This project extends the v2-0 Hybrid Model (statistical features + TTM embeddings) by adding a third modality: text embeddings derived from equipment master data. The three feature vectors — statistical x ∈ ℝ²⁸, TTM embedding y ∈ ℝ⁶⁴, and text embedding z ∈ ℝ¹⁰²⁴ — are concatenated into a 1,116-dimensional triplet feature h and fed into a LightGBM.
time-series lightgbm tsne hvac dbscan umap triplet-loss gradient-boosting anomaly-detection predictive-maintenance feature-fusion text-embeddings multilingual-e5
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Updated
Feb 21, 2026 - Python