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xpertsystems
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oil021-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Languages:
English
Size:
100K<n<1M
Tags:
Synthetic
oil-and-gas
equipment-performance
predictive-maintenance
condition-monitoring
rul-prediction
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oil021-sample / README.md
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Initial release: OIL-021 sample, 350 equipment / 180K rows, Grade A+ (10/10)
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metadata 
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 

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

Or with pandas:

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 

@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+