Modern electromechanical systems increasingly operate in dynamic, uncertain environments where conventional control and monitoring pipelines are insufficient for ensuring reliability, efficiency, and autonomy. Most existing approaches treat condition monitoring and control as isolated processes and rely heavily on static models and offline analysis. This paper introduces a unified neuro-adaptive machine framework designed to endow machines with real-time perception, health awareness, and self-optimizing control capabilities directly at the edge.

The proposed architecture integrates multi-modal sensor fusion (vibration, acoustic, thermal, and electrical signals) with a lightweight deep learning pipeline deployed on embedded hardware. A sparse neural representation layer performs continuous feature extraction, while a continual learning module tracks machine state evolution and detects emerging fault patterns. These learned health states are coupled with a predictive control module that dynamically adjusts operating parameters to mitigate degradation. Drift-aware training and incremental updating enable the system to adapt autonomously to changing operating conditions without cloud dependency.

Experimental evaluation on an electromechanical test platform demonstrates that the framework achieves earlier fault detection, higher diagnostic accuracy, and faster adaptation to unseen anomalies compared to traditional signal-processing-based monitoring and fixed-parameter control strategies. The system also exhibits improved operational stability and measurable gains in energy efficiency through health-aware control adjustments.

The results validate the feasibility of embedding neuro-adaptive intelligence directly into machines to move from passive monitoring toward proactive, self-optimizing operation. The proposed framework offers a scalable pathway toward cognitive electromechanical systems, contributing to advances in automation, condition monitoring, and intelligent machine design.