Monitoring Diagnostics Toolkit 0.17.1 — English documentation

Documentation patch release; diagnostic calculation logic is unchanged.

• Full English README covering every equipment module, purpose, sensor roles and units, parameters, calculated outputs, assumptions and evidence limits.
• Korean README and 0.17.0 verification/publication records preserved. Explicit RO pressure/temperature roles and staged chiller sensors are listed.
• Installed-wheel README example and synthetic CLI fit/run pass. Local regression/physics: 286 tests; scoped existing-project integrations: 7 tests. Core AST continuity with evaluated 0.16.0 is retained after docstring/version normalization. No new measured fault-validation claim.
• Version-aware packaging checks and a version/hash manifest preserve identical reviewed artefacts across GitHub, PyPI and Zenodo.

Zenodo 0.17.1: https://doi.org/10.5281/zenodo.23993237

Original code/docs: MIT. Raw external data, papers, upstream source and fitted corpus profiles are excluded. Research alpha: alarms are not confirmed fault causes or field safety certification.

Monitoring Diagnostics Toolkit 0.17.0 — public research alpha

Initial public release of the original equipment-monitoring toolkit.

• Eleven equipment diagnostic kinds plus Python modules for normal residuals, causal evidence, filter service-risk, physical input preparation and alarm policies.
• Detailed README tables describe each module purpose, sensor roles/units, parameters, outputs and applicability. API reference, user/model guides, retrospective development history, rejected candidates, provenance, reproduction and research handoff are included.
• Local software/physics regression: 286 passing tests; scoped upstream object integrations: 7 passing tests. Public Windows/Linux Python 3.11/3.12 CI passes. Installed-wheel README and 10-diagnostic synthetic CLI execution pass.
• The31 core source ASTs match evaluated0.16 after removing docstrings/version; this release makes no new independent measured-data accuracy claim. Reused heat-pump gains remain confounded by a0.3K condensing-temperature session difference; engine-filter faster candidates were rejected for false alarms.
• Original code/docs: MIT. Raw datasets, papers, upstream source and trained corpus profiles are excluded and retain separate terms.

Zenodo software archive: https://doi.org/10.5281/zenodo.23992253

The attached wheel and sdist are the exact audited Zenodo files. PyPI publication is handled separately by the project-scoped trusted-publisher workflow. Alpha research software; alarms do not establish root causes or field safety qualification.