[CDD 3ANS THESE] - Hybrid Approaches to Fault Prognostics in Complex and Uncertain Industrial S[...]

Fault Prognostic, Knowledge Management, Industry 5.0.

Keywords :

Prognostics, Fault Detection, Neuro-Symbolic Methods, Distributed Systems, Data Fusion, Uncertainty Management.

Research Work

Thesis Subject Summary

As the cutting edge in industrial evolution, Industry 5.0 seamlessly fuses human intelligence with advanced technologies to forge highly personalized and hyper-efficient production systems. Prognostics and health management (PHM) techniques stand at the heart of this transformative era, delivering indispensable tools for proactive maintenance and peak performance optimization. In this dynamic landscape, hybrid methods that synergize data-driven approaches with expert knowledge are surfacing as powerful solutions to the intricate challenges of contemporary industrial environments. The capability to manage uncertainty and operate within distributed systems is pivotal to the triumph of these innovative approaches.

This thesis endeavors to pioneer and validate cutting-edge prognostic models that harness neuro-symbolic methods and data fusion, aligning with the ambitious vision of Industry 5.0. The primary objective is to design a robust and sustainable predictive maintenance solution that not only meets but exceeds optimization and efficiency standards, effectively addressing the myriad challenges inherent in industrial maintenance.

Thesis Project

Scientific Context

Industry 5.0 represents the next evolution of the industrial sector, where human intelligence, advanced technologies, and artificial intelligence (AI) converge to create more flexible, efficient, and personalized production systems.

Unlike Industry 4.0, which focused primarily on automation and data exchange, Industry 5.0 emphasizes the collaboration between humans and machines, leveraging the strengths of both to achieve unprecedented levels of productivity and innovation.

In this advanced industrial landscape, prognostics play a crucial role. Prognostics involve predicting the future condition and performance of systems and components, which is essential for proactive maintenance, optimizing operational efficiency, and minimizing downtime. Accurate prognostics enable industries to anticipate failures before they occur, schedule maintenance activities more effectively, and ensure the smooth functioning of production processes. However, the complexity of modern industrial systems presents significant challenges for traditional prognostic methods.

To address the challenges of modern industrial prognostics, a hybrid approach that combines data-driven methods and expert knowledge is essential. Data-driven methods, such as machine learning and statistical analysis, excel at identifying patterns and making predictions based on large datasets. Conversely, expert knowledge encapsulates years of human experience and domain-specific insights, which are invaluable for understanding the underlying mechanisms of system behavior. The fusion of these two approaches can lead to more accurate and robust prognostic models. However, implementing such a hybrid approach in real-world industrial environments introduces additional challenges.

Managing uncertainty is critical for making reliable predictions and informed decisions. To navigate these complexities, neuro-symbolic approaches offer a promising solution. These approaches combine the learning capabilities of neural networks with the logical reasoning and interpretability of symbolic systems.

In summary, this thesis is set against the backdrop of Industry 5.0, where the integration of human and machine intelligence is driving the next wave of industrial innovation. The development of advanced prognostic methods that combine data-driven techniques and expert knowledge, manage uncertainty, and operate in distributed environments is essential for realizing the full potential of Industry 5.0.

Scientific Challenges

The development of advanced prognostic models for Industry 5.0 involves addressing several scientific challenges:

1. Data and Knowledge Fusion: Develop methods to effectively integrate heterogeneous data from various sources and formalize expert knowledge into a unified framework.
2. Neuro-Symbolic Approaches: Design hybrid models that integrate the deep learning capabilities of neural networks with symbolic systems for knowledge management.
3. Uncertainty Management: Develop techniques to quantify and manage uncertainty in prognostic predictions.

Context

Laboratory Presentation :

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction.

Previous Work in the Laboratory :

This thesis aims to push the boundaries of prognostic technologies by integrating innovative hybrid approaches and addressing the challenges posed by uncertainty and distributed environments, thus contributing to the ambitious goals of Industry 5.0.

Work Program :

Steps and Schedule

1. Literature Review and Initial Research (Months 1-3): Conduct an extensive review of existing literature on prognostics, neuro-symbolic methods, and Industry 5.0 technologies.
2. Development of Hybrid Prognostic Models (Months 4-9): Design and implement hybrid models that integrate neural networks with symbolic reasoning systems.
3. Implementation and Testing (Months 10-18): Deploy the models in a simulated industrial environment using CESI LINEACT’s facilities.
4. Real-world Application and Evaluation (Months 19-24): Gather and analyze data to evaluate the performance and robustness of the models.
5. Finalization and Documentation (Months 25-30): Compile the research findings and draft the thesis.

Expected Scientific / Technical Output :

1. Journal Articles and Conference Papers: Publish findings in high-impact journals.
2. Software Tools: Develop and release software tools for prognostics and health management.
3. PhD thesis: Complete and defend the PhD thesis.

Recruitment

Modalities :

• Application should include a detailed Curriculum Vitae.
• A motivation letter explaining your motivations for pursuing a doctoral thesis.
• MASTER 1 and 2 results.

Skills :

• Scientific and Technical Skills: Experience in Machine Learning and Deep Learning.
• Interpersonal Skills: Ability to work in a team and have good interpersonal skills.