Cross-Node Fault Diagnosis of Solar Insecticidal Lamp IoT Equipment for Reliable Precision Pest Monitoring Using a Diagnosability-Aware Health Baseline

Reliable solar insecticidal lamp Internet of Things (SIL-IoT) equipment underpins green pest control and precise pest monitoring. Healthy ranges of light, photovoltaic and thermal variables shift with daylight, weather, energy availability and installation conditions, while labelled faults remain scarce and imbalanced. We introduce a diagnosability-aware operating-state health baseline (DA-OHB) for calibration-based cross-node diagnosis of data-observable faults in SIL-IoT equipment. Candidate faults were screened by data observability, mechanistic expressibility and availability as curated telemetry event labels. Photovoltaic–light, electrical-box/air-temperature, power and rolling-state features were combined with operating-state gates, direction-sensitive evidence scores and target-node healthy false-alarm calibration. We evaluated DA-OHB on July–August 2025 field records from four devices deployed in Chuzhou, China, for three maintenance-relevant faults: light-intensity sensor open circuit, light-intensity/solar-panel-current mismatch, and electrical-box/air-temperature mismatch. Under leave-one-device-out aggregation with an early healthy calibration subset from each target node, mean F1-scores were 0.996, 0.823 and 0.770; mean area under the precision–recall curve values were 1.000, 0.890 and 0.963. Across five seeds, DA-OHB F1-scores were 0.996 ± 0.000, 0.823 ± 0.000 and 0.763 ± 0.004. F1 reflects the target-node mechanism with sufficient fault evidence, whereas F2 and F3 demonstrate cross-node evidence from multiple devices. Field diagnosis of SIL-IoT equipment thus benefits from linking alarms to valid operating states, fault directions and node-specific healthy calibration. DA-OHB provides an interpretable basis for SIL-IoT maintenance under curated telemetry labels; effects on pest-count estimates and agricultural decisions require separate evaluation.