Fault-class coverage–aligned combined training for AFDD of AHUs across multiple buildings

Abstract

Abstract Deep learning–based Automated Fault Detection and Diagnosis (AFDD) for Air Handling Units (AHUs) has often performed well. However, most prior studies relied on single-building datasets and fixed feature schemas, limiting applicability to new sites. This study evaluated TabTransformer and TabNet on a unified multi-building dataset pooling auditorium, hospital, and office data to examine coverage-aligned combined training across buildings. Extensive hyperparameter optimization covering 3240 models and systematic analyses were performed, including checks for underfitting and overfitting, assessment of validation–test variation, best-model selection, attention heatmaps, and comparisons with non-attention baselines. The optimized TabNet trained on unified data achieved, for the auditorium, an F1 score of 97.43% and an accuracy of 97.91%; for the hospital, an F1 score of 92.01% and an accuracy of 92.50%; and for the office, an F1 score of 92.25% and an accuracy of 92.46%. Single-building TabNet baselines reached 96.82% F1 and 97.37% accuracy for the auditorium, 95.40% F1 and 97.21% accuracy for the hospital, and 96.27% F1 and 97.29% accuracy for the office. Across all three buildings, gains from combined training arose primarily under strong coverage alignment of fault classes between the training and target sets; when alignment was weak, the gains diminished.