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Digital engine data for actionable insights
Digitalization is transforming the performance and efficiency of engines and gensets. By capturing and analyzing data from the start, they can provide valuable insights for operating and maintenance.
This is not only true for newer equipment, but also for older ones that can be upgraded or retrofitted with digital solutions. Digital upgrades can unlock the full potential of older engines and gensets, enhancing their reliability, performance, and lifespan.
Whatever generation of hardware you are running, and whichever maintenance strategy is right for your business, we can support you with digitalization solutions that optimize your processes and support your journey towards Industry 4.0.
The Journey of your Data
Engine data
Sensors within the engine or in relevant components (e.g., turbocharger) capture real-time data on safety and performance parameters, such as lube oil inlet temperature, fuel pressure, and exhaust gas temperatures.
Engine information
To be useful, this data must be organized and presented as information. As this information is stored digitally, it can also be transmitted to remote locations for further analysis and actions.
Actionable insights
Engine data can be analyzed for operational decisions. For example, we know that elevated crankpin oil temperature can lead to engine failure. A data-supported actionable insight could be an alarm or automatic shutdown that occurs when the crankpin oil temperature is higher than pre-defined thresholds.
Academic definitions
Types of maintenance strategies
Learn more about maintenance strategies with these definitions.
This strategy is a run to failure. Maintenance is carried out only if and when anomalies are detected or in case of failure. This approach is based on the assumption that maintenance and downtime costs will always be lower than the costs of other maintenance strategies.
Key benefits
- Cost-effective approach where data-supported actionable insights are not a priority
- Works for low-value assets but not high-value, mission-critical assets, such as a main engine
- Any data and information is used reactively, not proactively
This approach follows maintenance schedules and is the most common engine maintenance strategy. Maintenance is carried out at pre-determined intervals (e.g., hours of running time) as defined by their own designs, risk analyses, mean-time-between-failure studies, and experience. Asset owners must typically follow these to achieve compliance with classification societies and insurers.
Key benefits
- More cost-effective than reactive maintenance approaches
- More predictable maintenance scheduling with less unplanned downtime
- Cost-effective if the maintenance costs are lower than the costs induced by an overhaul after a major failure of the equipment (e.g., replacement of an engine crankshaft) and its consequences (e.g., extended downtime, production stops, and related impact on the entire vessel and/or enterprise)
- Suitable for high-value and/or critical assets
This approach uses sensor data and analytics to fix issues right away. Maintenance is carried out when pre-defined conditions or thresholds are met. Condition-based maintenance is typically a supplement to planned/preventive maintenance regimes.
Key benefits
- Sensors provide data on critical parameters
- Maintenance needs are signaled based on actual conditions
- Maintenance and condition monitoring software analyzes data; analysis can occur onsite or remotely
- May reduce planned maintenance downtime and related costs (maintenance is carried out only when warranted by actual conditions)
- A good supplement to planned maintenance
This strategy uses sensor data, analytics, and AI to foresee issues. Relevant engine components are continuously monitored, and data is transmitted for ongoing analysis. Using this data alongside engine-specific, precise wearing rate information, AI-powered algorithms predict exactly when maintenance is required, and only necessary maintenance is performed. In its pure form, this is the holy grail of maintenance strategies. Pragmatically, predictive maintenance is combined with other maintenance regimes.
Key benefits
- Sensors provide data on critical parameters
- Maintenance is performed only as needed
- Maintenance and condition monitoring software analyze data; AI and machine learning enable ongoing optimization of algorithms
- Remote analysis support provides actionable insights regarding optimal time to perform maintenance
- Fewer unplanned downtime events through improved maintenance prioritization
Supporting you every step of your digitalization journey
Join our growing customer base worldwide that uses digital solutions allowing them to personalize their digitalization journey and keeping the asset in its prime condition for years to come. Take advantage of preventative, condition-based, and more and more predictive maintenance for your engines.
Our engineering and R&D teams are also developing cutting-edge technologies that will soon enable condition-based and predictive maintenance solutions. This means that wherever you are in your digital transition journey, we at Everllence PrimeServ are by your side with expertise and solutions ready now and in the future as the technology that supports them continues to evolve.
Contact our experts
We're standing by to help
To find out how Everllence PrimeServ can improve your engine's performance and ROI, contact us today.
The power of digital
We believe the future of maritime shipping is digital. Join us on our transformation journey as we deliver flexible, future-minded technology to help you stay competitive, agile, and safe. Let us navigate the ebbs and flows of digital transformation to be prepared for tomorrow's challenges.
Digital benefits
