Data-Driven Crack Detection Framework for Full-Scale Fatigue Tests Based on Principal Component Analysis

Y. Ofir, Efrat Pinhas, Y. Freed, Yael Buimovich, Gil Noivirt, Orly Dolev, S. Shoham

Full-scale fatigue testing is a standard and essential procedure in the development of new air vehicles. In this process, a full-scale aircraft is used as a test article and subjected to fatigue loads representing the loads expected during its life. Dozens of hydraulic loading jacks are utilized to simulate ground and flight loads, with cabin pressure added as required. The primary objective of a full-scale fatigue test is to verify that the airframe maintains structural integrity throughout its intended design life. Beyond this, the test provides critical validation of crack initiation and growth predictions made during the design phase of aircraft development. It also serves to validate non-destructive inspection (NDI) techniques that will later be applied during maintenance checks in service. Such full-scale tests are time-consuming, typically last several years and are often completed after the first aircraft are delivered to the customers. When fatigue cracking is detected early in the test, design modifications can be introduced into serial production at relatively low cost. However, retrofitting aircraft already delivered to the customers is highly expensive, as it often requires complicated maintenance operation and substantial downtime. Such repairs, including aircraft downtime, can cost tens of thousands of dollars per airplane, and when multiplied across an entire fleet, the expense can reach millions. The economic incentive for early crack detection during full-scale fatigue tests is therefore clear. Traditionally, two methods are employed to detect cracks during full-scale fatigue test: Scheduled non-destructive inspections at predefined airframe locations. Strain gauge monitoring, where variations in strain distribution are used as indirect indicators of crack initiation. While these methods are valuable, they are limited in their ability to efficiently process the vast amounts of data generated during long fatigue campaigns. This is where machine learning, and particularly Principal Component Analysis (PCA), becomes effective. PCA is capable of reducing high-dimensional strain gauge datasets into a smaller set of uncorrelated principal components, allowing efficient data compression while preserving the dominant structural response patterns. Subtle deviations from these baseline patterns, such as those caused by load redistribution due to crack initiation and propagation, can then be identified as anomalies. Unlike simple threshold-based monitoring, PCA captures correlations across multiple sensors simultaneously, providing a more sensitive and robust early-warning mechanism. The objective of this paper is therefore to propose a PCA-driven approach for anomaly detection in full-scale fatigue testing. It will be demonstrated that PCA can efficiently handle large datasets, highlight abnormal structural behavior, and enable earlier and more reliable detection of cracks (see Figure 1). By integrating PCA into fatigue testing workflows, the aviation industry can reduce the risk of late design modifications, minimize costly retrofits, and ultimately save hundreds of thousands of dollars per test campaign.