Introduction

The aviation industry relies heavily on predictive maintenance to keep operations smooth, safe, and cost-effective. One of the most effective tools for this is survival analysis, which estimates the Remaining Useful Life (RUL) of aircraft engines. Using historical maintenance and failure data, survival analysis can help MRO (Maintenance, Repair, and Overhaul) teams plan proactive maintenance before failures occur, potentially saving costs and improving aircraft uptime. In this article, we will walk through how to use survival analysis with Python’s Lifelines library to estimate engine RUL and make data-driven maintenance decisions.

Why Use Survival Analysis for Predictive Maintenance?

Survival analysis originated in healthcare for estimating patient survival times, but it can be applied to any domain where you need to predict "time-to-event." In aviation, "time-to-event" might refer to predicting the time until an engine failure or the next necessary maintenance interval. Using survival analysis for predictive maintenance provides several advantages:

1. Proactive Repairs: Estimate engine lifespan to prevent unplanned downtime.
2. Optimized Maintenance Intervals: Schedule maintenance based on actual usage and historical data instead of fixed intervals.
3. Cost Reduction: Minimize costly reactive maintenance by intervening before critical failures.

Survival Analysis Basics: The Kaplan-Meier Estimator

The Kaplan-Meier estimator is one of the most common tools in survival analysis. It calculates the probability of survival past a given time point, accommodating censored data (cases where an event, such as failure, has not yet occurred). This is ideal for MRO teams, as they can estimate survival probabilities for engines still in service and project future maintenance needs.

Let’s dive into the code!

Step 1: Setting Up Your Environment

First, make sure you have the necessary Python packages installed. You’ll need the pandas and lifelines libraries.

pip install pandas lifelines

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Step 2: Calculating Survival Probabilities with the Kaplan-Meier Estimator

We can now use the Kaplan-Meier estimator from the lifelines library to analyze the survival probabilities of the engines. This estimator will help predict the likelihood that an engine will continue to operate beyond certain hours.

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from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

# Instantiate the KaplanMeierFitter model
kmf = KaplanMeierFitter()

# Fit the model using the data
kmf.fit(durations=engine_df['Operating_Hours'], event_observed=engine_df['Event'])

# Plot the survival function
plt.figure(figsize=(10, 6))
kmf.plot_survival_function()
plt.title("Kaplan-Meier Survival Estimate for Engine Life")
plt.xlabel("Operating Hours")
plt.ylabel("Survival Probability")
plt.grid()
plt.show()

The survival function plot gives us a clear view of how the probability of survival decreases as operating hours increase. Each drop in the curve represents an observed failure, while the steady segments represent periods with no failure events.

Step 3: Interpreting the Kaplan-Meier Results

The Kaplan-Meier curve indicates the probability of an engine surviving past certain operating hours. For example, if the curve shows a survival probability of 0.8 at 3,000 hours, this means there’s an 80% chance an engine will continue running beyond 3,000 hours. These insights allow MRO teams to schedule maintenance before reaching critical failure points.

Step 4: Estimating the Mean Survival Time for Predictive Maintenance

The mean survival time provides an estimate for when most engines will require maintenance or may fail. This can guide decisions on maintenance intervals.

# Get the mean survival time (expected lifetime)
mean_survival_time = kmf.median_survival_time_
print(f"Estimated Median Survival Time: {mean_survival_time} hours")

This result tells us the estimated number of operating hours after which 50% of engines are expected to require maintenance.