Why Fixed-Weight Fusion Fails: A Non-Circular Evaluation of LSTM–DBSCAN–XGBoost for Predictive Maintenance

This paper’s real contribution isn’t the LSTM+DBSCAN+XGBoost architecture itself — those are established methods. It’s a methodological finding, made visible only by a non-circular evaluation protocol:
Fixed-weight fusion of a forecaster, an anomaly detector, and a health classifier looks catastrophic (macro-F1 0.592 vs. 0.913 for the best single branch) but that’s almost entirely a measurement artifact, not a real weakness of fixed weights. Once you fuse at the probability level instead of collapsing everything to a scalar first, the same fixed weights recover to 0.892. 0.298 of the 0.322-point gap comes from throwing away class information (scalarization); only 0.024 comes from the weights being fixed rather than learned (statistically confirmed, p=0.003).