CNN-KAN Hybrid Driven Intelligent Vibration Machinery and Vibration State Recognition Method of Edge Deployment

Concrete vibration quality has an outsized effect on structural durability, but construction sites have no reliable way to monitor it in real time. Compounding this, vibration machinery has no self-awareness of its own operating state, so failures and degradation tend to go unnoticed until something goes wrong. The proposed system integrates a Raspberry Pi controller and a hybrid neural network model within the vibrator apparatus itself. The model pairs a 1D CNN with a Kolmogorov–Arnold Network (KAN). The CNN initially conducts the majority of the computational workload: it systematically reduces dimensionality and extracts salient features from extensive time-series data, thereby circumventing the convergence challenges that a KAN encounters when processing unrefined high-dimensional sequences independently. Subsequently, a B-spline-based classification module supersedes the conventional fully connected layer. This innovation is noteworthy; the module is capable of identifying minute damping variations and frequency alterations during the process of concrete liquefaction, accurately distinguishing between states such as “adequate compaction” and “over-vibration,” which may appear nearly indistinguishable in their dynamic responses. The achieved accuracy in vibration state classification was 97.55%, while recognition of no-load conditions reached 98.17%. The system provides millisecond-level active protection against hazardous impacts, effectively reducing equipment wear. With a low implementation cost of approximately 800 RMB and a projected 20% improvement in construction compliance, this work provides reliable technical support for ensuring controllable construction quality and extending equipment service life, offering an efficient solution for the intelligent upgrade of building equipment.