What is remaining useful life (RUL)?

Remaining useful life (RUL) refers to the estimated amount of time an asset has until it becomes unusable or requires replacement. RUL is commonly used in predictive maintenance (opens in new tab) to determine when maintenance activities need to be performed on equipment to extend its lifespan and ensure reliable performance.

How does remaining useful life work?

RUL is calculated by analyzing various data points, including equipment history, environmental factors, maintenance records, and sensor readings. Predictive analytics models can analyze this information to determine an asset's health and the likelihood of failing in the future.

Below is a table that illustrates an asset deterioration profile, with A indicating the current condition of the asset and B indicating the minimum acceptable condition in which an asset can be in service. As time goes on, that asset continues to lose its optimal performance, as illustrated by the blue line descending.

Asset deterioration profile graph

The distance between A and B is the asset's RUL. This concept is easy enough to understand visually, but in practice, it can become complicated depending on the data and information available on a specific asset.

How to calculate the remaining useful life

The calculation method can vary for RUL depending on the type of data available. There are, however, three common ways teams can calculate RUL through:

  1. Lifetime data that indicates how long it took for an asset or similar assets to fail
  2. Run-to-failure (RTF) histories of assets similar to the one you wish to diagnose
  3. A threshold value that has been set on an asset and a condition indicator that flags a failure

We will review these three common ways in more detail and with examples below.

1. Using lifetime data to calculate RUL

Using lifetime data to estimate RUL involves analyzing historical data about an asset's performance, failures, and maintenance to predict how long it will function before needing significant repair or replacement. Here is an example:

  • Aircraft maintenance: Airlines collect vast amounts of data on their aircraft's performance, including engine health, flight patterns, and maintenance history. By analyzing this lifetime data, engineers can estimate when critical components like engines or landing gear might fail, allowing for proactive maintenance to avoid costly downtime.

2. Using run-to-failure (RTF) histories to calculate RUL

Using run-to-failure (RTF) histories to calculate RUL involves analyzing data from assets that have operated until failure to predict how much longer similar assets might last before they fail. Here is an example where this approach is used:

  • Automotive components: Manufacturers study the performance of automotive parts like brakes, tires, and batteries. By analyzing RTF histories, they can estimate how long these parts typically last under various usage conditions, helping drivers plan for replacements before failures occur.

3. Using a threshold value on an asset to calculate RUL

Using a threshold value and a condition indicator to calculate RUL involves setting a predefined limit (threshold value) for a specific condition indicator of an asset. When the condition indicator reaches or exceeds this threshold, it signals that the asset is approaching a critical state, and the RUL can be estimated based on this information. Here is an example:

  • Vibration monitoring in rotating machinery: For equipment like motors, pumps, and turbines, vibration is a critical condition indicator. A vibration sensor is often used on this equipment, and a threshold value for acceptable vibration levels is set based on historical data and manufacturer recommendations. When vibration levels exceed this threshold, it indicates potential wear or imbalance, and the RUL can be estimated using predictive models that correlate vibration levels with remaining life.

Remaining useful life vs. life of an asset

While RUL and the overall life of an asset may seem similar, they differ in some significant ways. The overall life of an asset refers to the total duration of time that an asset is expected to remain useful. This is typically measured in years. RUL, on the other hand, is an estimation of the remaining time until an asset fails or can no longer perform its intended function and may be measured in days or hours. The remaining useful life of an asset is often more important than the overall life of an asset, as it helps maintenance teams identify the optimal time to perform maintenance activities to mitigate the risk of failure.

Advantages of remaining useful life

Predictive maintenance that utilizes RUL calculations offers several benefits, including:

  • Reduced downtime and equipment failure
  • Increased longevity of assets
  • Lower maintenance and repair costs
  • Greater predictability and better asset management
  • Improved safety for workers and equipment users

These are just a few of the advantages, but one of the biggest advantages is the cost savings for maintenance teams. Research has proven that fixing a problem after a breakdown or failure can be more costly than conducting preventive maintenance ahead of the breakdown. The maintenance cost of some industries can increase by up to 70% of the total cost [1]. Calculating RUL and making it part of your predictive maintenance strategy is a great way to save on costs in the long run.

Additionally, RUL helps in providing necessary planning for condition-based maintenance tasks of such systems with an attempt to approach zero downtimes [2]. This is especially crucial when we think about critical systems.

Challenges of remaining useful life

While RUL has proven to be useful in predictive maintenance, it presents some challenges. Data quality and availability, sensor reading accuracy, and the complex mathematical methods used to analyze data can be difficult to implement and may require significant investment in specialized equipment, data collection, and analytics software. Additionally, the models used to predict RUL may need to be fixed, and accurate predictions can result in wasted time and resources.

Make RUL part of your predictive maintenance strategy

Remaining useful life is a powerful metric that enables maintenance teams to predict asset failure and plan maintenance activities more effectively. As machine learning and predictive analytics technology evolve, predictive maintenance strategies based on RUL calculations will become increasingly accurate and effective.


[1] Kang, Z., Catal, C. and Tekinerdogan, B. (2021) Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866836/ (opens in new tab) (Accessed: April 24, 2024).

[2] Berghout, T. and Benbouzid, M. (2022) A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Available: https://www.mdpi.com/2079-9292/11/7/1125 (opens in new tab) (Accessed: April 24, 2024).

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