Adaptative Reinforcement Learning Approach for Predictive Maintenance of a Smart Building Lighting System
Conférence : Communications avec actes dans un congrès international
Due to advancements in sensing technologies, enhanced IoT architectures, and expanded connectivity options,
predictive maintenance has emerged as a compelling solution
within the context of Industry 4.0 for industrial systems. However,
within this landscape, such as in Smart Buildings (SBs), the lack
of failure data poses a significant challenge for implementing
traditional data-based approaches documented in the literature.
Additionally, SBs are complex systems of systems, where failures
in one subsystem can propagate and impact other interconnected
systems, adding layers of complexity to maintenance decisionmaking processes. In light of these challenges, this paper proposes
a Reinforcement Learning approach for Predictive Maintenance
tailored for the lighting system within a Smart Building. This
model leverages a Markov Decision Process and Q-learning
adaptation, which adjusts based on the life cycle of system
components. The results demonstrate that, even with limited
system data, monitoring the system’s operational status effectively is feasible, meeting defined quality of service standards
and minimizing maintenance costs.