Sensor placement design (SPD) for distributed parameter systems (DPSs) remains challenging due to the vast number of potential sensor locations and the associated deployment costs. Traditional SPD methods, such as those based on observability and Kalman filters, are limited by assumptions of linearity and low sensor counts, which can be impractical in complex industrial environments. In this work, we propose a diffusion-attention-based approach that is fully data-driven, eliminating the need for explicit numerical models of the system. Our approach integrates a diffusion model—capable of progressively denoising corrupted data—and an attention mechanism that identifies the most informative sensor locations. By prioritizing sensors with higher attention weights, we ensure accurate reconstruction of the unobserved states despite using relatively few measurement points. We validate the proposed method on two benchmark DPSs, the catalytic rod and the Brusselator. Results demonstrate that our algorithm achieves sufficient accuracy in both state reconstruction and fault detection. Furthermore, the approach scales naturally to scenarios where certain states can be easily measured, thus enhancing performance.