This project demonstrates a structured analytics framework for quantifying process instability and operational risk in solar ingot puller operations.
Using engineered control deviation (SP–PV gaps), rolling variability metrics, and drift features derived from equipment telemetry, the pipeline identifies high-risk operating regimes and isolates interpretable instability drivers.
The repository uses fully synthetic data for demonstration purposes. The analytics methodology mirrors real-world industrial implementation while preserving data confidentiality.
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Industrial process understanding (crystal growth / puller operations)
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Time-series feature engineering for stability quantification
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Risk regime segmentation using composite stability indices
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Leakage-aware regression validation using interpretable models
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Corporate-grade data governance and anonymization practices
This workflow illustrates the structured progression from raw telemetry to engineered instability features, stability quantification, risk regime segmentation, and interpretable regression validation.