README.md

---
pretty_name: "DRMHB - Dynamic Rotating Machinery Health Benchmark"
license: cc-by-4.0
tags:
  - synthetic-data
  - vibration
  - condition-monitoring
  - predictive-maintenance
  - anomaly-detection
  - industrial
  - rotating-machinery
  - time-series
  - multivariate-time-series
  - anndata
  - vibframe
  - vibsynth
size_categories:
  - 10K<n<100K
---

# DRMHB - Dynamic Rotating Machinery Health Benchmark

DRMHB is a synthetic benchmark for machine-learning research in rotating
machinery condition monitoring. It represents 180 calendar days for a fleet of
12 nominally equivalent variable-speed motor-pump units, combining:

- multiple healthy operating regimes;
- explicit startup, shutdown and internal regime transitions;
- machine-to-machine individuality;
- rare healthy out-of-distribution (OOD) operation;
- realistic non-fault disturbances and sensor ageing;
- progressive single and interacting mechanical faults;
- synchronized triaxial spectra, waveforms and tachometer edges;
- a causal calibration/holdout/future evaluation protocol.

All measurements are synthetic. DRMHB is not field data and is not a substitute
for validation on real machinery.

The ZIPs under `data/` are the **2.6.0 compact campaign** and its separate
512 paired controls. The conversion examples use
[`vibframe-anndata==0.3.0`](https://413hq.github.io/vibframe-anndata-docs/0.3.0/)
with complete evaluation preservation. The complete EDA, all report figures and
baseline tables were rerun on the current campaign and checked on 1 October 2026.
Measured identities and checks are in [the dataset audit](docs/provenance/current_dataset.json).

**Start here:** [dataset report](docs/DRMHB.pdf), [conversion examples](examples/README.md)
and [complete ground-truth guide](docs/GROUND_TRUTH.md).

## The challenge

The task is not merely to separate healthy and faulty rows. A useful model must
learn fleet normality while avoiding false alarms caused by legitimate process
changes. In particular:

- R2 and R4 have similar rotational speed but different hydraulic load;
- forward and reverse transitions follow different paths because state has lag
  and memory;
- adaptive acquisition heavily oversamples transitions relative to elapsed
  time;
- equivalent machines retain stable individual vibration fingerprints;
- healthy OOD conditions are statistically unusual but not damaged;
- observable vibration need not increase monotonically with latent severity;
- some machines contain two coupled degradation mechanisms.

## Industrial scenario

The simulated plant contains 12 variable-frequency-drive motor-pump sets,
identified as `M01` through `M12`. Vibration is measured at the pump drive-end
bearing housing in three directions:

- `pump_DE_H`: radial horizontal;
- `pump_DE_V`: radial vertical;
- `pump_DE_A`: axial.

Five active regimes are used:

| ID | Name | Nominal meaning |
|---|---|---|
| `R1` | ECO | Reduced speed and demand |
| `R2` | NOMINAL | Standard production duty |
| `R3` | PEAK | Temporary high-demand duty |
| `R4` | THROTTLED | R2-like speed with different hydraulic load |
| `R5` | RECIRCULATION | Low-flow recirculating operation |

`OFF` is also an explicit state. Machines operate Monday-Thursday from 08:00
to 22:30 UTC and Friday from 08:00 to 18:00 UTC; weekends remain stopped.

## Time horizon, splits and adaptive acquisition

The fixture covers:

```text
2026-01-01 00:00:00 UTC -> 2026-06-29 22:30:00 UTC
```

The 46,907 observations are divided as follows:

| Split | Rows | Intended use |
|---|---:|---|
| `calibration` | 8,179 | Learn healthy normality |
| `normal_holdout` | 3,723 | Calibrate thresholds and false-positive behaviour |
| `future_stream` | 35,005 | Causal evaluation with faults, transitions and OOD |

Acquisition is deliberately non-uniform:

| Context | Policy | Realized rows |
|---|---|---:|
| Startup transition | Reference: every 5 minutes | 8,729 |
| Shutdown transition | Reference: every 5 minutes | 8,784 |
| Internal regime transition | Reference: every 10-15 minutes | 14,562 |
| Stable running | Reference: every 2-4 hours, plus reallocated captures | 11,424 |
| OFF | Every 8 hours | 3,408 |

The compact profile reallocates 8,000 reference transition observations to steady
operation while protecting endpoints, central progress and fast motor edges.
There are 32,075 transition observations. Realized steady spacing has a median
of 55 minutes; reference cadences are not strict spacing rules after reallocation. Observation count is therefore not proportional to elapsed time.
Temporal plots and persistence metrics must use timestamps rather than row index.

## Signal acquisition and storage

Every observation begins from a synchronized triaxial acceleration realization
of 1.6 seconds at 51.2 kHz. Both stored spectral products are calculated from
that complete source realization:

- `VEL_1K`: velocity spectrum from 0 to 1,000 Hz;
- `ACC_10K`: acceleration spectrum from 0 to 10,000 Hz.

`VEL_1K` stores native peak lines at 0.625 Hz spacing. `ACC_10K` stores the
root-sum-square of native peak lines in 6.25 Hz bands, rather than a maximum line
amplitude. Under its periodic-Hann convention, RMS is `sqrt(sum(data**2)/3)`;
apply the correction once. Each spectrum has 1,601 bins. With three directions and two processing modes,
the dataset contains 281,442 spectral signals.

The persisted waveform branch is anti-alias filtered and decimated to 25.6 kHz.
Its length is time-based rather than revolution-normalized:

| Stored window | Samples/channel | Role |
|---|---:|---|
| 0.25 s | 6,400 | Ordinary OFF observation |
| 0.40 s | 10,240 | Ordinary active or transition observation |
| 0.75 s | 19,200 | Localized event |
| 1.60 s | 40,960 | Stratified anchor |

The current fixture contains 3,240 short OFF windows, 41,125 ordinary windows,
198 event windows and 2,344 full-length anchors. Each observation has H/V/A
waveforms, giving 140,721 waveform signals and 1,625,003,520 persisted samples.
The separate paired controls use 0.50 s / 12,800-sample windows.

Waveforms are quantized to an int16-equivalent grid and reconstructed as
physical-unit float32 values to remain compatible with VibFrame 0.2.

## Legitimate novelty and faults

Healthy active observations may contain low-probability disturbances such as
RPM drift, load transients, electrical interference, sensor-noise bursts and
transient impacts. The current fixture contains 1,756 such rows. Three healthy
machines each receive eight operational-OOD episodes, totaling 24 episodes and
1,057 observations. The report lists every configured UTC interval, RPM and valve command.

Fault distribution:

| Machine | Fault 1 | Fault 2 |
|---|---|---|
| `M01` | Imbalance | - |
| `M02` | Healthy | - |
| `M03` | Angular misalignment | - |
| `M04` | Bearing outer-race | - |
| `M05` | Imbalance | Bearing outer-race |
| `M06` | Mechanical looseness | - |
| `M07` | Healthy | - |
| `M08` | Angular misalignment | Mechanical looseness |
| `M09` | Bearing inner-race | - |
| `M10` | Bearing inner-race | Resonance |
| `M11` | Healthy | - |
| `M12` | Resonance | - |

No fault begins before day 60. Fault severity is derived from mechanism-specific
physical state, and observable response remains dependent on speed, load,
thermal history and structural transfer.

## Ground truth and leakage prevention

The source VibFrame keeps evaluation information in separate sidecars. The
0.3.0 workflow preserves both supported observation-aligned views and the
original files, without automatically exposing targets to a model:

| AnnData location | Information |
|---|---|
| `obsm["ground_truth"]` | Snapshot truth, aligned with `obs_names` |
| `obsm["waveform_ground_truth"]` | Per-waveform annotations with explicit raw-channel bindings |
| `uns["vibframe_evaluation"]` | Byte-exact original annotation files and source/path/size/SHA-256 manifest |
| `uns["vibframe_anndata"]` | Conversion, raw-channel and feature provenance |
| `obsm["raw_*_capture_t"]` and `*_capture_t_known` | Actual capture times and explicit/fallback flags |

This includes construction labels, crop offsets and durations, quantization and
hashes, machine parameters, scenario configuration and DiagGT documents/tables.
All regular files under `evaluation/`, `ground-truth/` and `ground_truth/` are
preserved, plus root JSON/YAML and machine JSON context. Unknown annotation
fields are retained; the original Parquet schema can be recovered.

Snapshot joins use **source, machine and integer UTC-microsecond `snap_t`**, not
row order. Per-waveform joins also identify the raw channel. Capture `t` can be
later than `snap_t` because of cropping; it is not a different snapshot.

The current original snapshot-truth tables have 49 columns; the aligned view
also contains conversion identity fields. Code must inspect actual fields
instead of hard-coding a fixed schema. Do not use `split`, `fault_*`, severity,
physical states, `ood_operational`, nuisance labels or DiagGT as unsupervised
model inputs. Operational-state knowledge must be an explicitly declared
experimental condition. The package never adds these fields to `X` implicitly.

**Preservation is not automatic labelling:** DiagGT intervals, consolidated
observations and findings retain their original semantics. No nearest-time
join is invented, and absence of a diagnosis does not mean healthy. The original
archive remains dataset-wide when AnnData observations are sliced.

## Distribution and local AnnData workflow

The longitudinal signal artifact is `data/DRMHB-compact.vibframe.zip`.
Paired controls are in `data/DRMHB-compact-controls.vibframe.zip`; import them
to a separate H5AD. H5ADs are local derivatives.

```bash
python -m pip install -r examples/requirements.txt

python examples/01_create_featureless_anndata.py \
    data/DRMHB-compact.vibframe.zip derived/DRMHB-compact.v0.3.0.raw.h5ad

python examples/02_inspect_anndata.py \
    derived/DRMHB-compact.v0.3.0.raw.h5ad --waveform-details

python examples/03_add_features.py \
    derived/DRMHB-compact.v0.3.0.raw.h5ad derived/DRMHB-compact.v0.3.0.features.h5ad

python examples/05_read_evaluation.py derived/DRMHB-compact.v0.3.0.features.h5ad
```

Example 01 explicitly enables `ground_truth.scope="all"`, stores numerical
values as `float32`, and ingests in raw-data blocks of 8 MiB. The featureless base
has `X.shape == (46907, 0)` for the current longitudinal ZIP. The feature requests materialized 468 machine/source-scoped columns in the current
EDA, with structural missing values for other machines. They are not 468 dense
fleet-wide descriptors; the notebook explains the logical-column alignment.

Example 03 calculates features only from the raw H5AD and checks that original
annotations and raw coverage are preserved. Example 02 reads HDF5 metadata and
public annotation APIs selectively; it does not assume AnnData backed mode
keeps `obsm` or the evaluation archive out of RAM.

For an existing 0.2.x H5AD, preserve the original source identities and run:

```bash
python examples/04_add_complete_ground_truth.py \
    derived/DRMHB-compact.v0.3.0.no-truth.h5ad data/DRMHB-compact.vibframe.zip \
    derived/DRMHB-compact.v0.3.0.enriched.h5ad
```

`no-truth.h5ad` represents an existing H5AD created from the same longitudinal
ZIP with ground-truth import disabled; adjust the path to your actual file.
The command adds annotations without recalculating `X`, `var` or raw samples.
A transactional temporary copy needs roughly one additional H5AD of free disk.
Legacy capture-time companion arrays are not retroactively recovered by this
operation; new raw imports populate them. Crop information is still retained
in waveform truth.

### Storage and memory

The longitudinal ZIP is **7,635,324,956 bytes (7.111 GiB)**. The measured
0.3.0 featureless H5AD is **8,771,810,437 bytes (8.169 GiB)** and the feature H5AD
is **8,874,970,373 bytes (8.265 GiB)**. Both retain 97 original annotation files,
totaling 26,874,052 bytes. The audit verifies all original annotation bytes
against the ZIP, snapshot identities, waveform lengths and identical raw buffers
in both H5ADs. These sizes include full annotations and capture-time matrices.

The default 512 MiB sidecar budget covers original encoded annotation files,
not decoded tables or total process RAM. Inspect exact archived sizes with
`list_evaluation_files()`. Raw-array compression remains `none`, as in the
existing examples; no compression benchmark is rerun. Ground-truth conversion
does not modify the VibFrame ZIP.

## AnnData layout

```text
obs   : snapshot identity, snap_t, machine, source, speed and availability
X     : n_snapshots × n_materialized_features (initially zero columns)
var   : feature descriptors, units, channel identity and provenance
obsm  : ragged raw spectra/waveforms + lengths/positions/speed
        tachometer edges + capture_t/known companions
        ground_truth + waveform_ground_truth (evaluation only)
uns   : vibframe_anndata (package/raw/feature provenance)
        vibframe_evaluation (original sidecar bytes and manifest)
```

See [examples/README.md](examples/README.md) for precise commands and
[docs/GROUND_TRUTH.md](docs/GROUND_TRUTH.md) for target selection, source identity,
original DiagGT access and missing-data semantics.

## Recommended evaluation protocol

1. Fit preprocessing and models only on `calibration`.
2. Use `normal_holdout` to select thresholds.
3. Evaluate once on `future_stream`.
4. Treat healthy transitions and healthy OOD as negative examples unless the
   research question explicitly targets novelty.
5. Use timestamps for detection persistence and delay.
6. Report per-machine and per-fault results alongside aggregate metrics.

Recommended metrics include AUROC, AUPRC, F1, ordinary healthy FPR, transition
FPR, OOD FPR, wall-clock detection delay and the fraction of faulty machines
with a confirmed detection.

The reference EDA confirms an alarm after six continuously positive wall-clock
hours; an observation gap longer than 4.5 hours breaks the run.

## Compact reference baseline

The values below come from the complete current 2.6.0 EDA using converter 0.3.0.
Four deliberately small unsupervised baselines use 39 logical features, fitted
on calibration only. Thresholds are the 99th percentile of normal-holdout scores.

| Model | AUROC | AUPRC | F1 | Healthy FPR | Transition FPR | OOD FPR | Median delay |
|---|---:|---:|---:|---:|---:|---:|---:|
| Robust distance | 0.741 | 0.771 | 0.237 | 0.616% | 0.668% | **0.000%** | 38.44 d |
| PCA reconstruction | 0.735 | 0.768 | 0.329 | 1.017% | 1.142% | 3.784% | **13.48 d** |
| Isolation Forest | **0.815** | **0.843** | 0.485 | 1.371% | 1.675% | 5.866% | 53.25 d |
| One-Class SVM | 0.687 | 0.774 | **0.508** | 0.842% | 1.049% | 2.744% | 36.51 d |

These are sanity checks, not leaderboard targets. Median delay excludes
undetected machines: robust distance detects 2/9 faulty machines, PCA 3/9,
and Isolation Forest and One-Class SVM each 5/9. Report coverage with delay.
OOD false positives exceed ordinary healthy FPR for three models.

Healthy calibration requires 6, 9 and 13 PCA components for 80%, 90% and 95%
cumulative variance, respectively. The TwoNN intrinsic-dimension estimate is 9.17.

The 512 paired controls contain 256 damage interventions and 256 healthy
operating-state interventions (1,024 snapshots). Their timestamps are variant
identifiers, not a degradation trajectory. `fault_present` and
`diagnostic_eligible` (RPM >= 120) are separate truth fields; eligibility does
not guarantee detectability. No RUL or failure-time target is defined.

## Repository contents

```text
DRMHB/
|- data/DRMHB-compact.vibframe.zip          current longitudinal signal dataset
|- data/DRMHB-compact-controls.vibframe.zip separate 512-pair controls
|- derived/                      local H5ADs and EDA exports (not versioned)
|- examples/                     import, inspect, features, retrofit, evaluation readers
|- notebooks/DRMHB_EDA.ipynb       complete executed 0.3.0 EDA on the 2.6.0 campaign
|- latexdocs/DRMHB.tex            editable report source
|- latexdocs/figures/             promoted EDA images and editable TikZ diagrams
|- latexdocs/provenance/          figure/table and report input identities
|- docs/DRMHB.pdf                 compiled current dataset report
|- docs/provenance/               measured current dataset audit
`- tools/                        audit and report build
```

The [report](docs/DRMHB.pdf) explains the installation, schedules, acquisitions,
degradation, paired controls, evaluation and complete AnnData workflow.
The 37 figures and two baseline tables come from the complete current notebook
execution; their [manifest](latexdocs/provenance/eda_assets.json) records hashes
and notebook identity. The TikZ week was verified against the current full-horizon
process recipe.

Run the audit with the notebook's Python environment:

```bash
python tools/audit_dataset.py --deep
python tools/build_report.py --refresh-assets
```

`--deep` checks every raw numerical buffer in the base and feature H5ADs.
The build requires a complete sequential notebook execution and promotes all
report figures/tables together. Rebuild without promotion using
`python tools/build_report.py`; a LaTeX distribution with `latexmk` and TikZ is
required because the report has multiple input files and image assets.

The source VibFrame validation reports have `ok=true`, `strict_ok=false`:
the warning concerns the open node type `CentrifugalPump`. The consistency audit
does not re-synthesise signals or independently validate the physical model.

## Limitations

- The plant, signals and labels are entirely synthetic.
- The hydraulic and mechanical dynamics are reduced models, not CFD/FEM.
- The persisted 25.6 kHz waveform discards content above 12.8 kHz.
- Spectra contain magnitude, not absolute complex phase.
- Persisted waveform length depends on acquisition policy.
- Adaptive sampling biases count-based summaries unless elapsed time is used.
- Evaluation truth is cleaner and more complete than real maintenance labels.
- Performance on DRMHB is not evidence of production readiness.

Results should ideally be validated on independent field datasets.

## Citation

```bibtex
@dataset{drmhb_2026,
  title     = {DRMHB: Dynamic Rotating Machinery Health Benchmark},
  author    = {Gonzalez Zapico, Alejandro},
  year      = {2026},
  publisher = {Hugging Face},
  note      = {Synthetic rotating-machinery health-monitoring benchmark generated with VibSynth by TWave}
}
```

## License

DRMHB is released under the Creative Commons Attribution 4.0 International
license (`CC-BY-4.0`). See [LICENSE.md](LICENSE.md).

## Reporting results

Please state:

- the temporal split and calibration procedure;
- which raw modalities or derived features were used;
- whether RPM, regime or other operating metadata were supplied;
- how transitions and healthy OOD were scored;
- the wall-clock alarm persistence rule;
- per-machine detection coverage and delay;
- whether evaluation truth was used only after prediction.