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

Scientific Fields:

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 [20]. 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 [8, 14]. The capability to manage uncertainty and operate within distributed systems is pivotal to the triumph of these innovative approaches [9].

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 [7, 17].

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 [19].

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 [16]. 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. The data generated by these systems are vast, diverse, and often noisy. Additionally, the integration of various subsystems and the interactions between them introduce layers of complexity that are difficult to model and predict using conventional techniques [22].

Thesis Subject:

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 [21]. The fusion of these two approaches can lead to more accurate and robust prognostic models [18]. However, implementing such a hybrid approach in real-world industrial environments introduces additional challenges. These environments are often distributed, with various components located across different geographical locations. This distribution requires solutions that can process data and execute models in a decentralized manner while ensuring consistency and reliability [23]. Moreover, uncertainty is an inherent aspect of industrial prognostics. Uncertainty arises from various sources, including measurement noise, incomplete data, and the stochastic nature of many industrial processes.

Managing this uncertainty is critical for making reliable predictions and informed decisions [3]. 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 [7].

By leveraging the strengths of both, neuro-symbolic methods can enhance the accuracy and explainability of prognostic models, making them more suitable for complex industrial applications [10].

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. This thesis aims to contribute to this vision by developing and validating hybrid prognostic approaches that address these challenges and advance the state of the art in industrial prognostics.

Scientific Challenges

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

These challenges encompass the integration of diverse data sources, the creation of hybrid neuro-symbolic models, the management of uncertainty, the design of distributed environments, and the practical application of these approaches in real-world industrial settings. Below are the key scientific challenges to be tackled:

1. Data and Knowledge Fusion

• Develop methods to effectively integrate heterogeneous data from various sources and formalize expert knowledge into a unified framework.
• Address conflicts and inconsistencies between data and knowledge, and explore new methods for knowledge integration.

2. Neuro-Symbolic Approaches

• Design hybrid models: One major challenge is to effectively design hybrid models that integrate the deep learning capabilities of neural networks with symbolic systems for knowledge management. This requires finding a balance between the strengths of both approaches, ensuring seamless interaction, and overcoming the inherent differences in their methodologies.
• Ensure explainability and traceability: Another significant hurdle is to ensure the explainability and traceability of decisions made by these hybrid models. While deep learning models, particularly neural networks, are often seen as "black boxes" with little transparency, symbolic systems are known for their clarity and logic. The challenge lies in making the complex decision-making processes of these hybrid models comprehensible and ensuring that each step can be traced back and justified.

3. Uncertainty Management

• Develop techniques to quantify and manage uncertainty in prognostic predictions, using uncertainty management frameworks such as belief function theory.

4. Distributed Environments

• Design distributed architectures for data processing and the execution of prognostic models.

5. Application to Industry 5.0

• Demonstrate the effectiveness of the proposed approaches in real-world use cases within the industry.

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. The historical proximity of CESI with companies is a determining element for our research activities, and has led us to concentrate our efforts on applied research close to the company and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research; it…

Thesis Organization

• Funding: CESI (100%)
• Workplace: Campus CESI Villeurbanne (Lyon) and Campus CESI Nanterre (Paris)
• Start Date: February 2025
• Duration: 3 years

Recruitment

Modalities:

Application should include:

• Detailed Curriculum Vitae. In case of a break in academic studies, please provide an explanation;
• A motivation letter explaining your motivations for pursuing a doctoral thesis;
• MASTER 1 and 2 results;
• Any other documents you consider useful (recommendation letter, etc.).

Please submit all documents in one document.

Skills:

Scientific and Technical Skills:

• Artificial Intelligence: Experience in Machine Learning and Deep Learning,
• Programming: Proficiency in several languages: Object Oriented, Python and IA librairies,
• Modeling, Knowledge representation and Reasoning ( rules-based and Expert systems, ontologies).

Interpersonal Skills:

• Being autonomous, having initiative and curiosity,
• Ability to work in a team and have good interpersonal skills,
• Being rigorous.

References

[1] Fidma Mohamed Abdelillah, Hamour Nora, Samir Ouchani, and Sidi Mohamed Benslimane. Predictive maintenance approaches in industry 4.0: A systematic literature review. In IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2023, Paris, France, December 14-16, 2023, pages 1-6. IEEE, 2023.

[2] Fidma Mohamed Abdelillah, Hamour Nora, and Samir Ouchani and Sidi Mohamed Benslimane. Hybrid data-driven and knowledge-based predictive maintenance framework in the context of industry 4.0. In Model and Data Engineering - 12th International Conference, MEDI 2023, Sousse, Tunisia, November 2-4, 2023, Proceedings, volume 14396 of Lecture Notes in Computer Science, pages 319-337. Springer, 2023.

[3] Safa Ben Ayed, Roozbeh Sadeghian, and Rachid Hamza. Remaining useful life prediction with uncertainty quantification using evidential deep learning. [Reviewing in progress].

[4] Safa Ben Ayed, Malika Ben Khalifa, and Samir Ouchani. Modeling distributed and flexible phm framework based on the belief function theory. In IFIP International Conference on Artificial Intelligence Applications and Innovations, 2024.

[5] Ibrahima Barry and Meriem Hafsi. Towards hybrid predictive maintenance for aircraft engine: Embracing an ontological-data approach. In 20th ACS/IEEE International Conference On Computer Systems And Applications, December 2023. URL https://hal.science/hal-04369673.

[6] Mohamed-Amin Benatia, Meriem Hafsi, and Safa Ben Ayed. A continual learning approach for failure prediction under non-stationary conditions: Application to condition monitoring data streams. [Reviewing in progress].

[7] T. R. Besold, A. d'Avila Garcez, S. Bader, H. Bowman, P. Domingos, P. Hitzler, and G. Zaverucha. Neural-symbolic learning and reasoning: A survey and interpretation 1. In Neuro-Symbolic Artificial Intelligence: The State of the Art, pages 1-51. IOS press, 2021.

[8] Q. Cao, C. Zanni-Merk, A. Samet, C. Reich, F. Beuvron, A. Beckmann, and C. Giannetti. Kspmi: A knowledge-based system for predictive maintenance in industry 4.0. Robotics And Computer-Integrated Manufacturing, 74:102281, 2022.

[9] C. Chen, J. Shi, N. Lu, Z. Zhu, and B. Jiang. Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction. Neurocomputing, 494:79-88, 2022.

[10] A. d. Garcez, M. Gori, L. C. Lamb, L. Serafini, M. Spranger, and S. N. Tran. Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. ArXiv preprint arXiv:2102.12344, 2021.

[11] Meriem Hafsi. Exploring a knowledge-based approach for predictive maintenance of aircraft engines: Studying fault propagation through spatial and topological component relationships. In 8th European Conference Of The Prognostics And Health Management Society 2024, volume 8, July

2024. URL https://hal.science/hal-04649557.

[12] Meriem Hafsi, Nora Hamour, and Samir Ouchani. Advancing industry: A systematic literature review of recent developments and perspectives in predictive maintenance for cps. [Reviewing in progress].

[13] Meriem Hafsi, Nora Hamour, and Samir Ouchani. Predictive maintenance for smart industrial systems: A roadmap. In The 6th International

Conference On Emerging Data And Industry (EDI40), March 2023. URL https://hal.science/hal-04398272.

[14] S. Hagmeyer, P. Zeiler, and M. Huber. On the integration of fundamental knowledge about degradation processes into data-driven diagnostics and prognostics using theory-guided data science. In PHM Society European Conference, volume 7, pages 156-165, 2022.

[15] Rachid Hamza and Safa Ben Ayed. Supervised learning based approach for turbofan engines failure prognosis within the belief function framework. In International Conference on Applied Computing, 2022.

[16] Y. Lei, N. Li, L. Guo, Z. Xu, N. Li, and T. Yan. Machinery health prognostics: A systematic review from data acquisition to rul prediction.

Mechanical Systems and Signal Processing, 104761, 2020.

[17] A. Liguori, S. Mungari, E. Ritacco, F. Ricca, G. Manco, and S. Iiritano. Neuro-symbolic techniques for predictive maintenance, 2023.

[18] Q. Liu, W. Zhang, B. Wang, and J. He. A hybrid prognostic model for predicting remaining useful life of mechanical systems. Reliability Engineering & System Safety, 208:107399, 2021.

[19] P. K. R. Maddikunta, Q. V. Pham, S. M. Basha, and T. R. Gadekallu. Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26:100257, 2022.

[20] R. Oudenhoven and E. Demerouti. Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. International Journal Of Production Research, 61:7846-7865, 2023. doi: 10.1080/00207543.2022.2154403.

[21] J. Z. Sikorska, M. Hodkiewicz, and L. Ma. Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 144:106860, 2021.

[22] R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao. Deep learning and its applications to machine health monitoring: A survey. IEEE Transactions on Neural Networks and Learning Systems, 33(3):1094-1113, 2021.

[23] K. Zhou, L. Zhang, and S. Yang. Cyber-physical-social system in intelligent manufacturing. Automation in Construction, 137:104164, 2022.

CESI est une école d'ingénieurs qui fait de la promotion sociale par l'excellence un modèle de réussite. Rejoignez un environnement stimulant où l'esprit d'équipe, la diversité des projets et l'autonomie ne font qu'un. Découvrez une école qui a su développer un modèle unique et se donne les moyens au quotidien de relever les grands défis de l'époque. Nos 25 campus, 28 000 étudiants, 8000 entreprises partenaires et 106 000 alumni témoignent de l'impact de CESI au niveau national.

CESI accompagne ses étudiants en utilisant des méthodes innovantes de pédagogie active. L'établissement forme avec rigueur les futurs ingénieurs, techniciens et managers, dans les secteurs suivants : l'Industrie & l'Innovation, le BTP, l'Informatique et le Numérique et le Développement Durable. Parallèlement, CESI concrétise son engagement dans la Recherche à travers des activités menées au sein de son Laboratoire d'Innovation Numérique, CESI LINEACT.

Les partenariats établis avec 130 universités à travers le globe, attestent de l'engagement international de CESI. Ces liens privilégiés offrent aux élèves ingénieurs une mobilité sortante et entrante à l'échelle internationale, façonnée notamment par des stages obligatoires faisant partie intégrante de leur cursus.