In process manufacturing – where every minute of unplanned downtime can lead to thousands of dollars in lost production – asset reliability and predictability is a central priority. When critical machinery fails unexpectedly, it can disrupt entire production flows and safety systems.

One of the most transformative tools that Novity provides to mitigate this risk is condition-based Remaining Useful Life (RUL) prognostics. This is the capability to estimate how long a component or system can operate before repair or replacement is needed based on the actual, physical condition of the machine or component, as measured in real-time. RUL enables operations and maintenance teams to act before failures happen, becoming truly predictive, </em>while avoiding premature or unnecessary interventions.

<strong>What Is Remaining Useful Life Prognostics?

RUL prognostics use a combination of real-time sensor data, historical maintenance records, and advanced analytical models to estimate the time remaining before failure or critical degradation. The animation below illustrates the power of failure prognosis. At each point in time the operator knows how much time the machine has left until failure.

 

For example, for compressors, this might include analysis of:

• Vibration and acoustic patterns,
• Motor temperature and current draw,
• Pressure differentials and efficiency trends.

To achieve our highly accurate failure predictions, Novity uses hybrid models—combining physics-based understanding (such as thermodynamic behavior, rotor dynamics, and fluid mechanics) with machine learning techniques trained on historical and contextual data. Physics-based models bring domain expertise, deterministic reliability, and explainability, while machine learning models adapt to plant-specific patterns and evolving conditions and help account for uncertainty. The result is a more accurate, interpretable, and robust prognostic system.

The Value of Time-Aware Failure Predictions

While traditional predictive maintenance helps detect emerging faults, RUL goes a step further by answering the crucial question: “When will this asset fail?”

That time-based insight offers significant operational advantages:

• Reduced Unplanned Downtime: RUL forecasts allow teams to schedule repairs proactively, avoiding emergency shutdowns.
• Better Spare Parts Planning: With advance notice of impending failures, spare parts can be sourced just in time, reducing inventory costs.
• Informed Labor Allocation: Knowing when work needs to be done allows maintenance leaders to assign resources more effectively.
• Maximized Asset Value: RUL helps avoid both early and late interventions, ensuring equipment is used to its fullest potential without added risk.

Case Study: Pump Failure Prognostics

Motors driving rotating equipment such as pumps and compressors often have a belt or gearbox between the motor and the pump. This allows for the pump to operate at a different speed than the motor, but can also introduce additional points of failure. Problems with the gear tooth at a customer site caused the vibration to rise and the risk of failure to increase rapidly.

As shown in the above figures, the early RUL estimate suggests that the gear is likely to reach a critical wear threshold in 5-10 days. Armed with this insight, the operations team temporarily reduces load on the unit to extend the life, as visible in the health level plateauing, while procurement secures replacement parts. The fault progresses into a failure at the projected timeline, demonstrating the accuracy of the prognosis.

Embedding RUL in Operations and Maintenance Strategy

Ultimately, RUL prognostics should be integrated into existing decision-making workflows for their full value to be realized. It’s not enough to generate accurate prognoses – supporting processes and technologies need to enable maintenance teams to plan and take action. To maximize the benefit of prognosis, we either recommend or enable our customers to use:

• Visualization tools that display RUL estimates and confidence intervals alongside asset health indicators,
• Alerting systems that notify teams of degrading trends with enough lead time to act,
• Cross-functional visibility so that reliability engineers, maintenance supervisors, and operations managers all share the same future-focused picture,
• Planning software (like CMMS and ERP platforms) that consumes RUL data to optimize work orders and part stocking.

For Novity’s customers, Remaining Useful Life Prognostics insights are now feeding into everything from daily standups to capital planning, turning predictive maintenance from a reactive tool into a strategic lever.

Remaining Useful Life Prognostics Makes the Difference

For process manufacturing facilities – particularly those operating in asset-intensive, continuous environments – Remaining Useful Life prognostics represent a major leap forward in equipment reliability and operational agility.

For machines which are both mission-critical and prone to wear-related failures, like pumps and other rotating machinery, RUL predictions deliver the dual benefit of avoiding unplanned shutdowns and extending the useful life of expensive components. Hybrid models that blend physics with machine learning offer the accuracy and contextual intelligence required to make these forecasts as actionable as possible.

When uptime translates directly into dollars, and every repair window is a logistics puzzle, knowing not just what’s wrong but when it will go wrong changes everything.</p>

In our next post, we will discuss the challenges with getting prognostics built and deployed. Stay tuned!

How do you forecast ma

chine failures in your plant? Drop us a note and let us know!