README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - vibration
  - fft
  - condition-monitoring
  - predictive-maintenance
  - sensor-data
  - oil-and-gas
  - rotating-equipment
  - iso-10816
  - iso-20816
  - iso-17359
  - iso-13373
  - iso-14224
  - iso-4406
  - api-rp-670
  - api-rp-580
  - iso-18436
  - signal-processing
  - 3-axis-vibration
  - harmonic-analysis
pretty_name: "OIL-040 — Synthetic Vibration & Sensor Dataset (Sample)"
size_categories:
  - 100K<n<1M
---

# OIL-040 — Synthetic Vibration & Sensor Dataset (Sample)

A schema-identical preview of **OIL-040**, the XpertSystems.ai synthetic
vibration-and-sensor dataset for oil & gas rotating equipment condition
monitoring. The full product covers ~15,000 assets across a 365-day horizon
with 96-bin FFT spectra. This sample is a custom HF preview (80 assets × 30
days × 4 samples/day, 32-bin FFT) covering all 12 product tables, optimized
for ISO 10816 / ISO 13373 / API RP 670 vibration analytics work.

> **Built by** XpertSystems.ai — Synthetic Data Platform
> **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai)
> **License** CC-BY-NC-4.0 (sample); commercial license available for the full product.

---

## OIL-040 vs OIL-038 vs OIL-039 — what's different

OIL-038, OIL-039, and OIL-040 are three complementary upstream-asset PdM
products covering different research workloads:

| Dimension | **OIL-040** (this dataset) | OIL-039 (PHM/RUL) | OIL-038 (failure events) |
|---|---|---|---|
| Primary focus | **3-axis vibration + FFT signal processing** | Per-timestamp RUL prognostics | Failure-event analytics + reliability KPIs |
| 3-axis vibration (X/Y/Z) | Yes (horizontal-dominant) | RMS only | RMS only |
| FFT spectra | 32-bin (sample), 96-bin (full) | 4-band (sample), 7-band (full) | None |
| ISO 10816 calibration | Yes — median RMS in normal/alert band | Different unit normalization | ISO 10816 absolute units |
| Sensor pack tiers | 4-tier (basic / standard / advanced / edge_ai) | None | None |
| Pre-built labels | anomaly + fault_class + severity + rare_event + 30d target | RUL hours/days + 7d/30d failure prob | 30d/90d failure probability |
| Best for | Signal-processing ML, FFT-based fault classification, ISO 10816 severity work | RUL regression, prognostics benchmarks | Reliability KPI fitting, MTBF/MTTR |

Buy or download **all three** for complete PdM coverage. They share the
upstream-asset, ISO 14224 / API RP 580 / API RP 670 calibration heritage.

---

## What's inside

12 CSV tables covering 3-axis vibration + 5 supporting telemetry modalities
+ workorders + failures + per-record health/RUL/labels + 32-bin FFT spectra.

| Table | Rows (sample) | What it represents |
|---|---:|---|
| `equipment_master.csv` | 80 | 12-type asset master with sensor pack, criticality, primary fault mode |
| `vibration_timeseries.csv` | 9,600 | 3-axis (X/Y/Z) RMS mm/s + crest factor + kurtosis per timestamp |
| `temperature_telemetry.csv` | 9,600 | Thermocouple temperature, gradient, overheat flag |
| `pressure_telemetry.csv` | 9,600 | Pressure psi, delta, spike flag |
| `acoustic_signals.csv` | 9,600 | Acoustic dB, ultrasonic energy, anomaly score |
| `lubrication_analysis.csv` | 9,600 | Viscosity index, contamination, water ppm, lubrication risk |
| `maintenance_workorders.csv` | ~80 | 6-type maintenance work orders with priority, downtime, notes quality |
| `failure_events.csv` | ~5 | Per-failure mode + severity + repair cost + production loss |
| `health_scores.csv` | 9,600 | Per-record health index, degradation score, condition state |
| `remaining_useful_life.csv` | 9,600 | Per-record RUL days, 30d failure probability, maintenance recommendation |
| `vibration_labels.csv` | 9,600 | Anomaly + fault class + severity + rare event flag + 30d target |
| `fft_spectra.csv` | ~307,000 | 32-bin FFT (5–1000 Hz) with rotational harmonics + fault-defect frequency energy |

Total: ~388,000 rows, ~40 MB. The full OIL-040 product is ~80 million rows
with 96-bin FFT decomposition.

---

## Calibration sources

Every distribution and ratio is anchored to **named public references**. The
validation scorecard (see below) re-scores observed vs. target for 10
industry-anchored metrics, every one citing its source. Highlights:

- **ISO 10816 / ISO 20816** Mechanical vibration evaluation — vibration
  severity bands (normal / alert / alarm / shutdown) for Class II rotating
  equipment.
- **ISO 17359** Condition monitoring of machines — crest factor severity bands.
- **ISO 13373-1 / ISO 13373-2** Vibration condition monitoring — kurtosis,
  spectrum analysis.
- **ISO 18436-2** Vibration analyst certification conventions — horizontal /
  vertical / axial axis amplitude relationships.
- **API RP 670** Machinery Protection Systems — FFT decomposition standards
  and rotational harmonic boost relationships (1x, 2x, 3x, 4x rpm).
- **API RP 580** Risk-Based Inspection — criticality-tier distributions.
- **ISO 14224:2016** Reliability and Maintenance Data — equipment taxonomy
  and maintenance work classification.
- **ISO 4406:2021** Hydraulic fluid power: cleanliness code thresholds.
- **ARC Advisory PdM Maturity Survey** + **ISA-95 / OSDU** — advanced sensor
  pack deployment baselines.
- **Noria Lubrication Practices** — water content thresholds.

---

## Validation scorecard

The wrapper ships a 10-metric scorecard (`validation_scorecard.json`) that
re-scores the dataset on every generation. Default seed 42 result:

| ID | Metric | Target | Observed | Source |
|---|---|---|---:|---|
| M01 | Vibration RMS median (mm/s) | 1.5–4.5 | **2.38** | ISO 10816 / ISO 20816 |
| M02 | Crest factor (mean) | 2–6 | **3.30** | ISO 17359 / ISO 13373-1 |
| M03 | Kurtosis (mean) | 2–6 | **4.35** | ISO 13373-1 |
| M04 | Lubrication water ppm (ceiling) | ≤ 250 | **112** | ISO 4406 / Noria |
| M05 | Horizontal-axis dominance (X/RMS) | 0.85–1.15 | **1.006** | ISO 18436-2 / API RP 670 |
| M06 | Criticality tier ≥ 3 share | 0.60–0.80 | **0.775** | API RP 580 RBI |
| M07 | Maintenance-type coverage (floor) | ≥ 6 | **6** | ISO 14224:2016 |
| M08 | FFT bin coverage (floor) | ≥ 32 | **32** | API RP 670 / ISO 13373-2 |
| M09 | Asset-type taxonomy (floor) | ≥ 12 | **12** | ISO 14224 |
| M10 | Advanced sensor pack share (floor) | ≥ 0.25 | **0.388** | ARC Advisory / OSDU |

**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**

---

## Suggested use cases

- **FFT-based fault classification** — 32-bin FFT spectra include classic
  rotational harmonic peaks (1x, 2x, 3x, 4x rpm) and fault-specific defect
  frequencies (6.3x rpm bearing tone, 14x rpm gear-mesh tone, 420 Hz
  cavitation/surge band). Train CNN-on-spectrogram or 1D-conv classifiers.
- **ISO 10816 vibration severity classification** — per-record RMS in mm/s
  is calibrated to the standard's Class II band, enabling direct alert /
  alarm / shutdown classifier training without unit conversion.
- **3-axis anomaly detection** — X/Y/Z axis decomposition with the classic
  horizontal-dominant ratio (X > Y > Z) makes this dataset suitable for
  geometry-aware anomaly models and axis-mixing experiments.
- **Crest factor + kurtosis impulsive fault detection** — both metrics are
  ISO-calibrated and per-record, enabling bearing-fault and gear-mesh
  detection benchmarking against ISO 13373-1 thresholds.
- **Multi-modal sensor fusion** — 6 telemetry modalities (vibration +
  temperature + pressure + acoustic + lubrication + health) are
  per-`record_id`-aligned for tight multi-modal experiments.
- **Sensor-pack tier ROI** — `sensor_pack` field (basic / standard /
  advanced / edge_ai) on each asset enables ROI quantification of advanced
  PdM hardware against detection rate and failure cost.
- **Rare-event detection** — `rare_event_flag` in `vibration_labels.csv`
  flags spike events (vibration ×1.7–3.8 multipliers) calibrated to
  fault-mode-dependent rates; useful for imbalanced-class ML training.
- **RUL regression** — `rul_days` is per-record and calibrated against
  health index + failure probability; alternative to OIL-039's RUL bucket
  formulation.

---

## Loading

```python
from datasets import load_dataset

master = load_dataset(
    "xpertsystems/oil040-sample",
    data_files="equipment_master.csv",
    split="train",
)
vibration = load_dataset(
    "xpertsystems/oil040-sample",
    data_files="vibration_timeseries.csv",
    split="train",
)
fft = load_dataset(
    "xpertsystems/oil040-sample",
    data_files="fft_spectra.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil040-sample",
    data_files="vibration_labels.csv",
    split="train",
)
```

Or with pandas directly:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/oil040-sample",
    filename="fft_spectra.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)
```

All 12 tables join on:

- `equipment_id` → master ↔ all telemetry ↔ FFT ↔ workorders ↔ failures ↔ labels
- `record_id` → tight per-timestamp join across all 6 telemetry modalities + labels + health + RUL
- `timestamp` → temporal join across asset/record streams

The shared `record_id` makes multi-modal fusion experiments straightforward:
join on `record_id` to get every modality at the same instant for the same
asset.

---

## Schema highlights

**`equipment_master.csv`** — `equipment_id`, `facility_id`, `facility_type`
(7-class: upstream / offshore / midstream / refinery / lng / petrochemical
/ tank_farm), `asset_type` (12-class: centrifugal_pump /
reciprocating_compressor / gas_turbine / steam_turbine / electric_motor /
gearbox / pipeline_booster / drilling_mud_pump / lng_refrigeration_compressor
/ refinery_process_pump / blower / offshore_lift_motor), `manufacturer`
(6-class), `model`, `install_date`, `age_years`, `criticality` ∈ {1, 2, 3,
4, 5}, `rated_rpm`, `bearing_count`, `sensor_pack` ∈ {basic, standard,
advanced, edge_ai}, plus 4 baseline reference values per asset and
`primary_fault_mode` (12-class).

**`vibration_timeseries.csv`** — `record_id`, `equipment_id`, `timestamp`,
`rpm`, `axis_x_mm_s`, `axis_y_mm_s`, `axis_z_mm_s` (horizontal-dominant
ISO 18436 convention), `vibration_rms_mm_s` (ISO 10816 unit),
`crest_factor` (ISO 17359), `kurtosis` (ISO 13373-1).

**`fft_spectra.csv`** — Per-record × 32-bin FFT decomposition (5–1000 Hz
linear), each row has `frequency_hz`, `amplitude`, `phase_angle`.
Amplitude includes base lognormal noise + rotational harmonic boosts at
{1x, 2x, 3x, 4x} rpm/60 + fault-defect frequency boosts: bearing tone at
6.3x rpm (bearing_wear, lubrication_loss), gear-mesh tone at 14x rpm
(gear_mesh_wear), and 420 Hz cavitation/surge band.

**`vibration_labels.csv`** — `record_id`, `equipment_id`, `timestamp`,
`anomaly_label` (binary), `fault_class` (12-class), `severity_level`
(5-class: normal / low / medium / high / critical), `rare_event_flag`,
`target_failure_30d`.

**`maintenance_workorders.csv`** — `maintenance_type` (6-class:
inspection / lubrication / bearing_replacement / alignment /
sensor_calibration / overhaul), `priority` (4-class: low / medium / high /
emergency), `downtime_hours`, `technician_notes_quality` ∈ {complete,
partial, missing}.

---

## Calibration notes & limitations

In the spirit of honest synthetic data, a few things buyers of the sample
should know:

1. **Custom HF preview sizing.** The default generator `sample` mode
   produces ~326 MB (250 assets × 30 days × 24 samples/day × 32 FFT bins =
   ~1.9M FFT rows). The HF preview is reduced to **80 assets × 30 days ×
   4 samples/day** to stay under 50 MB while preserving every table,
   schema, and the scorecard's industry-anchored calibration validity.
   For higher time-density studies, override sizing with the underlying
   generator's `--samples-per-day` and `--n-assets` flags, or use the
   commercial full product.

2. **Anomaly label rate is ~99%.** In `vibration_labels.csv`, the
   `anomaly_label` (binary) is set to 1 whenever `condition_state != normal`,
   and the severity-label thresholds combined with the fail_prob distribution
   put ~99% of records in low/medium/high/critical bands. This is **a
   labeling-convention artifact, not a positive-class density claim**.
   For binary anomaly classification work, **use `severity_level` directly**
   (5-class) or **threshold `failure_probability_30d > 0.70`** to recover
   a balanced positive class (~5% of records). The full product ships a
   threshold-tuned binary label variant.

3. **Overheat flag is 0 in the sample.** `temperature_telemetry.csv`'s
   `overheat_flag` triggers above 115°C, but at the 30-day window most assets
   don't reach that threshold. For overheat-detection studies, lower the
   threshold to 95°C in your downstream pipeline, or use the full product's
   365-day window which exposes more thermal-overload events.

4. **Only 9 of 12 fault modes appear at sample scale.** With 80 assets and
   55% `normal` primary fault, only 9 of the 12 fault modes are represented
   in any given seed's sample. For full taxonomy coverage, **use multiple
   seeds and concatenate**, or use the full product (15,000 assets sees all
   12 fault modes with statistically representative density).

5. **Small failure-event count.** With 80 assets × 30 days, the sample
   produces ~5–10 failure events depending on seed. Failure-severity
   distributions are not reliably estimable at this scale (small-sample
   variance). **For severity-pyramid analytics**, use OIL-038 (rich
   failure-event tables) or OIL-039 (sigmoid-calibrated 7d/30d
   probabilities).

6. **FFT amplitude scale.** FFT amplitudes are in normalized units derived
   from `vibration_rms_mm_s` × harmonic-boost factors. They are NOT
   absolute G or m/s². For absolute-unit FFT work, calibrate against the
   `vibration_rms_mm_s` baseline.

7. **Deterministic seeding.** All 12 tables are deterministic on `--seed`.
   Catalog default is seed 42. Seed sweep verifies Grade A+ across
   {42, 7, 123, 2024, 99, 1}.

---

## Commercial / full product

The full **OIL-040** product covers ~15,000 assets × 365 days × 24
samples/day × 96-bin FFT decomposition (~80 million rows total), with
threshold-tuned binary anomaly labels, full 12-fault-mode coverage at
production scale, and longer-horizon thermal-overload events. Available
under commercial license — contact
[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).

XpertSystems.ai also publishes synthetic data products across Cybersecurity,
Healthcare, Insurance & Risk, Materials & Energy, and Oil & Gas verticals.
Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).