Marie Skłodowska-Curie Actions

Onboard aircraft engine monitoring analyzing vibratory and sound signals captured by smartphones / tablets

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    SAFRAN Tech Paris
    EngineeringCommunication engineering
    First Stage Researcher (R1)
    31/03/2022 23:00 - Europe/Brussels
    France › Châteaufort
    H2020 / Marie Skłodowska-Curie Actions


This PhD position is part of the “MOIRA” (MOnItoRing of large scale complex technologicAl systems) project, funded by the European Commission through the H2020 “Marie Skłodowska-Curie Innovative Training Networks” program.

Modern technological systems increase in scale and are becoming more and more complex and sophisticated. Parallel, the revolution in electronics, digital technology and communications have drastically modified and expanded the physical diversity, scope, processing capabilities and complexity of the monitoring equipment and infrastructure used. Millions of networked sensors are being embedded in the physical world sensing, creating and communicating data. The amount of data available for capturing has been exploding and the era of Big Data is already here, as the Internet of Things (IoT) is becoming a reality. The main question which arises is how, following which steps and with which tools the data can be transformed to information and knowledge.

The objectives of MOIRA are

  • the development of novel signal processing tools for the monitoring of industrial processes based on machine learning methods applied on heterogeneous time series,
  • the application of data mining technologies for the estimation of Key Performance Indicators which determine the operational profit,
  • the conception, development and validation of methodologies for automated monitoring of cyber physical system fleets,
  • The multi sensor machine condition monitoring under variable operating conditions.

Within the context of the MOIRA project, the PhD student will work on the analysis, identification and modelling of vibratory and acoustic sources of aircraft engines. Vibratory and acoustic sources include random phenomena related to fuel combustion, cyclic phenomena related to the rotations of shafts/rotors and nonlinear phenomena related to the fluidic link between the high-pressure and the low-pressure turbines. The digital industrial evolution is characterized by an interconnection of machines and systems (objects) that is mainly based on the use of many sensors that collect information, but with a degraded quality related to the nature of the “general public” sensors they are equipped with. ESR15 will study how the health condition of aircraft engines can be analysed in real time using this type of generally low-quality sensor and establish a diagnosis that will anticipate and optimize the maintenance. The work will rely on baseline data collected on board during a flight from the specialized monitoring equipment and on data captured by common smartphones/tablets during actual flights.

The captured data will be firstly structured and ESR15 will work towards the correlation of the different sources and the development and application of novel high-level signal-processing tools in order to extract the diagnostic information hidden in the sound-and-vibration signals collected by common smartphones/tablets (low-quality sensors). The statistical properties of the captured signals will be exploited to characterize the sources and provide indicators of the motor’s health. Moreover, the ESR will develop a model for predictive maintenance exploiting additional information collected on the ground and during the planned maintenance. To achieve this, four principal tasks are required

  • Task 1: In general, the data acquired from real-world machine are subject to some errors or censoring, which harden their subsequent use and processing. Typical examples may include sensor defects, partial loss in the data or some erroneous measurements caused by environmental factors. To address this issue, the first tasks focuses on structuring, pre-processing, cleaning and visualising the database while conserving useful information.
  • Task 2: As the amount of data to be treated is large, the access to relevant information is like looking for a needle in a haystack. This naturally calls for advanced statistical and signal processing techniques to construct a meaningful and low-dimensional feature space. This provides useful patterns allowing straightforward selection of suspicious data.
  • Task 3: Signals captured from rotating machines are known to have specific spectral patterns often modelled through physical or phenomenological models. These models offer an understanding of the baseline (healthy) state as well as potential faults on various organs. Faults are generally identified through their specific signature that departs from the baseline. This task consists in exploiting the available data to construct a model for different states of the system. An interesting way to explore is to construct a spectral cartography of the machine comprising the main and the most influencing components.
  • Task 4: This task consists in constructing condition indicators to monitor the health state of the machine, detect potential faults and identify the faulty components. Those indicators will be constructed based on physical and data-driven approaches. A decision scheme will be then elaborated to establish an accurate diagnosis of the monitored system

More Information

Eligibility criteria

Candidates must meet the eligibility conditions of ETN projects:

no residence in France longer than 12 months in the past 3 years immediately before the date of recruitment

not been involved in research for more than 4 years (full time equivalent) starting to count the date this person graduates his/her first MSc degree.

Offer Requirements

    Engineering: Master Degree or equivalent


Candidates must have completed an M2 level with excellent academic results in signal processing, machine learning and statistics.



Specific Requirements

Candidates must meet the eligibility conditions of ETN projects:

no residence in France longer than 12 months in the past 3 years immediately before the date of recruitment

not been involved in research for more than 4 years (full time equivalent) starting to count the date this person graduates his/her first MSc degree.

Work location(s)
1 position(s) available at

EURAXESS offer ID: 644203


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