Collaborative Predictive Maintenance for Smart Manufacturing : From Wireless Control to Federated Learning

Abstract: Industry 4.0 represents a significant shift in the industrial landscape, aimed at improving efficiency, productivity, and competitiveness. This shift involves the digitalization of industries, impacting manufacturing and maintenance processes. A pivotal element of this transformation is the development of Cyber-Physical Systems (CPS) that seamlessly connect the physical factory floor with the digital realm. These systems monitor real-time data from the physical world and prepare feedback from the digital space, necessitating the harmonious integration of computation and communication, especially through wireless technology. Simultaneously, Machine Learning (ML) methods are advancing across various domains. The proliferation of wireless sensors and the Internet of Things, particularly within the CPS framework, generates substantial data. To address challenges such as latency, device resource limitations, and privacy concerns associated with centralized cloud processing, there is a shift towards edge computing, enabling distributed learning algorithms.This dissertation tackles these challenges with four innovative methods that combine wireless technology, control systems, and distributed ML in the context of Industry 4.0. These methods aim to harness the potential of this digital transformation, making Predictive Maintenance (PdM) in industries smarter and more efficient. The first method, parallel event-triggering, is designed for multi-agent systems in industrial environments. It utilizes distributed event-based state estimation to enhance control performance and reduce network resource consumption. The second and third methods are developed for collaborative PdM using wireless communication in a federated approach. The second method focuses on real-time anomaly detection while preserving asset privacy at the edge level, and the third method optimizes remaining useful life prediction from sequential data within a federated learning framework. Both federated approaches enhance efficiency, simplify communication, and improve local model convergence. The fourth method introduces an innovative approach to collaborative PdM, utilizing over-the-air computation at the edge level. This approach offers low latency and improved spectral efficiency. The optimization challenges at the edge level are addressed by using a modified gradient descent approach, which effectively handles noisy communication channels and improves the convergence of ML algorithms. All four methods proposed in this thesis underwent a comprehensive evaluation,and the experimental findings demonstrate their effectiveness in achieving their intended objectives.