AI-Driven Predictive Maintenance and Energy-Efficient Robotics for Adaptive Production Systems

Abstract

Adaptive production systems—characterized by dynamic product variety, fluctuating demand, and complex multi‐stage processes—face
constant challenges in maintaining high uptime, minimized energy consumption, and fast response to failures. Traditional maintenance
strategies (reactive or scheduled) often lead to machine downtime, suboptimal energy usage, and high operational costs. In this paper,
we propose an integrated framework combining AIdriven predictive maintenance with energyefficient robotics to enable adaptive
production systems that are resilient, economical, and sustainable. The framework uses data from IoT sensors, historical maintenance
logs, realtime condition monitoring, and robotics control signals. AI models—especially deep learning (e.g. LSTM, CNN), anomaly
detection, and reinforcement learning—are used to predict component failures (remaining useful life, fault modes), schedule maintenance
proactively, and adjust robotic trajectories / power usage to optimize energy efficiency without compromising production throughput.
Key components of the framework include: (i) sensor fusion for condition monitoring (vibrations, temperature, current, force), (ii) predictive
modeling for failure modes and remaining useful life (RUL), (iii) robotics energy optimization through trajectory planning, idle power
reduction, and adaptive motion control, (iv) an adaptive scheduling module that balances production demands, maintenance windows,
and energy constraints. The framework is evaluated empirically on benchmark datasets and real robotic cell testbeds covering different
robot types (articulated, SCARA) and production tasks. Results show that predictive maintenance can reduce unexpected downtime by
up to 3050%, while energy‐efficient robotic strategies can reduce energy consumption in robotic operations by 2035%, with minimal
impact (< 5%) on throughput. Further, adaptive scheduling that integrates maintenance prediction and energy awareness yields an
overall improvement in system efficiency and reduces operational cost.
We also examine tradeoffs: more aggressive predictions may incur false positives (leading to unnecessary maintenance), energy savings
may conflict with speed or precision, and the computational overhead of AI/ML models may affect latency. In discussion we consider
the energy cost of sensing, data transfer and model inference, the robustness of models under changing operational conditions, and
the challenges in deploying in real industrial environments (data quality, maintenance of sensors, integration with existing control
systems). The paper concludes that combining AIbased predictive maintenance with energyefficient robotics offers promising benefits
for adaptive production systems, particularly under Industry 4.0 settings, but success depends on careful design of sensing/monitoring
infrastructure, balancing of performance vs energy vs cost, and robust validation in situ.