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HdR :"Data Mining Driven Approaches for Predictive Maintenance, Inventive Design Modelling and Energy Storage Systems"

Le 23 novembre 2023
À 09h00
Art et Industrie amphitheater at INSA Strasbourg (Campus esplanade)

HDR : Ahmed Samet

Team : SDC

Date & hours : November 23rd at 9 AM

Place : Art et Industrie amphitheater at INSA Strasbourg (Campus esplanade)

Title:  "Data Mining Driven Approaches for Predictive Maintenance, Inventive Design Modelling and Energy Storage Systems"

In this thesis, we focus on three distinct applications based on data mining and machine
learning, which are: (i) predictive maintenance, (ii)inventive design modelling and (iii) energy
efficiency. In these three applications, we investigate the use of data mining techniques for
industry 4.0. Indeed, data mining is a well known sub-discipline of machine learning (ML) that
aims to extract knowledge from large volume of data and find knowledge when human are
incapable to do so.
A first interaction is explored between data mining and predictive maintenance. In this topic,
we investigate the use of pattern mining for explaining the failure happening in production lines.
In the context of predictive maintenance in smart factories, pattern mining has been widely used
to discover frequently occurring temporally-constrained patterns, through which warning
signals can be sent to humans for a timely intervention (Dousson and Duong, 1999a). Among
pattern mining techniques, chronicle mining has been applied to industrial data sets for
extracting temporal information of events (Cram, Mathern, and Mille, 2012). The extracted
temporal information is valuable for predicting potential machinery failures that may appear in
the future (Cram, Mathern, and Mille, 2012). However, even though chronicle mining results
are expressive and interpretable representations of complex temporal information, domain
knowledge is required for users to have a comprehensive understanding of the mined chronicles
(Pei, Han, and Wang, 2002).
A second interaction explores the interaction of text mining and TRIZ theory. The goal is to
analyze patent documents to extract TRIZ contradiction. A contradiction is a domain-free
formulation of a problem. When comparing problems from different fields, the problem that
arises is the vocabulary used to describe the problems, which is fundamentally different for a
problem in mechanics and a problem in chemistry. Thus, Altshuller (Altshuller, 1999)
introduced general parameters (called TRIZ parameters) which can apply to all domains to
describe problems (Weight of Stationary Objects, Speed, Power, Waste of Energy...). A
contradiction from TRIZ domain is seen as a parameter improvement which leads to another
parameter degradation and compromising between these two parameters is not the appropriate
path to invent. One should find a solution that both improve the first parameter while also
improving the second. The matrix gathers the inventive principles (i.e. the inventive paths to
follow) that statistically have the best chance of success on every possible contradiction
between TRIZ parameters. The idea, in this thesis, is to mine and find patents sharing the same
A third interaction consists in analyzing Lithium-ion batteries in order to predict the
Remaining Useful Life (RUL). The goal is in line with the topic of energy efficiency in industry
4.0. Indeed, we assume that when the RUL is predictable, the user could be notified and
recommendation could be made to extend it. To do so, we focus on analyzing Lithium-ion
batteries cells RUL using user usage data such as current, voltage and/or temperature. The
problem is then assumed to be a regression problem using exogenous variables. To the best of
our knowledge, the problem is not sufficiently investigated in literature. Therefore, we propose
a two folded contributions. First, we aim using regressive neural networks model to predict the
RUL using the usage data. In this part, we investigate, in particular, how to compress the data
of the usage time series data. In the second step, we intend to make these black boxes more
explainable. We add a layer of SHAP model (Lundberg and Lee, 2017) on top of a predictive
model to explain the relation between the predicted output and the inputs.

The jury will be composed of :


Robert J. Howlett, Professeur, Director of KES International Research

Salem Benfarhat, Professeur, Université d’Artois

Bruno Cremeilleux, Professeur, Université de Caen Normandie



Hubert Cardot, Professeur, Ecole Polytechnique de l’Université de Tours

Pierre Gançarski, Professeur, Université de Strasbourg

Osmar R. Zaiane, Professeur, University of Alberta

Claudia Hentschel, Professeur, HTW Berlin, University of Applied Sciences

Denis Cavallucci, Professeur, INSA Strasbourg

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