance steering group 1) was introduced, and was followed up later with MSG-2 and MSG-3 [4]. A central theme in the MSG documents is that when hard-time or scheduled maintenance is not relevant, maintenance is performed on-condition, as determined by inspection or condition monitoring based on sensor measurements.

As seen in Figure 8, implementation of MSG-1, 2 and 3 maintenance strategies occurred a few years before the flattening and fall in the number of lives lost in passenger aviation. This indicates strongly that the new maintenance regime has had a profound impact on the safety of passengers in aviation. However, it is important to note that the effect did not occur immediately. A time-lag of 5-10 years elapsed between the publication of these documents and the effect on safety. Such a time-lag is to be expected due to the major changes in organization and culture that are needed for a paradigm shift from scheduled maintenance to condition-based maintenance. In addition, the effectiveness of the methodology will depend on the accumulated amount of data, as this provides the basis for the statistics-based diagnostics and the means for continuously refining the tools of model-based diagnostics. A time-lag is therefore to be expected due to the time taken to establish the methodology.


14 Beyond condition monitoring in the maritime industry

Mechanical Dispatch Reliability U.S Operators of Boeing Commercial Transports 99.5 99.0

98.0 97.5 97.0 96.5

200 9

200 7

200 3

200 5

200 1

9 199

199 7

199 5

199 3

199 1

9 198

198 7

198 5

198 3


96.0 198



Source: Boeing

Figure 10.  Mechanical dispatch reliability for U.S. operated Boeing commercial aircrafts [8].

Among aircraft of this time are the two large passenger carriers, Douglas DC-8 and Boeing 747. The DC-8 was put into service using the old maintenance paradigm of hard-time and scheduled maintenance, while the 747 maintenance programme was the first to apply on-condition maintenance tasks according to RCM principles. On the DC-8, there were 339 scheduled component replacement tasks, while there were only 8 such tasks on the 747. This led to a massive reduction in maintenance labour costs and a simultaneous increase in reliability. Figure 9 illustrates the shift towards component condition monitoring in the air transportation industry. For passenger aircraft, unplanned repair or maintenance incurs a high cost, due also to loss of income. When an aircraft is grounded, passengers experience lower quality of service due to delays and cancellations. A common measure of the availability of commercial aircraft is the dispatch reliability, meaning the percentage of flights that depart on schedule. For Boeing commercial aircraft, the dispatch reliability has been increasing from 97.25% in 1981 to 98.9% in 2009 (Figure 10). Considering the introduction of several new technologies during

this period, this is remarkable. Despite this, reliability and availability have increased and there have been excellent improvements in safety. Moreover, cost of maintenance per flight hour has been relatively constant, with some variations, since the introduction of the RCM framework in the air transportation industry [7] [8].

100 % 75 % 50 % 25 % 0%



Condition monitoring On condition Hard time

Figure 9.  Evolution of component maintenance policy for aircraft [8].


Beyond condition monitoring in the maritime industry


16 Beyond condition monitoring in the maritime industry

STATUS OF CONDITION MONITORING WITHIN SHIPPING The current level of RCM and condition monitoring within shipping is very variable and depends on ship type, age and components. The monitoring mostly takes place at the component level and the sophistication is dependent on the component maker. Standard sensors measuring quantities such as temperature, pressure, vibration and strain are normally used, but research into new sensor types for detection of failure modes is also being carried out. Main engines are normally the components with the most sensors and sophisticated monitoring systems, due to the risks associated with main engine failures. Damage statistics show that the main engine is the single component responsible for most serious damages on ships [10]. Condition monitoring has also been expanded to other components in the propulsion system, such as gears, bearings, and propellers. In addition, critical auxiliary components and systems are subject to sensor-based monitoring. The requirement and sophistication of monitoring depend on the criticality of the component with respect to the safety and performance of the ship. Sensor-based monitoring is also carried out for the ship hull. This type of monitoring typically monitors hull girder stresses by means of strain gauges and motion sensors, but can also account for other phenomena affecting the hull integrity and performance.

For the components, condition monitoring is normally facilitated by the component maker. For a ship with components from many different makers, there may be a variety of uncoordinated monitoring systems. This increases the complexity for the operator and fails to capture the systems’ perspective for a set of integrated or interdependent components. Some dedicated providers of condition monitoring systems for components and systems are starting to emerge, but the ability of such companies to provide adequate monitoring of proprietary components may be limited by the complexity of retrofitting sensors to the component and the legal implications of doing so. In most cases, the processing of monitored data is carried out on the ships themselves, by means of manual monitoring of alarms and trends, but in some cases some simple automated data diagnostics and prognostic routines are built into the onboard systems. Data from such systems are commonly transferred to shore, manually or via mail, on a regular basis for further processing. However, systems are gradually being introduced that allow for remote monitoring and processing through the transmission of sensor data to shore. The data can then be handled by the ship owner and/or the component maker, or, alternatively, a condition monitoring service provider. The time-




MSG-3 1980



MSG-2 1970



Beyond condition monitoring in the maritime industry


7000 Ship years





5000 Fatalities/year moving average (bold)


4500 2010





4000 1960


Figure 11.  Fatalities per year in passenger ships (left axis), and ship-years (right axis). Data from IHS Fairplay database (shiptypes A32, A36 and A37) [11].

lag between the signal measured by the sensors and the diagnostics carried out by an operator or automated diagnostics routine will determine the applicability and quality of the condition monitoring. The advantage of sending raw or processed data to shore real-time is that experts or computationally intensive diagnostics routines may be used to process the data and give adequate feedback to the ship, such as advice on operational measures or corrective actions required in time to prevent failures. The quality of the diagnostics and prognostics will be dependent on experience data or simulation models that are probably too computationally expensive to be run on board.

WHAT IS LACKING AS COMPARED WITH AVIATION As discussed in the previous section, the introduction of condition-based maintenance has had a tremendous impact on aircraft safety, with the risk of fatalities greatly reduced. In shipping, the picture is somewhat different. Looking at the fatalities/year and ship years for passenger ships as shown in Figure 11, the number of ship years has increased almost linearly since 1980 [11]. However, the number of fatalities over this period has remained almost constant, with a

small increase recently. This means that the safety of passenger ships improved until about 2005, and has since become worse. Thus, compared with aviation, safety development in shipping is not nearly as positive. Since maintenance procedures in shipping have not undergone the same developments as in aviation, this may be a contributing cause. Although condition-based maintenance is becoming more commonplace, maintenance in shipping generally follows a preventive or scheduled maintenance system, often called Planned Maintenance System (PMS), which may or may not include a condition monitoring scheme. According to Ref [12] only 17% of classed ships operate with an approved PMS, and only 12% of these use condition monitoring, leaving ~2% of classed ships with a condition monitoring scheme in place.

Ship yearss







18 Beyond condition monitoring in the maritime industry

CHALLENGES AND POTENTIAL BENEFITS A RCM approach with a predictive maintenance scheme has been adopted and further developed within several industries, but adoption of these methodologies within shipping has been sluggish. Several challenges may explain the slow take-up of this approach in the maritime industry. However, smart organizations know that they can no longer afford to see maintenance as just an expense. Rather, maintenance must be integrated within the business cycle in order to guarantee predictability and growth and to increase the overall quality of operations. This smarter view of maintenance enables the achievement of many practical advantages, leading to lower costs and increased safety and availability of ship systems. Challenges The correct adoption of these new technologies requires a big change undertaking in management, comprising the adoption and the integration of new technologies within the business cycle. It involves a change in the culture of an organization, and includes the development of trust and belief in a black box, as well as the acceptance of shortcomings such as errors. The implementation of new technologies requires a strong knowledge base about ship functions, systems and components; a knowledge that can be brought into operations only through a structured and collaborative design phase where the maintainability of the asset is taken into account. Only in this way is it possible to address effectively the monitoring and maintainability of a ship. Moreover, an increase in the number of equipment items being monitored and poor system control and integration could lead to a greater workload on operators and introduce more training requirements. The current competences and their organization in the industry today will not be sufficient for dealing with the new complexity of systems. Furthermore, the component focus has been very strong in the shipping industry, while the introduction of increasingly more complex systems necessitates increased attention to the effects arising from interacting and interdependent components. Appropriate software and new standards must be developed to ensure the control of these interconnected systems and the handling of the large volumes of data that will be generated. Software is subjected to continuous updates that could influence interconnected systems and can

have bugs that manifest only on-demand. Data issues will be related to their maintainability, accessibility, security, trustworthiness, quality, and many more factors. In order for proper diagnosis of issues based on the monitored signals to take place, a database of monitored data and events is required to which statistical methods can be applied. In comparison with aircraft, which are produced in series of hundreds or even thousands and mostly using the same components, ships are often purposedesigned and unique, or they are produced in series of 2-10. Therefore, failures and measurement data in the airline industry accumulate more rapidly to form a comprehensive database that can be utilized for data-driven diagnostics and prognostics. In shipping, such databases of time series data are currently scattered and generic methodologies for the purpose of diagnostics of various systems and components are scarce and incomplete with respect to description of failure modes. Modelbased diagnostics and prognostics are also largely unchartered territory in the maritime industry. However, the quality of the condition monitoring depends on the existence of large amounts of reliable data collected over time, as well as the availability of detailed simulation models allowing for model-based methodologies. In order to accumulate such data, access to many ships with detailed failure statistics and high quality measurements is necessary. In general, small or medium-sized shipowners may not have sufficient volume to attain this amount of data, and this represents a challenge for ship-owners who would like to implement a condition monitoring system. On the other hand, many ships, although not in themselves identical, use many identical components. Hence, it should be possible to build comprehensive databases if these data could be shared between different stakeholders. Another barrier to implementation of effective condition monitoring of systems could therefore be issues relating to sharing of proprietary data. Ship-owners could facilitate this on their own ships by combining data from various sources as they have ownership of these data, but most shipowners do not have the in-house competence or resources to implement an effective condition monitoring system. A major barrier to real-time remote condition monitoring has been the ship to shore data transfer capabilities. In shipping today, the Inmarsat-4

Beyond condition monitoring in the maritime industry

satellites provide the Fleet Broadband service with a symmetrical bandwidth of up to 432 kbps. The global satellite data bandwidth is, however, increasing steadily, and in 2013 and 2014 Inmarsat will launch three data transfer satellites (Inmarsat-5) and create a service called Global Xpress速 that will offer global coverage, starting in late 2014 and offering a bandwidth of 50 Mbps down and 5 Mbps up [13]. In addition, Telenor will launch Thor 7 in late

2014, which will provide broadband communications (also for maritime applications) in Europe and the northern areas (spot beams over the North Sea, Norwegian Sea, Red Sea, Baltic Sea, the Persian Gulf and the Mediterranean) [14]. However, the most common geo-stationary satellites do not provide any coverage in the Arctic regions; therefore an initiative has been launched by Telenor and several partners that will investigate the industry interest in launching


20 Beyond condition monitoring in the maritime industry

communications satellites with a north-south highly elliptical orbit to provide Arctic coverage. Initiatives are also underway to explore the use of mesh networks that have already been developed and tested for maritime use [15]. Benefits The benefits to ship-owners of implementing a condition monitoring strategy are related to cost savings and the predictability of operations. Cost savings are related to reductions in maintenance needs such as inspections and repairs, decreased costs due to failures and downtimes and unplanned maintenance, lower insurance costs, and savings due to improved equipment performance that may lead to decreased fuel consumption. Predictability, besides reducing the uncertainty around decisions, is also a source of competitive advantage and can be used in many business relations to attract charterers. The accurate picture obtained of a vessel’s current and future status also contributes towards a higher awareness of system capabilities. This allows preventive actions to be performed to increase reliability and safety, and to ensure that ship capabilities match operational requirements. In addition, the increased knowledge and documentation of a ship’s health status and maintenance history will increase the second-hand value of a well-maintained ship. Moreover, with onshore support possible, part of the operations can be delegated to specialized personnel and systems that can keep track of

performance and risks. This also has a positive effect on the crew competences, since onboard personnel can be involved in a continuous learning process in close collaboration with the onshore organization. In this way, not only will the ship be maintained effectively, but the competence and experience of the crew will also be enhanced. Component manufacturers can improve the quality of components during the lifecycle by analysing operational data and trying to minimize losses and failures in relation to different operation profiles. Components can be more easily targeted to specific needs (e.g., Arctic, special purpose vessels) and operations. Maintenance can be optimized based on condition and suggestions regarding timing of repairs, substitutions, etc. can be given to the shipowner. Potential sources of benefits that drive business value within a structured approach to vessel maintainability are outlined in Figure 12.

Beyond condition monitoring in the maritime industry

Increased output

Risk reduction

• Increased Asset utilization

• Compliance to health and safety regulations

• Improved capacity management • Aligned goals between maintenance & operations • Alignment between maintenance and part planning

• Management of tolerable risk • Audit trails for maintenance and operation activities • Managed corporate standards

BUSINESS VALUE Cost reduction

Strategic positioning

• Effective maintenance craft utilization

• Better information to run the business

• Effective maintenance planning

• Strong and flexible foundation to support future growth

• Reduced working capital and inventory requirements • Better supplier, procurement and warranty management

• Improved supply chain integration • Greater employee satisfaction

Figure 12.  Potential sources of benefits which drive business value within a structured approach to vessel maintainability.


22 Beyond condition monitoring in the maritime industry

THE WAY FORWARD In 2030, the status of condition monitoring technologies in shipping will be very different from how it is today. As a reminder, it is worth considering the development in aviation where, during the course of 30 years (1970-2000), inspection, condition monitoring and maintenance regimes have changed completely. Today, aviation is far ahead of shipping regarding maintenance technologies, and there is no doubt that this is of benefit to airlines. In shipping we can expect an accelerated development compared with aviation, such that by the year 2030 shipping may have caught up with aviation and, together with large emerging industries like the wind turbine industry, may be participating in pushing forward the science of condition monitoring and maintenance. However, there are several new developments that are necessary to enable this future.

ENABLING TECHNOLOGIES Several technologies are currently emerging that may be expected to enhance the field of maintenance by making it cheaper, easier, less invasive, and closer to maintenance-free. Smart sensors Smart sensors are sensors with integrated preprocessing capabilities that deliver processed and digitized information. This information can be more easily incorporated in a system through a digital interface, significantly reducing development costs

since the pre-processing of the signal does not need to be done by the system developer. Smart sensors can react to changes in their surrounding environments or when a user injects a command. When networked together, smart sensors can automatically organize to form a reconfigurable and collaborative network. In this way, a network of smart sensors can achieve higher confidence for the measured event characteristics while simultaneously reducing the overall quantity of transmitted data. Moreover, when connected through a wireless medium, sensors can be more easily installed and cabling can be avoided. Although the power needs of sensors may be covered by using long lasting batteries, energy harvesting devices that convert ambient energy from vibrations, sunlight, or temperature gradients to electricity are making their appearance; these will allow the powering of wireless sensors without the need for cabling or batteries. Harsh environment sensing is also an emerging sensor technology that makes use of new materials technology, such as silicon carbide (SiC) that is extremely resilient to high temperatures and pressures. SiC is also among the hardest materials known after diamond, thus allowing usage at abrasive locations such as oil-well drill heads, engine cylinder walls, and turbine blades. Connectivity, Big data and Fast computing Sensor systems will send logs of data onshore at a rate that will depend on the criticality of operations

Beyond condition monitoring in the maritime industry


24 Beyond condition monitoring in the maritime industry

and the complexity of the onboard systems. This will spur the development of structures and methods for the collection, transmission, and handling of this new “big data�. This will be achieved through a highly developed and reliable ship to shore connectivity link. Therefore, ship operators can control the ship and its components from shore, possibly through mobile devices. Distributed data storage and computing architecture, as well as a powerful information management system, will be in place, and this will increase workforce communication, competence sharing, benchmarking via the Internet, quasi-instantaneous responses, and new types of analyses. These analyses could be enhanced by the deployment of new computing and processing capabilities of sensor networks and their increased Internet accessibility. Autonomous and reconfigurable systems Fast-forward a few decades, and tasks will gradually be handed over to computers that make the decisions regarding performing maintenance and allocating resources. Systems will be semiautonomous in the sense that they will be able to decide on maintenance needs with limited inputs from a human operator. These systems may be able to reconfigure themselves through advanced sensors and actuator networks when the requested operations compromise the condition of components and, therefore, the induced risk of failures.

THE STAGES OF IMPLEMENTATION For ship-owners of today, it is important to consider the stages of implementation of a highly advanced condition monitoring system. Benefits will also come in stages, depending on the level of commitment in terms of corporate cultural changes and willingness to invest in development and infrastructure. Defining an effective diagnostics system The first stage of implementation is to determine the most critical failure modes of a ship system and to equip the relevant components with sensors that can detect related physical parameters. This information is analysed by onboard computers that perform diagnostics and determine the health state of the component, and whether it should be inspected, overhauled, or discarded. The direct benefits of this first stage are the capability of preventing failures and the reduction in costs and safety concerns associated with the failures that are prevented.

Moving towards an effective prognostics systems The second stage includes the use of prognostics to determine the remaining useful life (RUL) of components and to gain an understanding of when maintenance should be performed and to plan for that. Prognostics use statistical information about failures or physical degradation models, together with continuously measured parameters that determine health state, to determine RUL. The benefit of having access to this information is that the reliability of the ship is estimated in real-time, and actions can be taken if needed. The end result is greater availability and lower maintenance costs. Migrating to integrated, real time, risk-based maintenance At this stage, calendar-based and hard-time maintenance will belong to the past and condition monitoring based maintenance is in place. Condition-based maintenance will be able to address the correct timing and quantity of maintenance for specific monitored components, and this will be an enormous improvement on old practices. As experience with such systems grow, and the related mathematical knowledge of failure processes develops, the accuracy of both diagnostic and prognostic algorithms will improve and give more accurate residual-life estimates. This will allow further switching towards online, risk-based maintenance, where maintenance is seen as the set of actions that is used to maintain or reduce the system risk level cost-effectively. Here, the difference from the old view of risk-based methods lies in the fact that these measures are evaluated real-time and from actual operations, rather than being based on recommendations and poor statistics. This new perspective requires that the maintainability of an asset is considered as early as in the design phase. In fact, at this stage, models of ship systems must be developed for future use in operations and to become part of the decision support and information management system of an enterprise. One approach may be to create an integrated framework for lifecycle design that includes computer models for components and system models that can accommodate changes measured by sensors and the effects that these changes have on interacting components. Hence, on both a component and system level, the models will include information about physical behaviour, failure modes and failure propagation mechanisms, and the characteristics and location of sensors. In addition, the framework could include the maintenance and

Beyond condition monitoring in the maritime industry

operations database, allowing for easy access to these data from the modelling environment.

management in making the correct decisions concerning investment in maintenance or related fields. This will, in turn, result in better asset and capital utilization.

REAL-TIME RISK BASED APPROACHES The overall objective of the maintenance process is to increase the profitability of the operation and to optimize the total lifecycle cost without compromising safety or environmental issues. Risk assessment integrates costs, reliability, safety, and environmental issues, and therefore can be used as a decision tool for real-time maintenance planning when combined with real-time data from condition monitoring systems. Maintenance planning that is based on risk analysis minimizes the probability of system failure and its consequences (related to safety, economy, and environment), and helps

In brief, the concept of real-time, risk-based maintenance integrates the condition of an individual component at the system level and uses reliability and risk indicators evaluated in real-time to prioritize maintenance actions on individual components (Figure 13). The systems perspective displayed on the bridge With an effective condition monitoring system the current and future health state is used in early identification of failures through advanced analysis algorithms, and maintenance actions can be

Operating system Build a system reliability model


Evaluate Reliability of components Observations C1

Evaluate Reliability of the system Model of the system

Analyze C1 Diagnostics Prognostics

Component 1 (C1)

System function


Observations CN Analyze CN Diagnostics Prognostics

Component N (CN)

Evaluate criticality of components Importance of components C1 Correct / Maintain

CN Unreliability of components

Figure 13.  High level representation of a Risk-based condition based maintenance framework.



26 Beyond condition monitoring in the maritime industry

performed accordingly. This reduces the failure rate, and thus increases safety and reliability. Because failures, as well as avoided failures, are recorded along with condition monitoring data, improved reliability data of components and systems is obtained, thus laying the grounds for improved risk management with less uncertainty. In addition, the system can give immediate alerts of impending failures, such that the captain of the ship may shut down equipment or return to shore to avoid a critical situation or additional damage arising. Using a hierarchical framework for aggregating the health condition or reliability of components into a health status of a sub-system will give an instantaneous indication to the crew and managers about potential failures and the effects these will have on the ship reliability and performance (Figure 14). The possibility of making informed decisions about whether to continue a mission, while balancing the reliability, performance, and mission importance, can improve availability without compromising safety.

NEW BUSINESS OPPORTUNITIES In order to exploit the true benefits of the collection and analysis of operational data, a new generation of tools, methods, and systems needs to be adopted by the shipping industry. The objective of such systems is to combine empirical knowledge, based on operational data, with system knowledge with the aim of supporting the various stages of a vessel’s lifecycle. These new methods and technologies shall become one of the languages of the sector and be integrated within the operational procedures of the industry. These developments will require new collaborative efforts between designers, class societies, and operators to ensure that the monitoring system is fit for purpose and that the collected data conveys valuable information.

Ship Propulsion Propulsion 1

Propulsion 2

Thruster 1

Thruster 2

Engine 1

Engine 2 LNG Tank

Figure 14.  Integrated approach to System condition monitoring.

Beyond condition monitoring in the maritime industry

Onshore support One of the first impacts on ship condition management from the adoption of the new technologies mentioned above is the possibility of defining arrangements where the system integrators, component manufacturers, or other service providers offer onshore support for diagnostics & prognostics, and guidance in emergency situations. Nowadays, there are some original equipment manufacturers (OEMs) already providing this kind of service, where remote control centres, managed by the OEMs themselves, have a direct link with the ship and can suggest preventive or recovery actions to the crew onboard. In general, this kind of agreement allows the competence of the crew to be improved, as they are directly supported by experts onshore and can, thus, be engaged in a learning process. Moreover, by enhancing the guidance of onshore-based experts to the crew (e.g., by virtual spaces, vision-goggle), there will be less need for global presence, travelling to remote locations, and, thus, major cost savings can be achieved. Finally, diagnostic and prognostic support offers a valid means to cut the costs of spares procurement, due to larger lead times and overall improvements in resource allocation. Performance based arrangements A further possibility is the introduction of performance-based services. For component/system manufacturers, this may mean a comprehensive aftercare service in which the manufacturer performs all maintenance tasks and guarantees a certain level of performance of their product with a flat rate price tag. Such a solution spreads the risk between the manufacturer and its customers, and creates a centrally administrated maintenance organization for all similar products sold by the manufacturer. It also allows the manufacturer to keep full control of their products, and creates a strong incentive for continuous improvements in product reliability, since substandard performance will have a direct negative impact on the manufacturer. Also, by reducing technical risk and increasing the level of servitization, it can be considered a route by which manufacturers achieve higher long-term profit margins. For customers, the benefits include greater predictability of their assets and lower total cost of ownership. Such services are most widely applied in the defence sector, and are considered useful for complex and large-scale projects. However, this approach is also becoming more prevalent in the private and public

sectors, as particularly demonstrated in the aviation industry where a large proportion of aircraft jet engines are sold as a performance-based service. Analytics and Information management While system integrators and component manufacturers could be involved in the aftermarket of the engineering system that they deliver, with different levels of engagement, other players can have a major role in combining data from different sources and in providing solutions for the most effective maintainability of an asset. These players will leverage on predictive analytics technology to analyse data on multiple ship parts, components, and systems, and make recommendations to optimize maintenance and operations. By breaking down the data silos of individual stakeholders and exploiting the synergies of shared information streams, new opportunities may emerge. Verification, Certification and Assurance Finally, these new approaches for condition management can facilitate relations with classification societies and regulatory bodies, as well as with charterers. New integrated systems for showing compliance and ensuring customers that a ship is fit for purpose can be defined based on the collected data, thereby promoting transparency and enhanced communication between the industry actors and, not least, society at large.


28 Beyond condition monitoring in the maritime industry

CONCLUSIONS In this paper we have reviewed the main advances in the field of condition monitoring, with particular focus on developments in the air transportation industry as compared with the shipping industry. The main trends, achievements, possibilities, and challenges have been discussed, along with potential approaches to overcoming the barriers to implementation. It has been noted that developments in the field of maintenance have been driven by an increasing recognition of the role of maintenance in asset management and business performance. In fact, maintenance is currently recognized as affecting all aspects of business effectiveness and risk - safety, environmental integrity, energy efficiency, product quality, and customer service, not just availability and cost. Maintenance is considered to be a pillar of the value generation activities of those businesses that make use of engineering systems (physical and software assets) to generate value for customers. In order for the shipping industry to attain levels of development similar to those achieved in the air transportation industry, we advocate the necessity of establishing a systematic approach for the deployment of condition monitoring technologies that span from the design phase to the operational phase of the ship lifecycle. Moreover, the establishment of systematic approaches for the collection and analysis of data may lead to major improvements regarding how

operations are run and systems are maintained. It seems likely that during the next decade the shipping industry will increasingly look to the air transportation industry, which has developed a number of systems to improve performance. These trends may push the shipping industry towards becoming data-centric and based on extended value chains, with consequent shared risks, costs, revenues, and competencies. Technological development plays a very important role as an enabler of the commercial sector. Generally, technological development is expected to contribute towards reducing costs and increasing the safety and welfare of operators. However, it may also result in higher lifecycle costs due to increased performance requirements, greater systems complexity, and more requirements for competence and training. Therefore, managing the risk of introducing new technologies will remain critical in the future in order to be able to provide cost reductions while enhancing safety and sustainability performances. The development of such systems will not be limited by technology. Rather, the industry will have to weigh the benefits of more advanced systems, which include reduced costs, increased safety and improved vessel condition, against the large investments required for change management.

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30 Beyond condition monitoring in the maritime industry

BIBLIOGRAPHY [1] F. S. Nowlan and H. F. Heap, “Relibility-centered maintenance,” United Airlines, San Fransisco, 1978. [2] T. M. Allen, “US Navy Analysis of Submarine Maintenance Data and the Development of Age and Reliability Profiles.,” Submarine Maintenance Engineering, Planning and Procurement (SUBMEPP) , 2001. [3] G. Manno, K. E. Knutsen and B. J. Vartdal, “An Importance Measure approach to System level Condition Monitoring of Ship Machinery Systems,” in CM 2014 and MFPT 2014, Manchester, 2014. [4] Maintenance Steering Group - 3 Task Force, “MSG-3, MAINTENANCE PROGRAM DEVELOPMENT DOCUMENT,” Air Transport Association of America, Washington, 1980. [5] The world bank, “Air transport, passengers carried,” International Civil Aviation Organization, [Online]. Available: http:// [Accessed May 2013]. [6] Aircraft Crashes Record Office, “Death rate per year,” BAAA/ACRO, [Online]. Available: deces%20par%20annee.htm. [Accessed May 2013]. [7] A. M. Smith and G. R. Hinchcliffe, “Reliability centered maintenance,” in RCM - Gateway to world class maintenance, ButterworthHeinemann, 2003. [8] US Department of Transportation (US DOT), “Research and Innovative Technology Administration, TranStats,” [Online].

Available: releaseinfo.asp. [Accessed 13 December 2013]. [9] B. Barimo, “Airlines for America,” 18 November 2009. [Online]. Available: http://www.airlines. org/Pages/ATA-Testimony-by-Vice-PresidentBasil-Barimo-before-the-House-TransportationSecurity-and-Infrastructure-Protection-Sub.aspx. [Accessed 10 December 2013]. [10] G. Manno, “Statistical analysis of casualty data: a simple method with application on ship machinery systems, 2012-1068,” DNV publishing, Oslo, 2012. [11] IHS Fairplay, “Global Maritime Statistical Reports,” [Online]. Available: http://www.ihs. com/products/maritime-information/statisticsforecasts/index.aspx. [Accessed 2013]. [12] D. C. Shorten, “Marine Machinery Condition Monitoring: Why has the shipping industry been slow to adopt?,” Lloyd's Register, London, 2012. [13] Inmarsat plc, “Inmarsat Global Xpress - Home,” 2013. [Online]. Available: [Accessed 6 February 2013]. [14] Satbeams, “Satbeams - Satellite Details Thor 7,” [Online]. Available: http://www. [Accessed 8 February 2013]. [15] J. Pathmasuntharam, P. Kong, M. Zhou and Y. Ge, “TRITON: High speed maritime mesh networks.,” in IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC., Cannes, France, 2008.

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