Failure-Blind Detection of Structural Regime Exit in Aircraft Engine Degradation
Abstract
Overview This preprint introduces the concept of structural regime exit as a failure-blind, descriptive indicator of irreversible loss of structural recoverability in complex multisensor systems. Using the NASA C-MAPSS FD001 dataset, the work demonstrates that aircraft engine degradation can be characterized by changes in relational sensor structure prior to observable failure, without training, labels, or physics-based models. Methodological Positioning The presented approach is intentionally descriptive, not predictive. It does not estimate Remaining Useful Life (RUL), does not perform fault classification, and does not define operational thresholds. Structural regime exit denotes a state transition in which a system irreversibly leaves its initial structurally coherent and recoverable regime. Failure times are used only post hoc to report descriptive lead times. Scope and Disclosure Boundary This preprint documents the phenomenon, protocol logic, invariance requirements, and empirical evidence of structural regime exit. Concrete mathematical instantiations of the abstract structural descriptors referred to as harmony and stress, as well as specific parameter choices and implementation details, are intentionally not disclosed in this publication. The purpose of this work is to establish a stable conceptual and empirical reference, not to publish a fully reproducible algorithm. Intended Use The document is intended as: a scientific reference and timestamped record, a basis for discussion in PHM and system-level diagnostics, an upstream analytical layer complementary to existing PHM pipelines. It is not intended for direct operational deployment or safety-critical decision making. Dataset Evaluation is performed using the publicly available NASA C-MAPSS FD001 benchmark dataset. No additional or proprietary data is used.
Fields of Study & Subjects
Machine Fault Diagnosis Techniques
Fault Detection and Control Systems
Reliability and Maintenance Optimization
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 9 February 2026
- Language
- EN
- Document type
- Preprint