Tag: PHM
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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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Self-Supervised Contrastive Learning for Fault Diagnosis
SimCLR contrastive pretraining boosted fault diagnosis from 73% to 89% using 8,000 unlabeled vibration signals. Here's the exact PHM workflow.
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SCADA Data Integration for Multi-Asset PHM Pipelines
MQTT vs direct writes: we tested both on 47 SCADA assets. The hybrid approach cut sync lag 73% and survived 8 months in production.
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Deploying RUL Models: Optimization and PHM Integration
Deploy RUL models without crashing production: quantization cuts inference from 45ms to 8ms. Real metrics, optimization code, and PHM integration.
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RUL Prediction in Python: Linear Regression to LSTM
Build bearing RUL prediction in Python: linear regression baseline to LSTM with NASA C-MAPSS data. Full code, degradation features, performance.
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PHM Intro: What Is RUL Prediction? Concepts to Data Prep
RUL prediction 101: formulate the problem, engineer degradation features from sensor data, and prepare NASA C-MAPSS dataset for model training.
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Attention-Based Sensor Fusion: 15% Better Pump Failure Prediction
Fusing vibration, temperature, and current sensors with Attention boosted pump failure prediction from 79% to 94.2% โ here's the architecture.
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Federated Learning์ผ๋ก ๋ค์ค ๊ณต์ฅ ์ค๋น ๊ณ ์ฅ ์์ธกํ๊ธฐ: ๋ฐ์ดํฐ ๊ณต์ ์์ด AI ๋ชจ๋ธ ํ์ ํ์ต ๊ตฌํ
Train fault prediction across factories without sharing data. Federated Learning with FedAvg โ solve Non-IID issues in PyTorch.