KU Leuven

Acoustic monitoring of (fleets of) machines and vehicles

2022-12-07
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KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.

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(ref. BAP-2022-785)

The proposed research track runs at the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division which currently counts more than 100 researchers and is part of the Department of Mechanical Engineering, a vibrant environment of more than 300 researchers (www.mech.kuleuven.be). Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). LMSD has a longstanding history and internationally highly recognized expertise in the fields of condition monitoring, numerical modeling, engineering dynamics, automotive engineering, vibro-acoustic analysis, identification and robust optimal control of (non-) linear systems, active control and lightweight structure design and analysis. It is also recognized for its yearly Modal Analysis (ISMA) and Acoustics (ISAAC) courses and for organizing the biennial ISMA Noise and Vibration Engineering Conference (www.isma-isaac.be). The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. Furthermore, the group contributes to the Dynamics of Mechanical and Mechatronic Systems (DMMS) University Core Lab of Flanders Make. Flanders Make (https://www.flandersmake.be/en) is the strategic research centre for the manufacturing industry in Flanders, stimulating open innovation through excellent research. The research group's international research flavour is illustrated amongst others by the large portfolio of research projects (https://www.mech.kuleuven.be/en/mod/Projects) which includes regional, national and international funded activities through which the group cooperates with leading mechatronic and machine & vehicle-building companies in Flanders and throughout Europe. More information on the research group can be found on the website: https://www.mech.kuleuven.be/en/research/mod/about and our Linked.In page: https://www.linkedin.com/showcase/noise-&-vibration-research-group/. The PhD will be co-supervised by Prof. Konstantinos Gryllias and Dr. Hervé Denayer.

Website unit

Project

In industry, monitoring is often used to improve the productivity of for example manufacturing systems. Such techniques aim at tracking the state of a machine or system and detecting anomalies before an unexpected failure, which often leads to a high cost and long downtime, would occur. A common approach is the analysis of vibration signals. However, vibration measurements require the installation of sensors on the machine of interest and often only provide information on the components close to the sensor. Applying vibration-based monitoring approaches on complex systems, therefore, requires the installation of a large number of sensors. For these reasons, industry is actively researching alternative methods that can significantly reduce the cost of the monitoring system. A promising approach is the use of acoustic signals, which can be measured remotely with microphones and which are known to contain information on the state of a system as a whole. Hence, such acoustic monitoring approaches offer new possibilities for applications monitoring complex machines or fleets of vehicles, where the installation of a large number of (vibration) sensors is practically and/or economically not feasible. However, the current acoustic monitoring systems struggle with two main challenges. First of all, it is not straightforward to extract the relevant information from acoustic signals, which are affected by other nearby noise sources and which often lead to a very poor signal-to-noise ratio. Secondly, acoustic signals depend not only on the system dynamics of interest, which act as a source of sound, but also on the interactions between these sources and the acoustic environment. This makes it difficult to deploy a developed monitoring solution in different industrial environments. Therefore, this PhD track targets to develop a more robust and easier to deploy acoustic monitoring toolchain for the detection of anomalies in machines and (fleets of) vehicles, based on novel acoustic measurement approaches, signal processing techniques and machine learning methods, supported by advanced (vibro-)acoustic models. The developed methodologies will be tested and validated on in-house test rigs, exploiting the state of the art measurement equipment available within the research group.

Profile

If you recognize yourself in the story below, then you have the profile that fits the project and the research group.

  • I have a master degree in engineering, physics, computer science or mathematics and performed above average in comparison to my peers.
  • I am proficient in written and spoken English.
  • I have a genuine interest in combining advanced signal processing methods, acoustic models, machine learning techniques and vibroacoustic measurement approaches into an innovative toolchain and I have experience with (at least) some of these topics.
  • I have good programming skills in Matlab and/or in Python (in particular Tensorflow or Pytorch).
  • As a PhD researcher of the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
  • Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
  • In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
  • I value being part of a large research group which is well connected to the machine and transportation industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
  • During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.

Offer

  • A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
  • An opportunity to pursue a PhD in Mechanical Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment.
  • Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/dopl/whytraining.
  • A stay in a vibrant environment in the hearth of Europe. The university is located in Leuven, a town of approximately 100000 inhabitants, located close to Brussels (25km), and 20 minutes by train from Brussels International Airport. This strategic positioning and the strong presence of the university, international research centers, and industry, lead to a safe town with high quality of life, welcome to non-Dutch speaking people and with ample opportunities for social and sport activities. The mixture of cultures and research fields are some of the ingredients making the university of Leuven the most innovative university in Europe (KU Leuven is the Most Innovative University of Europe – Faculty of Arts). Further information can be found on the website of the university: https://www.kuleuven.be/english/living.

Interested?

For more information please contact Prof. dr. ir. Konstantinos Gryllias, tel.: +32 16 32 30 00, mail: konstantinos.gryllias@kuleuven.be or Mr. Hervé Denayer, tel.: +32 16 37 28 27, mail: herve.denayer@kuleuven.be.

You can apply for this job no later than December 07, 2022 via the online application tool

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

Informatie over de vacature

Functienaam
Acoustic monitoring of (fleets of) machines and vehicles
Werkgever
Locatie
Oude Markt 13 Leuven, België
Gepubliceerd
2022-11-08
Uiterste sollicitatiedatum
2022-12-07
Soort functie
PhD
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