Learn the method
Understand Tensor SPC, T², Q, residual energy, and why preserving multiway structure matters.
Open Tensor SPC guide →STATISTICAL ENGINEERING PLATFORM
Statistical process control for structured, high-dimensional data.
Stat-Forge develops tensor-structured SPC methods, interactive examples, and applied Python tools for monitoring complex manufacturing, engineering, and process data.
Featured monograph: Tensor-Structured Statistical Process Control, Current Release.
Zenodo DOI: 10.5281/zenodo.20260650
Recommended Path
Explore the work from concept to application
The site is now organized as a learning and research hub: start with the plain-language explanation, walk through the conceptual example, compare PCA and Tensor SPC, then open the monograph or Python app resources.
See a conceptual example
Follow a sensor × time × condition monitoring workflow using tensor decomposition and T²-Q interpretation.
Open conceptual example →Compare against PCA
See why vectorized PCA is a useful baseline but can hide mode-specific structure.
Open comparison →Run an interactive demo
Use browser-based Python to adjust anomaly strength and see how residual Q responds.
Open interactive demo →Publication
Tensor-Structured Statistical Process Control
Download the current monograph or view the Zenodo record for citation and version history.
Software Direction
Stat-Forge Python App
The site now includes a Python app landing page designed for Streamlit/Railway deployment, demo screenshots, notebooks, and downloadable analytics resources.
Open tools/app page