Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
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A review on machinery diagnostics and prognostics implement- ing condition-based maintenance
4 Pith papers cite this work, alongside 4,529 external citations. Polarity classification is still indexing.
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Contextual fusion plus per-vehicle fine-tuning detects all six wear-driven service events on four real vehicles with 12.2-day mean MAE, while synthetic ablation credits environmental features a 2.6-point F1 gain.
On a 10-bearing PHME subset, a residual-calibrated fusion model hits ~0.15 normalized MAE and 0.90 average 90% coverage under leave-regime-out splits, while conditional diagnostics expose 0.666 coverage and raw-channel-loss collapse.
CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
citing papers explorer
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Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains
Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
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AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
Contextual fusion plus per-vehicle fine-tuning detects all six wear-driven service events on four real vehicles with 12.2-day mean MAE, while synthetic ablation credits environmental features a 2.6-point F1 gain.
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Empirical Calibration and Conditional-Reliability Diagnostics for Bearing RUL Prediction under Operating-Regime Shift
On a 10-bearing PHME subset, a residual-calibrated fusion model hits ~0.15 normalized MAE and 0.90 average 90% coverage under leave-regime-out splits, while conditional diagnostics expose 0.666 coverage and raw-channel-loss collapse.
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Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.