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Condition-Based Maintenance (CBM) is a maintenance strategy in which maintenance actions are triggered by the actual measured or observed condition of equipment, rather than by predetermined calendar intervals or fixed running hours. The underlying premise is that the optimal time to perform maintenance on a piece of equipment is when there is evidence that it is needed — not before (wasting resources on equipment that is still performing well) and not after (allowing a failure to occur that could have been prevented).
CBM relies on monitoring technologies and condition indicators to assess equipment health in real time or at regular intervals. These indicators may include vibration analysis (detecting early bearing or shaft deterioration through changes in vibration signature), oil analysis (identifying metal particles that indicate internal wear before it becomes a failure), thermography (detecting hot spots in electrical systems or bearings through infrared imaging), ultrasound testing (finding leaks, cavitation, or electrical discharge), and performance deviation monitoring (comparing actual output against expected performance to detect deterioration).
CBM is increasingly seen as a component of a broader predictive maintenance and Enterprise Asset Management (EAM) approach — one that uses data to drive maintenance decisions rather than fixed schedules. The distinction between CBM and purely predictive maintenance is subtle: CBM triggers maintenance when a condition threshold is crossed; predictive maintenance uses machine learning and historical failure data to forecast when a threshold is likely to be crossed in the future. Both approaches represent a significant advance over traditional time-based planned maintenance schedules.
Traditional Planned Maintenance System-based planned maintenance schedules maintenance tasks at fixed intervals — every 1,000 running hours, every six months, or every annual survey. These intervals are typically derived from manufacturer recommendations and class society requirements, and are designed to be conservative — ensuring that maintenance is performed before the expected failure point, with a margin of safety. The result is that some maintenance is performed on equipment that is in good condition and does not yet need it.
CBM addresses this limitation by replacing fixed intervals with condition-based triggers. Equipment is monitored continuously or at defined inspection points, and maintenance is initiated when the condition data indicates that it is needed — not when the calendar says so. This approach has two key advantages: it eliminates unnecessary maintenance on equipment that is performing well, reducing costs and crew workload; and it identifies developing problems before they reach the failure point, reducing unplanned downtime.
In practice, most fleets use a combination of time-based planned maintenance and CBM rather than replacing one with the other entirely. Critical systems that are difficult or impossible to monitor continuously — or where failure has catastrophic safety consequences — are typically maintained on conservative time-based schedules even if condition appears good. CBM is most effective for machinery that can be monitored economically and where the cost of monitoring is justified by the cost of failure and the expected efficiency gains from optimised maintenance timing.
Vibration analysis is one of the most widely used CBM techniques in marine engineering. Vibration sensors mounted on bearings, shafts, and rotating machinery measure vibration signatures that change as equipment deteriorates. Deviations from baseline signatures — changes in frequency, amplitude, or pattern — are early indicators of bearing wear, shaft misalignment, imbalance, or developing gear faults. Regular vibration measurements, plotted over time, allow engineers to track the progression of deterioration and schedule maintenance at the optimal point before failure.
Oil and fuel analysis — sending oil samples to a shore-based laboratory for spectrometric analysis — reveals the presence of wear metals, contamination, and chemical breakdown products in lubricating oil. Elevated copper levels in propulsion shaft bearing oil indicate bearing wear; elevated silicon suggests external contamination; viscosity changes indicate oil degradation. Fuel analysis confirms the quality and compliance of bunker fuel before it enters engine systems — protecting against contaminated or off-specification fuel that could damage injection equipment or invalidate MARPOL compliance records.
Performance monitoring CBM uses operational data — engine load, fuel consumption, exhaust temperatures, cylinder pressures, turbocharger performance — to detect deviations from baseline efficiency that signal developing problems. A turbocharger running 5% below its expected efficiency at a given load setting is showing early signs of fouling or deterioration; catching and addressing this early avoids both a potential failure and the fuel penalty of running an inefficient engine. This type of performance-based monitoring is increasingly automated through digital monitoring systems that compare real-time data against expected values continuously.
The value of CBM is only realised when condition data is integrated with the maintenance management system. A vibration reading, oil analysis result, or performance deviation that sits in a separate report — reviewed occasionally by a specialist — is far less effective than the same data flowing automatically into the Planned Maintenance System, triggering a maintenance job when a threshold is crossed, and creating a documented link between the condition evidence and the maintenance action. This integration is the practical challenge of CBM implementation.
A fleet management system that supports CBM must be able to receive condition data from monitoring systems (manual entry or automated data feeds), compare it against user-defined thresholds and baseline values, generate work orders automatically when thresholds are crossed, and link the work order to the condition evidence that triggered it. The resulting maintenance record then includes not just what was done and when, but why — which is valuable both for planning future maintenance and for demonstrating to class surveyors that the maintenance strategy is risk-based and evidence-driven.
The Enterprise Asset Management (EAM) perspective is important here. CBM data accumulated over time — condition measurements, failure records, maintenance histories — is a valuable asset for improving the maintenance strategy. Analysis of historical CBM data can reveal which equipment types deteriorate fastest in specific operating conditions, what condition thresholds reliably predict impending failure, and which monitoring techniques provide the most actionable information. This analytical capability turns CBM from a series of individual maintenance decisions into a continuous improvement process for the fleet's maintenance strategy.
Infoship supports condition-driven maintenance within its Enterprise Asset Management (EAM) platform, enabling condition data — from manual crew entries, oil analysis results, or integrated monitoring systems — to feed directly into the maintenance workflow. When a condition threshold is configured and a reading exceeds it, the system can automatically generate a maintenance job, assign it to the relevant crew member, flag it for shore superintendent review, and link it to the condition evidence record. This creates a complete, auditable trail from condition observation to maintenance action.
Combined with Infoship's Business Intelligence module and KPIs framework, condition and maintenance data accumulates over time into a fleet-level analytical resource. KPI reports showing mean time between maintenance events by equipment type, corrective versus planned maintenance ratios, and condition trend analysis across the fleet give technical managers the data they need to assess whether the current maintenance strategy is performing as intended — and to identify where CBM investment would deliver the greatest return in reliability and cost efficiency.
