README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- synthetic
- oil-and-gas
- equipment-performance
- predictive-maintenance
- condition-monitoring
- rul-prediction
- vibration-analysis
- iso-10816
- api-617
- xpertsystems
pretty_name: "OIL-021 — Synthetic Equipment Performance Dataset (Sample)"
size_categories:
- 100K<n<1M
---

# OIL-021 — Synthetic Equipment Performance Dataset (Sample)

**SKU:** `OIL021-SAMPLE` · **Vertical:** Oil & Gas / Cross-Stream Equipment Performance
**License:** CC-BY-NC-4.0 (sample) · **Schema version:** `oil021.v1`
**Sample version:** `1.0.0` · **Default seed:** `42`

A free, schema-identical preview of XpertSystems.ai's enterprise equipment
performance dataset for **predictive maintenance ML, vibration analysis,
condition monitoring, RUL (remaining useful life) prediction, and equipment
health scoring** across rotating and static equipment. The sample covers
**350 pieces of equipment** across **8 equipment
types**, **6 plants**, and **7 OEMs**, with
**179,149 rows** linked across **12 tables** via `equipment_id`.

**OIL-021 is the first cross-stream SKU in the catalog** — applicable to
upstream, midstream, and downstream operations (heat exchangers, compressors,
pumps, turbines, motors are universal across the value chain).

---

## What's in the box

| File | Rows | Cols | Description |
|---|---:|---:|---|
| `equipment_master.csv` | 350 | 9 | Equipment catalog: 8 types × 6 plants × 7 manufacturers × 4 criticality × age/design efficiency/rated power |
| `heat_exchanger_performance.csv` | 7,320 | 11 | HX time-series: inlet/outlet temp, cooling water, flow, duty, pressure drop, **fouling-coupled efficiency** per TEMA |
| `compressor_performance.csv` | 6,240 | 11 | Compressor time-series: suction/discharge P, pressure ratio, flow, **surge-margin-coupled efficiency** per API 617 |
| `pump_operations.csv` | 8,880 | 9 | Pump time-series: flow, head, **NPSH-cavitation-coupled efficiency** per API 610 + Hydraulic Institute |
| `vibration_signals.csv` | 84,000 | 8 | Per-equipment 10-min vibration: RMS + peak + axial + radial + **bearing-temperature-coupled** per ISO 10816 + API 670 |
| `lubrication_analysis.csv` | 5,600 | 9 | Biweekly oil samples: viscosity + water/iron/copper ppm + oxidation per ASTM D6595 + ISO 4406 |
| `thermal_monitoring.csv` | 21,000 | 7 | Per-equipment thermal: bearing + winding + casing temperatures + cooling efficiency |
| `maintenance_events.csv` | 1,015 | 7 | 4-class events (preventive/predictive/corrective/inspection per declared 42/24/17/17 weights) + parts replaced |
| `equipment_failures.csv` | 47 | 7 | 8-class failure modes + 6-class root causes + 4-class severity + repair cost per ISO 14224 |
| `alarm_trip_logs.csv` | 2,347 | 6 | 7-class alarm codes + trip flag + priority per ISA-18.2 |
| `efficiency_tracking.csv` | 42,000 | 6 | Daily efficiency with **runtime-driven degradation** (~0.0009/hr rate) |
| `equipment_labels.csv` | 350 | 6 | **FAILURE-COUPLED ML labels**: health score + 3-class failure risk + RUL days + maintenance priority |

Total: **179,149 rows** across 12 CSVs, ~13.3 MB on disk.

---

## Calibration: industry-anchored, honestly reported

Validation uses a **10-metric scorecard** with targets sourced exclusively to
**named industry standards**: **API 660** (Shell-and-Tube Heat Exchangers),
**TEMA RGP-T-2.4** (heat exchanger fouling), **API 617** (Axial and
Centrifugal Compressors), **API 670** (Machinery Protection Systems for
Vibration), **ISO 10816** (Mechanical Vibration Severity Zones), **API 610**
(Centrifugal Pumps for Petroleum), Hydraulic Institute Standards (pump
NPSH/cavitation), **ASTM D6595** (Wear Metals in Lubricants), ISO 4406
(Fluid Cleanliness Codes), API 580/581 (Risk-Based Inspection), **ISO 14224**
(Equipment Reliability and Maintenance Data Collection), DNV-RP-G101 (RBI),
IEC 60812 (FMEA), ISA-18.2 (Alarm Management), Kern correlation (HX fouling
pressure drop).

**Sample run** (seed `42`, equipment_count=350, periods=120):

| # | Metric | Observed | Target | Tolerance | Status | Source |
|---|---|---:|---:|---:|---|---|
| 1 | avg heat exchanger efficiency pct | 85.5153 | 85.0 | ±4.0 | ✓ PASS | API 660 (Shell-and-Tube Heat Exchangers) + TEMA RGP-T-2.4 — typical operating efficiency for clean-service refinery HX with moderate fouling (80-90% envelope; 85% target reflects mid-life operation) |
| 2 | avg compressor efficiency pct | 79.2021 | 79.0 | ±4.0 | ✓ PASS | API 617 (Axial and Centrifugal Compressors) — typical operating efficiency for moderate-pressure-ratio centrifugal compressors (75-83% for properly-staged machines; 79% reflects portfolio mean) |
| 3 | avg pump efficiency pct | 74.7788 | 75.0 | ±4.0 | ✓ PASS | API 610 (Centrifugal Pumps for Petroleum) + Hydraulic Institute Standards — typical pump efficiency at BEP (Best Efficiency Point) for refinery service (70-82% envelope; 75% reflects mixed centrifugal+reciprocating portfolio) |
| 4 | avg vibration rms mmsec | 2.5220 | 2.6 | ±0.6 | ✓ PASS | ISO 10816 (Mechanical Vibration Severity Zones) + API 670 — typical vibration RMS for medium-machinery Zone B operation (1.8-4.5 mm/sec — 'acceptable for long-term operation'; 2.6 mm/sec reflects mid-Zone B) |
| 5 | avg lubricant iron ppm | 39.2589 | 35.0 | ±15.0 | ✓ PASS | ASTM D6595 (Wear Metals in Lubricants) + ISO 4406 (Fluid Cleanliness Codes) — typical iron wear metal concentration in mid-life refinery equipment lubricants (20-60 ppm normal; >100 ppm indicates accelerated wear) |
| 6 | avg bearing temp f | 167.7871 | 168.0 | ±15.0 | ✓ PASS | API 670 (Machinery Protection Systems) — typical rolling-element bearing temperature for refinery machinery (140-180°F normal operating range; >200°F triggers high-temp alarm per API 670 Annex H) |
| 7 | hx fouling efficiency pearson correlation | -0.1936 | -0.18 | ±0.1 | ✓ PASS | TEMA RGP-T-2.4 + Kern correlation — expected inverse correlation between fouling factor and heat exchanger efficiency (fouling adds resistance to heat transfer, reducing efficiency). Validates generator's TEMA-style fouling physics. |
| 8 | pump npsh cavitation pearson correlation | -0.7643 | -0.75 | ±0.15 | ✓ PASS | API 610 + Hydraulic Institute Standards — expected strong inverse correlation between NPSH margin and cavitation index (cavitation_index = 1/NPSH_margin per HI standard formula). Validates generator's cavitation physics coupling. |
| 9 | failure label coupling health gap | 20.3976 | 20.0 | ±6.0 | ✓ PASS | ISO 14224 (Equipment Reliability and Maintenance Data Collection) + API 580/581 (Risk-Based Inspection) — expected health score gap between failed and non-failed equipment (failed assets show ~15-25 point lower health scores in real RAM databases; generator's coefficient is U(12, 28)) |
| 10 | equipment type diversity entropy | 0.9778 | 0.95 | ±0.04 | ✓ PASS | ISO 14224 equipment taxonomy + API 580 RBI classification — 8-class equipment-type diversity benchmark (heat exchanger, compressor, centrifugal/reciprocating pump, steam/gas turbine, electric motor, gearbox; weights [18/16/16/8/10/8/14/10] per industry portfolio mix), normalized Shannon entropy |

**Overall: 100.0/100 — Grade A+**
(10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

---

## Schema highlights

**`equipment_master.csv`** — 8-class equipment taxonomy per **ISO 14224**:

| Type | Weight | Design Efficiency |
|---|---:|---:|
| heat_exchanger | 18% | 86.5% |
| compressor | 16% | 80.5% |
| centrifugal_pump | 16% | 77.5% |
| reciprocating_pump | 8% | 74.5% |
| steam_turbine | 10% | 82.0% |
| gas_turbine | 8% | 36.0% |
| electric_motor | 14% | 93.0% |
| gearbox | 10% | 95.0% |

**`heat_exchanger_performance.csv`** — TEMA RGP-T-2.4 fouling-efficiency
physics:

> efficiency = 86.6 − fouling_factor × 85 − 0.003 × hours + asset_bias + noise
> pressure_drop = 14 + fouling × 150 + noise   (Kern correlation)

The sample's fouling↔efficiency Pearson correlation is r ≈ −0.19 in the
deposition zone — **validates TEMA-style fouling physics**.

**`compressor_performance.csv`** — API 617 surge-margin physics:

> efficiency = 79.2 + asset_bias − max(0, 12 − surge_margin) × 0.15 + noise
> anti_surge_valve_pct = 35 − surge_margin + noise   (recycle opens as surge approaches)
> discharge_pressure = suction × pressure_ratio   (math-exact)

**`pump_operations.csv`** — API 610 + Hydraulic Institute cavitation physics:

> cavitation_index = 1 / NPSH_margin + noise       (HI standard formula)
> efficiency = 76 + asset_bias − cavitation_index × 8 + noise

The sample's NPSH↔cavitation correlation is r ≈ −0.76 — **strong inverse
coupling per Hydraulic Institute** (lower NPSH margin → higher cavitation).

**`vibration_signals.csv`** — ISO 10816 vibration severity with **bearing-
temperature coupling**:

> vibration_rms = 2.28 + health_factor × 0.95 + asset_offset + load + noise
> bearing_temp = 162 + health_factor × 22 + noise

The sample mean RMS is 2.52 mm/sec — **mid-Zone B per ISO 10816** ("acceptable
for long-term operation"). Axial/RMS ratio = 0.62, radial/RMS ratio = 0.88
match ISO 10816 directional vibration distribution exactly.

**`lubrication_analysis.csv`** — ASTM D6595 wear metals with contamination
coupling:

> water_ppm = 85 + contamination × 250 + noise
> iron_ppm = 22 + contamination × 130 + noise
> copper_ppm = 7 + contamination × 50 + noise

**`equipment_labels.csv`** — **FAILURE-COUPLED LABELS** (first feature-
coupled label SKU in the OIL-019/020/021 sequence):

> base_health = N(86, 8)
> if equipment_id in failures: base_health −= U(12, 28)
> health = clip(base_health, 35, 99)
> risk = 'high' if health < 70 else 'medium' if health < 85 else 'low'

The sample observes a **20-point health gap** between failed and non-failed
equipment (failed mean ~65, non-failed mean ~85) — **validates feature-
coupled label generation** per ISO 14224 + API 580/581 RBI standards.

---

## Suggested use cases

1. **Heat exchanger fouling prediction** — regression on
   `fouling_factor` from operating features. **Strong physics signal**:
   fouling-efficiency inverse coupling validated to r ≈ −0.19.
2. **Compressor surge margin prediction** — regression on
   `surge_margin_pct` from suction + flow + ratio features per API 617.
3. **Pump cavitation prediction** — binary classifier on high
   cavitation (`cavitation_index > 0.3`) from NPSH + head features.
   **Very strong physics**: NPSH-cavitation r ≈ −0.76.
4. **Vibration anomaly detection** — multi-variate anomaly detection
   on RMS + axial + radial + bearing temperature per ISO 10816.
5. **Lubricant wear metal regression** — predict iron/copper ppm
   from contamination score per ASTM D6595.
6. **Remaining useful life (RUL) regression** — predict `rul_days`
   from upstream features. Standard PHM/CBM benchmark target.
7. **Health score regression** — predict `health_score` from
   upstream features. **Feature-coupled label** — models trained
   on this WILL learn meaningful patterns (unlike OIL-019/020
   labels).
8. **3-class failure risk classification** — multi-class classifier
   on `failure_risk_class` (low/medium/high) from upstream features.
9. **Equipment failure prediction** — binary classifier on
   "equipment_id in equipment_failures" from vibration + lubrication
   + thermal features. The failed equipment subset (~10-15% of
   assets) shows distinct health distributions.
10. **Multi-table relational ML** — entity-resolution and graph
    neural-network learning across the 12 joinable tables via
    `equipment_id`.

---

## Loading

```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/oil021-sample", data_files="vibration_signals.csv")
print(ds["train"][0])
```

Or with pandas:

```python
import pandas as pd
eq     = pd.read_csv("hf://datasets/xpertsystems/oil021-sample/equipment_master.csv")
vib    = pd.read_csv("hf://datasets/xpertsystems/oil021-sample/vibration_signals.csv")
lube   = pd.read_csv("hf://datasets/xpertsystems/oil021-sample/lubrication_analysis.csv")
fail   = pd.read_csv("hf://datasets/xpertsystems/oil021-sample/equipment_failures.csv")
labels = pd.read_csv("hf://datasets/xpertsystems/oil021-sample/equipment_labels.csv")

# All 12 tables joinable by equipment_id
joined = eq.merge(labels, on="equipment_id")

# Failed vs non-failed equipment for RUL ML
failed_ids = set(fail["equipment_id"])
labels["failed_flag"] = labels["equipment_id"].isin(failed_ids).astype(int)
# Now you have a clean feature-coupled binary classification target
```

---

## Reproducibility

All generation is deterministic via the integer `seed` parameter (driving
both `random.seed` and `np.random.seed`). A seed sweep across
`[42, 7, 123, 2024, 99, 1]` confirms Grade A+ on every seed in this sample.

---

## Honest disclosure of sample-scale limitations

This is a **sample** product calibrated for predictive maintenance ML
research, not for live operational decisions. Several notes:

1. **Detail-table coverage is equipment-type-conditional.** Only
   ~17% of equipment are heat_exchanger type → only ~60 HX units
   appear in `heat_exchanger_performance.csv`. Similarly for
   compressors (~50 units), pumps (~75 units across centrifugal +
   reciprocating). The remaining equipment types (turbines, motors,
   gearboxes) only generate vibration / lubrication / thermal /
   efficiency / alarm / maintenance / label rows — no dedicated
   performance table. For turbine-specific or motor-specific ML,
   the full product v1.1 will add type-specific detail tables.

2. **Failure rate is ~13% at sample scale** vs declared 10% — small-
   sample variance at n=350. The full product (5000+ equipment)
   converges closer to the declared 10% per ISO 14224 RAM
   statistics.

3. **All equipment timestamps start January 2025** — the
   `commissioning_date` ranges 2005-2025 but the operational time-
   series tables all use `timestamp = 2025-01-01 + offset`. There's
   no relationship between equipment age (from commissioning date)
   and time-series timestamp index. For age-conditional analysis,
   use `equipment_master.age_years` as a feature rather than
   inferring from timestamps.

4. **Efficiency degradation in `efficiency_tracking.csv` is mild
   over the 120-day window** — degradation rate ~0.0009/hr × 2880 hr
   = ~2.6% total decline over 4 months. The runtime↔efficiency
   correlation is weak (r ≈ −0.05) at sample horizon. For
   long-horizon degradation ML, use the full product (3-year
   simulation showing larger degradation envelopes).

5. **Maintenance event dates are uniformly distributed across the
   year**, not coupled to operational anomalies. Real maintenance
   schedules cluster around failures and post-anomaly inspections.
   Treat `event_date` as a sampling reference rather than a true
   operational sequence.

6. **Alarm trip rate is ~8.6%** — within declared 8% tolerance but
   trip events are not coupled to specific vibration / temperature /
   surge thresholds in the upstream tables. For threshold-triggered
   alarm ML, derive synthetic alarms from upstream feature
   thresholds (e.g., `HI_VIB` when `vibration_rms > 4.5`).

7. **Vibration RMS↔bearing temperature correlation is moderate**
   (r ≈ 0.18) — physically correct (both rise with degradation) but
   weaker than real ISO 10816 condition monitoring data shows.
   Generator's noise variance dominates the degradation signal at
   short horizons.

8. **Compressor surge margin↔efficiency coupling is weak in normal
   operation** (r ≈ 0.03) because the penalty only fires when
   surge_margin < 12 — most sample rows have surge_margin ≥ 12 and
   experience no penalty. To study surge-mode operation
   specifically, filter to `surge_margin_pct < 12` (~5% of rows).

---

## Cross-references to other XpertSystems OIL SKUs

This SKU is the **first cross-stream equipment SKU** in the catalog —
applicable to upstream, midstream, and downstream operations:

| SKU | Layer | Equipment focus |
|---|---|---|
| OIL-012 | Upstream | Rig sensor IoT (drilling equipment) |
| OIL-014 | Upstream | Artificial lift performance (ESP/rod pump/gas lift) |
| OIL-019 | Downstream | Refinery process operations (per-unit) |
| **OIL-021** | **Cross-stream** | **Equipment performance: HX/compressor/pump/turbine/motor/gearbox** *(this SKU)* |

**OIL-021 vs OIL-014**: OIL-014 focuses on **production artificial-lift
equipment** (ESP, rod pump, gas lift) with lift-system-specific physics
(fillage, fluid pound, gas interference). OIL-021 focuses on **general
rotating + static equipment** (HX, compressor, centrifugal pump, turbine,
motor) with API/ISO/TEMA-anchored physics. Use OIL-014 for production
optimization ML, OIL-021 for **predictive maintenance + RAM** ML across
the whole value chain.

---

## Full product

The **full OIL-021 dataset** ships at **5,000 equipment × 240-period
operational simulation** (prod mode) producing several million time-series
rows with **type-specific detail tables for all 8 equipment classes**,
**3-year simulation horizon** showing meaningful degradation envelopes,
**threshold-coupled alarm generation** (alarms triggered by upstream
feature crossings), and **proper maintenance scheduling** (clustered
around failure events) — licensed commercially. Contact XpertSystems.ai
for licensing terms.

📧 **pradeep@xpertsystems.ai**
🌐 **https://xpertsystems.ai**

---

## Citation

```bibtex
@dataset{xpertsystems_oil021_sample_2026,
  title  = {OIL-021: Synthetic Equipment Performance Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil021-sample}
}
```

## Generation details

- Sample version    : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-22 20:27:23 UTC
- Equipment         : 350
- Telemetry periods : 120 (hourly)
- Vibration periods : 240 (10-min interval)
- Lubrication samples per asset: 16 (biweekly over ~8 months)
- Thermal periods   : 60 (hourly)
- Efficiency periods: 120 (daily)
- Equipment types   : 8 (heat_exchanger, compressor,
                      centrifugal_pump, reciprocating_pump, steam_turbine,
                      gas_turbine, electric_motor, gearbox)
- Plants            : 6 (Permian Processing, North Sea Offshore,
                      LNG Gulf Coast, Middle East Refinery, Canadian
                      Upgrader, Gulf Coast Petrochemical)
- Manufacturers     : 7 (GE, Siemens, Emerson, Honeywell,
                      Sulzer, Baker Hughes, Elliott)
- Failure modes     : 8 (bearing/seal/cavitation/surge/thermal/lube/
                      misalignment/fouling per ISO 14224)
- Calibration basis : API 660, TEMA RGP-T-2.4, API 617, API 670,
                      ISO 10816, API 610, Hydraulic Institute, ASTM D6595,
                      ISO 4406, API 580/581, ISO 14224, DNV-RP-G101,
                      IEC 60812, ISA-18.2, Kern correlation
- Overall validation: 100.0/100 — Grade A+