Open-set fault diagnosis for solar insecticidal lamp IoT systems using adaptive prototypes

Reliable fault diagnosis of field-deployed intelligent agricultural equipment requires not only accurate recognition of known faults but also reliable rejection of previously unseen fault conditions encountered after deployment. This challenge is particularly pronounced in solar insecticidal lamp Internet of Things (SIL-IoTs) systems, where environmental variability, component aging, and operational disturbances can induce substantial intra-class variability and compromise the reliability of conventional closed-set diagnosis. To address this challenge, this article proposes a prototype-based open-set fault diagnosis framework for SIL-IoTs systems. A temporal encoder is trained to extract discriminative representations from multichannel electrical signals. Within the learned feature space, class prototypes are constructed for known faults, and an uncertainty-aware prototype distance (UAPD) is introduced to account for class-wise feature variance during prototype matching. A class-adaptive conformal thresholding (CACT) strategy is further developed to estimate an independent acceptance threshold for each known class using only calibration samples from known classes, thereby enabling open-set rejection without requiring real unknown faults for threshold tuning. Experiments on five open-set diagnosis tasks show that the proposed method achieves the highest overall open-set accuracy across all tasks and the highest H-score in four tasks, with absolute gains of up to 0.0377 over the strongest baseline. These results demonstrate that the proposed framework provides an effective and practically applicable solution for open-set fault diagnosis in field-deployed SIL-IoTs systems.