Predictive maintenance delivers the greatest savings where unplanned downtime triggers high consequential costs: on main engines and auxiliary machinery, turbochargers, pumps, separators and electrical drives. The benefit does not come from sensors alone but from earlier detection of deviations and properly planned interventions aligned with port windows, spare parts availability and survey schedules.
A typical example: a turbocharger on a two-stroke engine shows a slowly rising exhaust gas temperature over several weeks at constant load. Without systematic trend monitoring, this deviation is only noticed at alarm level or when performance drops – by which point the damage has already occurred. Early detection would have allowed a targeted turbocharger cleaning during the next port call, rather than an unplanned workshop visit carrying off-hire costs of several tens of thousands of euros per day.
From experience, the greatest savings potential lies in three areas: first, avoidance of consequential damage through early intervention; second, better alignment of spare parts procurement with actual demand rather than blanket stockholding; and third, the ability to extend maintenance intervals on a data-driven basis where condition permits – without violating class or manufacturer requirements.
In practice, a few reliable signals are often sufficient: vibration, bearing and exhaust gas temperatures, lube oil condition, pressure curves, running hours, load profiles and alarm histories. What matters is the quality of the trends, not the number of measurement points.
Vibration measurements on main bearings, turbochargers and large pumps provide robust early warning of mechanical wear. Bearing temperatures reveal changes in lubrication or alignment. Exhaust gas temperatures per cylinder allow conclusions about injector nozzles, exhaust valves and combustion. Lube oil analyses – particularly particle count, viscosity and water content – give reliable indications of internal component condition without disassembly.
Alarm histories are frequently underestimated. A single alarm often carries little significance, but clustering patterns over weeks can point to systematic problems that get lost in daily operations. Anyone who evaluates this data in a structured manner – for instance weekly trend reports from the Chief Engineer, cross-referenced ashore with PMS data – already has an effective basis for predictive maintenance without major IT investment.
The economic leverage lies in the workflow after the alarm. A good system does not merely flag a deviation but links it to a technical assessment, urgency rating, root cause hypothesis and recommended action.
In practice, this looks as follows: the trend of a rising bearing temperature on auxiliary diesel No. 2 is detected and automatically cross-referenced with the last maintenance history. The system establishes that the last bearing overhaul was 14,000 running hours ago and recommends inspection within the next 500 hours. Simultaneously, it checks whether the required spare parts are available on board or at the next port.
Without this structured process, the result is an alarm notification that the watch engineer acknowledges, possibly classifies as an isolated event, and does not follow up. The difference between these two scenarios is the actual ROI of predictive maintenance.
Typical false starts include overly broad pilot projects, unclear responsibilities and the attempt to replace time-based maintenance entirely from day one. A narrow focus on a few assets with high risk or cost potential is more effective.
Another common mistake is overemphasising technology over process. Expensive sensors and cloud platforms are of little use if nobody on board translates the results into actions, or if nobody ashore has defined the escalation pathways. The best systems I have seen in practice were often the simplest: structured trend tables exchanged weekly between vessel and shore, with clear thresholds and defined response times.
Additionally, many operators try to incorporate all assets simultaneously. This overwhelms both the crew and the shore organisation. Better: start with two to three critical systems – such as main engine, turbocharger and separators – build experience, then expand step by step.
The data architecture for predictive maintenance need not be complex, but it must be consistent. At its core lies the linkage of condition data with operational context. An exhaust gas temperature of 380°C says little if the corresponding engine load, speed, ambient temperature and fuel quality are not captured alongside it. Only normalisation to comparable operating conditions makes trends meaningful.
For maritime applications, three analysis tiers have proven effective. Tier one is simple trend monitoring with static thresholds – any ship manager can implement this with a spreadsheet. Tier two uses moving averages and regression analysis to forecast wear rates. Tier three works with multivariate models correlating several parameters simultaneously – vibration, temperature and oil analysis together, for example – to detect failure patterns earlier.
Most fleets already benefit significantly from tiers one and two. The leap to tier three demands considerably more data quality and analytical competence but delivers genuine added value for critical assets such as main propulsion or power generation. IACS Recommendation Rec. 74 provides a useful framework here for evaluating condition-based class programmes.
A frequently overlooked aspect is data transmission from vessel to shore. Satellite connections have limited bandwidth, which means data reduction must happen on board – aggregated trend values rather than raw data, with the option to access raw data when needed. Systems designed to transmit gigabytes of raw data daily fail in practice due to communication costs.
Implementing predictive maintenance requires clear role allocation. On board, the Chief Engineer is responsible for data collection and initial assessment. Ashore, a superintendent or technical manager should consolidate trends and make escalation decisions. Without this clear assignment, predictive maintenance remains a paper tiger.
In the first phase, existing PMS software supplemented by structured trend forms is often sufficient as tooling. Specialised condition monitoring platforms such as ENIRAM, Kyma or ShipNet offer more automation but presuppose a stable data infrastructure. The realistic timeline for a solid implementation is six to twelve months for the first pilot asset, with a further six months for rollout to critical systems.
Crew acceptance is decisive. If the crew does not understand the purpose of the reporting system, or feels that additional bureaucracy is being created without feedback, data quality drops rapidly. The best results are achieved by managers who give the Chief Engineer timely feedback – what happened with the data, which actions were derived.
A container feeder with a MAN B&W 6S50ME-C showed a slow rise in main bearing temperature at position 4 over three months. The increase was only 2°C per month – well below the alarm threshold, but unmistakable as a trend. Systematic evaluation led to a planned bearing inspection during the next docking window, which revealed early-stage bearing damage.
The cost of the planned repair was approximately EUR 35,000 including materials and service engineer. An unplanned failure would have caused towing, off-hire and express repair costs estimated at EUR 250,000–400,000. That is the difference predictive maintenance makes in practice – not through magic, but through consistent trend tracking.
Predictive maintenance is worthwhile when at least two of the following conditions are met: the fleet includes vessels with high failure impact (container, tanker, offshore), average unplanned repair costs exceed EUR 100,000 per year per vessel, or the existing data situation (onboard data, oil analyses, PMS histories) is already usable.
For small fleets of two to five vessels, a simple tier-one solution can be set up within a few weeks. Larger fleets benefit from a systematic assessment of asset criticality before investing in technology. The most common mistake is buying technology before process.
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