In this paper, we present a novel approach to modeling the wear of complex dynamic systems, exemplified by aircraft engines, through the construction of a structured latent space.Unlike traditional methods, our model does not rely on explicit wear data but instead leverages supervised training to minimize the error on observable system parameters.Beyond wear forecasting, this work offers a foundation for unsupervised diagnosis, risk prevention, and the quantification of repair impacts.

The latest IPCC 1 report shows that the aviation industry is responsible for around 2% of greenhouse gas emissions; this is lower than emissions from many other sectors, but still equivalent to the total emissions of a European country like Germany.Following the recommendations of the International Civil Aviation Organization (ICAO 2 ) and its long-term global aspirational goal (LTAG), the aeronautics industry has come together under the Air Transport Aviation Group (ATAG 3 ) to converge towards zero greenhouse gas emissions by 2050, such as CO2 emissions and other radiative effects such as those generated by condensation trails.To achieve this goal, we have a number of levers at our disposal: technical improvements to our engines and aircrafts, the use of new sustainable fuels, and the use of data now accessible thanks to new engineering 4.0 technologies.This document first presents the data we now have at our disposal.The second section briefly recalls the opportunities offered by new renewable fuels.Finally, we present some digital approaches and conclude with details of three central themes illustrated by the contributions to this special session. About data in the aeronautic domainAeronautical data is incredibly diverse, as it encompasses various measurements from onboard systems and critical supplementary information.Whether monitoring an aircraft's behavior, managing an airline, or overseeing an airport, understanding this data is essential.It includes not only data collected directly from embedded systems, but also crucial details related to weather, maintenance, safety, fuel consumption, design, development, and industrialization processesranging from the large 115000 lbf 4 thrust commercial engines for the Boeing 777 airliner to the smallest sensors. Onboard MeasurementsWhen thinking about aeronautical data, the first thing that comes to mind is the measurements acquired during flights.These measurements are typically taken at frequencies ranging from 1 to 100 Hz, and in some cases, at several tens of kHz for data 1 IPCC -Intergovernmental Panel on Climate Change (https://www.ipcc.ch/)-(GIEC in French). 2 ICAO -LTAG -https://www.icao.int/environmental-protection/Pages/LTAG.aspx.3 ATAG -Air Transport Aviation Group (https://atag.org).4 lbf (pounds) 1 lbf ~ 4.

The analysis and simulation of aircraft engine behavior have garnered significant attention in the aeronautical industry, primarily due to its implications for performance, maintenance, safety, and sustainability.Our work successfully showcases the efficacy of utilizing time series data collected from our aircraft engines to construct a digital twin capable of dynamically emulating their real-time behavior.We then introduce a new methodology to model the physical engine's degradation and meticulously monitor its evolution over time.By continuously analyzing the simulated data against real-world performance measurements, our approach offers valuable insights into the engine's long-term behavior and health trajectory.

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the imputation of missing data. The authenticity of the generated time series has seen remarkable advancements with the integration of recurrent neural networks (RNNs) and generative adversarial networks (GANs). RNNs used to represent the state-of-the-art (SOA) in processing sequence dependencies until the advent of Transformers, which redefined the SOA, especially in Natural Language Processing and Computer Vision. The introduction of a transformer-based GAN represented an innovative step forward, aiming to address the limitations inherent in RNNs. However, this model’s efficacy is constrained when faced with unimodal data distribution assumptions, leading to arbitrary outputs in complex distribution scenarios. This paper introduces a novel Multivariate Time Series Conditional GAN (MTS-CGAN), that leverages transformer-based architectures in generator and discriminator networks. MTS-CGAN conditions the generation process on a specific encoded context (categorical and MTS inputs), enabling one-pass generation of multivariate time series, and accommodating mixed distribution frameworks, outperforming existing models. We evaluate MTS-CGAN using quantitative metrics across multiple multivariate time series datasets. Furthermore, we propose also an innovative adaptation of the Frechet Inception Distance (FID), tailored for time series, to assess the quality of the generated data. This research demonstrates the potential of MTS-CGAN in generating high-fidelity multivariate time series.

The turbofan engine uses oil to lubricate and cool its components.This extremely sensitive system can cause in-flight engine shutdowns in the event of a failure.This article presents the implementation of a fully automatic anomaly detection system capable of detecting both known phenomena and exceptional cases using weak signals.2 Collected data CEOD data is made up of hundreds of measurements and calculations performed by the on-board computers and retrieved on the ground after each flight.To address the oil management system, we are only interested in 10 parameters, including 4 oil data parameters (quantity, temperature, main pressure, and pressure difference around the 287

The present manuscript tackles the issues of model interpretability and variable importance in random forests, in the presence of correlated input variables.Variable importance criteria based on random permutations are known to be sensitive when input variables are correlated, and may lead for instance to unreliability in the importance ranking.In order to overcome some of the problems raised by correlation, an original variable importance measure is introduced.The proposed measure builds upon an algorithm which clusters the input variables based on their correlations, and summarises each such cluster by a synthetic variable.The effectiveness of the proposed criterion is illustrated through simulations in a regression context, and compared with several existing variable importance measures.

In this article, we introduce a deep learning model (denoted thereafter DCM: Deep Contextual Model ) for survival analysis able of predicting the probability that a subject meets an event of interest according to its past life.The subject and the event of interest can be diverse depending on the field of application, thus the model can be applied in various contexts.We present an application in the aerospace field that consists in forecasting hot corrosion in turbofan.

Time series clustering is a challenging task due to the specificities of this type of data. Temporal correlation and invariance to transformations such as shifting, warping or noise prevent the use of standard data mining methods. Time series clustering has been mostly studied under the angle of finding efficient algorithms and distance metrics adapted to the specific nature of time series data. Much less attention has been devoted to the general problem of model selection. Clustering stability has emerged as a universal and model-agnostic principle for clustering model selection. This principle can be stated as follows: an algorithm should find a structure in the data that is resilient to perturbation by sampling or noise. We propose to apply stability analysis to time series by leveraging prior knowledge on the nature and invariances of the data. These invariances determine the perturbation process used to assess stability. Based on a recently introduced criterion combining between-cluster and within-cluster stability, we propose an invariance-guided method for model selection, applicable to a wide range of clustering algorithms. Experiments conducted on artificial and benchmark data sets demonstrate the ability of our criterion to discover structure and select the correct number of clusters, whenever data invariances are known beforehand.

A major application of data analytics for aircraft engine manufacturers is engine health monitoring, which consists in improving availability and operation of engines by leveraging operational data and past events. Traditional tools can no longer handle the increasing volume and velocity of data collected on modern aircraft. We propose a generic and scalable pipeline for large-scale analytics of operational data from a recent type of aircraft engine, oriented towards health monitoring applications. Based on Hadoop and Spark, our approach enables domain experts to scale their algorithms and extract features from tens of thousands of flights stored on a cluster. All computations are performed using the Spark framework, however custom functions and algorithms can be integrated without knowledge of distributed programming. Unsupervised learning algorithms are integrated for clustering and dimensionality reduction of the flight features, in order to allow efficient visualization and interpretation through a dedicated web application. The use case guiding our work is a methodology for engine fleet monitoring with a self-organizing map. Finally, this pipeline is meant to be end-to-end, fully customizable and ready for use in an industrial setting.

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance. This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.