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

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…

Location: Villeurbanne, FR

Posted Date: 6/16/2025