ci-monitoring-simulation 2.2.2

Calibrated simulation and detectors for MES-embedded carbon-intensity monitoring of energy anomalies in machining-style processes.

Detection limits of MES-embedded carbon-intensity monitoring

Simulation code, raw sweep data, and figure-generation scripts for the paper:

Yanytska, L. (2026). Detection limits of MES-embedded carbon-intensity monitoring for energy anomalies: a calibrated simulation study in machining-style processes. Manuscript in preparation.

This repository reproduces every figure in the paper from the 4,056 raw simulation runs released in data/.

Companion method (Paper 2): the paper2_anchored_detector/ folder contains the proposed event-anchored + residual-CUSUM detector that closes the adaptive-baseline inertia blind spot characterised in this study, together with its full evaluation — the operating-point calibration, the detector ablation, generalisation across severity, and validation on real Brillinger spindle-power traces. See that folder's README.

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Install

The simulation framework (Modules 1–5) and the proposed detector are available as an installable package, cimonitoring:

pip install ci-monitoring-simulation
# or, the latest development version straight from GitHub:
pip install git+https://github.com/lesiayanytska78/ci-monitoring-simulation.git
# or, from a local clone:
pip install .

import cimonitoring as ci
sub = ci.simulate_work_center(ci.Config(seed=1))
sub = ci.inject_anomalies(sub, ci.AnomalyConfig([
    ci.AnomalySpec(onset_hour=10, duration_minutes=240, magnitude_kw=2.0,
                   onset_profile="ramp", onset_ramp_seconds=3600,
                   affects="spindle", label="slow ramp")]))
obs = ci.run_monitoring_anchored(
    sub, ci.AnchoredMonitorConfig(detector="anchored_cusum"),
    ci.CarbonConfig().static_emission_factor_kg_per_kwh)

The figure-reproduction below reads the released CSVs and does not require installation.

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Quick reproduction

# 1. Clone and enter the repository
git clone https://github.com/lesiayanytska78/ci-monitoring-simulation.git
cd ci-monitoring-simulation

# 2. Install dependencies (Python 3.9 or newer)
pip install -r requirements.txt

# 3. Regenerate all seven figures from the released CSVs
python plot_paper_figs.py

Expected output: seven PNGs written to figures/. Wall time on a single CPU core: under one minute. plot_paper_figs.py reads only the CSVs in data/ and writes only to figures/ — it does not require the simulation modules.

A Makefile provides shortcuts: make install, make figures, make test.

Tests

A pytest suite in tests/ exercises the full pipeline end to end — the energy substrate, carbon layer, anomaly model, the deployed detector, and the proposed event-anchored + CUSUM detector — and includes a regression test for the paper's headline result (the fixed-reference detector fires on a slow ramp the deployed detector misses) and an integrity check that the released CSVs load and the shared simulation modules do not drift between simulation/ and paper2_anchored_detector/. Run with:

pip install -r requirements.txt pytest
pytest -q

Continuous integration runs the suite on Python 3.9, 3.11, and 3.12 on every push (see the badge above).

Re-running the simulations from scratch (optional)

The five simulation modules live in simulation/. The sweep functions that produced data/ are in simulation/sensitivity.py; the default sweep set (Sweeps 1–6) runs from its __main__ block, and the ramp-time, boundary, and ramp-transition sweeps (Sweeps 7–9) are provided as functions in the same module. Output paths are set at the bottom of the script — adjust them to your environment before running. The released CSVs in data/ are the authoritative results behind every reported number.

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Repository layout

.
├── README.md                                  this file
├── LICENSE                                    MIT (code)
├── requirements.txt                           numpy, pandas, matplotlib
├── CITATION.cff                               machine-readable citation
│
├── plot_paper_figs.py                         generates Figures 1–7 from data/
│
├── simulation/                                Module 1–5 simulation harness
│   ├── energy_substrate.py                    Module 1 — energy substrate (Gutowski decomposition)
│   ├── carbon_layer.py                        Module 2 — carbon layer (rolling CI per piece)
│   ├── anomaly_model.py                       Module 3 — four-archetype anomaly model
│   ├── monitoring.py                          Module 4 — MES/SCADA sensor + rule-based detector
│   └── sensitivity.py                         Module 5 — sensitivity-sweep harness
│
├── data/                                      raw per-run results (4,056 rows total)
│   ├── sweep1_latency.csv                     severity × duration (300 runs)
│   ├── sweep2_sampling.csv                    severity × meter cadence (240)
│   ├── sweep3_roc.csv                         relative-threshold tightness (240)
│   ├── sweep4_archetypes.csv                  severity × four archetypes (240)
│   ├── sweep5_threshold_types.csv             absolute / relative / statistical (496)
│   ├── sweep6_attribution.csv                 severity × affected channel (140)
│   ├── sweep7_ramp_time.csv                   ramp time × severity, coarse (350)
│   ├── sweep8_boundary.csv                    50-seed boundary, 1.0–2.0 kW (250)
│   └── sweep9_ramp_transition_200seed.csv     200-seed fine transition (1,800)
│
├── figures/                                   the seven paper figures (PNG)
│
└── docs/
    └── parameter_provenance.md                full [ANCHORED]/[LITERATURE]/[ASSUMPTION] table

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How paper claims map to data

Every figure and headline number is reproducible from one CSV in data/.

Paper claim	Section	CSV
Detection rate vs severity, three durations	§4.1, Fig. 1	sweep1_latency.csv + sweep8_boundary.csv
80%-detection threshold ≈ 47% of baseline (1.6 kW)	§4.1	sweep8_boundary.csv
Detection rate at four meter cadences	§4.2, Fig. 2	sweep2_sampling.csv
Threshold-tightness ROC	§4.3, Fig. 3	sweep3_roc.csv
Four-archetype comparison	§4.4, Fig. 4	sweep4_archetypes.csv
Threshold-family ROC (abs / rel / stat)	§4.5, Fig. 5	sweep5_threshold_types.csv
Attribution accuracy by channel	§4.6, Fig. 6	sweep6_attribution.csv
Inertia-trade-off sigmoid (200 seeds)	§4.7, Fig. 7	sweep9_ramp_transition_200seed.csv
Coarse multi-severity inertia behaviour	§4.7	sweep7_ramp_time.csv

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Parameter provenance

Every numeric parameter in the simulation carries one of three provenance tags, documented in docs/parameter_provenance.md:

• [ANCHORED] — fitted to a real measurement in the Brillinger et al. (2025) open CNC dataset.
• [LITERATURE] — taken from published sources cited in the paper.
• [ASSUMPTION] — engineering estimate, exercised in the sensitivity analysis.

The two [ANCHORED] values — the 0.80–0.99 kW no-load spindle range and the regenerative-braking behaviour — were extracted by the author from the raw Brillinger dataset; the extraction procedure is described in the paper's Methods (§3.2). The Brillinger dataset itself is not redistributed here: it is available from Mendeley Data (DOI 10.17632/gtvvwmz7r7.2, CC BY-NC) and must be downloaded separately to re-derive the anchored values.

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Citation

If you use this code or data, please cite the paper and the archived repository:

@article{yanytska2026detection,
  title   = {Detection limits of MES-embedded carbon-intensity monitoring for energy anomalies:
             a calibrated simulation study in machining-style processes},
  author  = {Yanytska, Lesia},
  year    = {2026},
  note    = {Manuscript in preparation}
}

@software{yanytska2026code,
  author    = {Yanytska, Lesia},
  title     = {Detection limits of MES-embedded carbon-intensity monitoring: simulation code and data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {v1.0.0},
  doi       = {10.5281/zenodo.20634187},
  url       = {https://doi.org/10.5281/zenodo.20634187}
}

This release is permanently archived on Zenodo: DOI 10.5281/zenodo.20634187. A machine-readable CITATION.cff is included at the repository root.

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Licence

• Code (simulation/, plot_paper_figs.py): MIT — see LICENSE.
• Data (data/): CC BY 4.0 — free to reuse with attribution.
• The upstream Brillinger et al. (2025) dataset, from which the anchored spindle values were derived, is CC BY-NC; commercial reuse of those specific anchored values must comply with the upstream licence.

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Contact

Lesia Yanytska — [e-mail]

Issues and pull requests are welcome.