Tag: Predictive Maintenance
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Rule-Based to ML PHM Migration: 4-Week Python Roadmap
Cut false alarms by 60% and get 2-week failure warnings. Step-by-step migration from threshold rules to ML models with real Python code.
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LSTM Encoder-Decoder vs Seq2Seq Transformer: CMAPSS RUL Benchmark
LSTM Encoder-Decoder vs Seq2Seq Transformer on NASA CMAPSS: 18% RMSE improvement on FD004 but LSTM wins on simple data. Full PyTorch benchmark inside.
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GNN for Multi-Component RUL: Graph Attention Beats Fixed Topology
Learn how Graph Attention Networks predict multi-component system failures better than fixed-topology GNNs. Attention mechanism key to RUL accuracy.
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CBM Migration Guide: Scheduled to Condition-Based
CBM migration forces you to rethink alerting, data pipelines, and ops workflows โ not just swap sensors. Here's what actually breaks and how to fix it.
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FFT Analysis for Bearing Fault Detection: 2048-Point Setup
Master FFT-based bearing fault diagnosis: 2048-point setup, frequency bin analysis, and practical vibration signal processing for predictive maintenance
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CWRU Bearing Dataset: End-to-End PHM Portfolio Project
Build a complete PHM pipeline with CWRU bearing data: fault detection, feature engineering, and RUL prediction from raw vibration signals.
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Gearbox Fault Classification: Why Voting Beat Stacking
Soft voting beat stacking by 0.7% on real gearbox fault data. Why simpler ensembles outperform complex ones in multi-fault PHM systems.
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Kalman vs Particle Filter for Bearing State Estimation
Kalman vs particle filter for bearing state estimation: real latency numbers on edge hardware, tuning tricks, and when each one breaks down.