Intelligent Real-Time Automatic Lubrication Using Microcontrollers

The transition of asset monitoring and predictive maintenance to Edge Computing devices faces a severe technological bottleneck: intensive mathematical processing on low-cost microcontrollers. This technical report presents a novel non-blocking software architecture to solve this challenge, enabling prescriptive autonomous lubrication on the shop floor. This work demonstrates how to restructure mathematical models traditionally executed in the cloud or simulation software (such as MATLAB) to operate within the memory and processing constraints of RISC cores (like the ESP32). The methodology encompasses the mechanical elimination of sampling jitter (mitigating spectral leakage), direct analytical envelope extraction via the Hilbert Transform in the frequency domain, and the application of Welford's Stochastic Algorithm, ensuring memory complexity. The model is encapsulated within a strictly asynchronous Finite State Machine (FSM), unified by the proposed General Equation of Embedded Autonomous Lubrication. This formalism converts constrained hardware into deterministic analytical instruments, marking the transition from traditional time-based lubrication to intelligent tribological actuation, driven in real-time by the actual elastohydrodynamic conditions of industrial machinery.