A survival curve represents the estimated percentage of assets in a group that remain in service as they age. Use it to understand the expected service-life pattern for a group of similar assets - not to predict the exact failure date of a single asset.
At the start of the curve, nearly all assets are expected to be in service. As they age, some begin to fail and the curve gradually declines. The steeper the drop, the faster that group is expected to deteriorate. For example, a durable pipe material may stay above 90% survival for decades before declining, while a less durable one may drop off much earlier.
The curve describes the expected performance of an entire asset group, based on historical data, industry knowledge, or engineering assumptions.
Each asset group in a deterioration model is assigned a survival curve - this is what tells the model how that group is expected to age. When the model runs, Info360 Asset places each asset on its group's curve by age and reads off:
From Remaining Useful Life and Failure Probability, the Failure Grade Matrix then assigns each asset a Failure Grade. These results are also what feed downstream tools such as the Risk Model and the Rehabilitation Decision Tree - so the quality of your survival curves shapes every decision built on top of them.
Every survival curve is created using a curve method. The curve method determines how the deterioration pattern for an asset group is generated.
Choose the curve method that best reflects the data and knowledge available for the asset group.
| Curve method | Description | When to use it |
|---|---|---|
| From Failure Definition |
Fits a survival curve to the historical failures that match the selected Failure Definition. When a curve is fitted from Failure Definition data, Info360 Asset uses a Weibull model - a standard reliability model that describes how failure risk increases with age. |
You have enough local failure history - at least 5 recorded failures in the group - and want the curve to reflect your network's actual performance. |
| Industry Standard Curve |
Applies a curve built from real-world failure data from 500+ utilities in the U.S. A strong, defensible starting point even when you do have some local data. |
You want a proven benchmark curve - either as your primary method or to compare against your own history. |
| Custom Curve | Allows you to define survival points manually. | Engineering judgement, condition data, or external studies support a specific deterioration profile. |
| Copy Curve from another Group | Copies a curve from another asset group. | Another group has similar characteristics and can use the same deterioration assumptions. |
| One Expected Life |
Generates a curve from a single expected service-life value. Generated curve will be a straight line that starts at 0 and ends at the one expected useful life. It will not appear in the chart. |
You only have a general estimate of expected asset life. |
Different methods can produce different curves - and therefore different results. A curve fitted to your local failure history may show a very different pattern than an industry-standard curve for the same asset type, so it's worth comparing.
See, Select a Survival Curve Method for how to assign a method to each group.
A survival curve has two axes:
| Axis | Meaning |
|---|---|
| Horizontal (X) | Asset age in years |
| Vertical (Y) | Percentage of assets expected to remain in service |
For example, if a curve shows 80% survival at 50 years, it means that approximately 80% of assets in that group are expected to still be in service at age 50, while about 20% are expected to have failed.
Imagine a group of 1,000 cast iron pipes:
| Age | Expected survival |
|---|---|
| 20 years | 98% |
| 40 years | 90% |
| 60 years | 70% |
| 80 years | 40% |
At 60 years, the model expects about 700 pipes to still be in service and about 300 to have failed.
This doesn't mean a specific pipe will fail at 60 years. Some may fail earlier and others much later. The curve represents the overall pattern for the group.
The survival curve is where a deterioration model gets its intelligence: it encodes how a given group of assets is expected to age, so the model can turn a simple attribute - age - into RUL, Failure Probability, and a Failure Grade for every asset. That, in turn, lets you answer questions like:
Because these results flow into the Risk Model and Rehabilitation Decision Tree, a well-chosen curve is the difference between prioritizing the right assets and the wrong ones. Two pipe groups both 50 years old can have very different curves - one with decades of life left, the other entering a high-risk period - and the survival curve is what surfaces that difference.