Datasets:

xpertsystems
/

oil039-sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - predictive-maintenance
  - pdm
  - rul
  - remaining-useful-life
  - prognostics
  - phm
  - condition-monitoring
  - oil-and-gas
  - rotating-equipment
  - fft
  - vibration
  - lubrication
  - thermal
  - iso-17359
  - iso-13373
  - iso-14224
  - api-rp-670
  - api-rp-691
  - api-rp-580
  - iogp
  - degradation-modeling
pretty_name: OIL-039 — Synthetic Predictive Maintenance Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-039 — Synthetic Predictive Maintenance Dataset (Sample)

A schema-identical preview of OIL-039, the XpertSystems.ai synthetic predictive-maintenance and prognostics dataset for oil & gas rotating and stationary assets. The full product covers ~12,000 assets across a 730-day horizon with high-fidelity 7-band FFT decomposition. This sample is the generator's sample mode (250 assets × 90 days × 2 samples/day) covering all 13 product tables, with pre-built per-timestamp RUL labels and 7d/30d failure probabilities ready for PHM model training.

Built by XpertSystems.ai — Synthetic Data Platform Contact pradeep@xpertsystems.ai · xpertsystems.ai License CC-BY-NC-4.0 (sample); commercial license available for the full product.

OIL-039 vs OIL-038 — what's different

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

Dimension	OIL-039 (this dataset)	OIL-038 (equipment-failure events)
Primary workload	PHM / RUL prognostics	Failure-event analytics + reliability KPIs
ML labels	Per-timestamp RUL + 7d/30d failure probs	Per-asset 30d/90d failure probability
FFT decomposition	4-band (sample) / 7-band (full)	None
Failure-event detail	Compact (3 tables)	Rich (3 equipment groups × 16 tables)
Telemetry tables	6 modalities (vibration + FFT + thermal + lubrication + pressure + acoustic)	5 modalities (vibration + thermal + lubrication + environmental + alarms)
Time density	2 samples/day at sample scale	1 sample/day at sample scale
Best for	Time-series prognostics, RUL regression, fault-signature classification	Reliability KPI benchmarking, MTBF/MTTR fitting, multi-modal anomaly detection

Buy or download both for full PHM + reliability coverage. They share the upstream-asset and ISO 14224 / API RP 580 / API RP 670 calibration heritage.

What's inside

13 CSV tables covering the complete PdM data plane: equipment master → 6-modality telemetry (vibration + FFT + thermal + lubrication + pressure + acoustic) → health scores → RUL labels → failure probabilities → maintenance work orders → failure & downtime events.

Table	Rows (sample)	What it represents
`equipment_master.csv`	250	10-type asset master with criticality, MTBF, maintenance strategy
`vibration_signatures.csv`	45,000	RMS, peak, kurtosis, crest factor, sensor quality
`fft_spectra.csv`	180,000	4-band FFT (1x, 2x, bearing, cavitation) × time × asset
`temperature_anomalies.csv`	45,000	Temperature, thermal gradient, anomaly score, thermal state
`lubrication_analysis.csv`	45,000	Viscosity, particle count, water ppm, contamination
`pressure_telemetry.csv`	45,000	Pressure, transient flags, flow rate
`acoustic_signals.csv`	45,000	Acoustic dB, ultrasonic energy, cavitation score
`equipment_health_scores.csv`	45,000	Per-timestamp health index, degradation, severity band
`remaining_useful_life.csv`	45,000	Predicted RUL hours/days + 5-class RUL bucket
`predictive_labels.csv`	45,000	7d + 30d failure probability + target failure mode + root cause
`maintenance_workorders.csv`	~110	7-type repair categories with labor hours, parts cost
`failure_events.csv`	~55	IOGP severity (major/critical/catastrophic) + production loss
`downtime_events.csv`	~55	Downtime hours, production impact USD, restart success

Total: ~610,000 rows, ~36 MB. The full OIL-039 product is ~140 million rows.

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:

SAE ARP4761 / API RP 691 — rotating equipment design MTBF benchmarks.
ISO 17359 Condition monitoring + ISO 13373-1 Vibration monitoring — crest factor and kurtosis severity bands.
API RP 670 Machinery protection systems — FFT decomposition standards.
ISO 10816 / 20816 Mechanical vibration evaluation.
API RP 580 Risk-based inspection — criticality-tier distributions.
Reliability Web Maintenance Strategy Survey — proactive maintenance share.
ARC Advisory Group Predictive Maintenance Maturity Survey — sensor detection share benchmarks.
IOGP Safety Performance Indicators Report — incident severity pyramid.
ISO 14224:2016 Reliability and Maintenance Data — work-classification.
ISO 45001 Clause 10.2 — work-order closure benchmarks.
PHM Society conventions — synthetic PdM dataset label quality norms.

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	Median design MTBF (hours)	4,000–15,000	4,506	SAE ARP4761 / API RP 691
M02	Vibration crest factor (mean)	3–7	4.92	ISO 17359 / ISO 13373-1
M03	Proactive maintenance share (floor)	≥ 0.45	0.624	Reliability Web survey
M04	Criticality tier ≥ 3 share	0.60–0.80	0.692	API RP 580 RBI
M05	IOGP severity pyramid — major share	0.45–0.75	0.679	IOGP Safety Performance
M06	Sensor-based detection share (floor)	≥ 0.40	0.566	ARC Advisory PdM
M07	Work-order close rate (floor)	≥ 0.65	0.748	ISO 45001 / CCPS
M08	Repair-type taxonomy coverage (floor)	≥ 7	7	ISO 14224:2016
M09	FFT frequency-band coverage (floor)	≥ 4	4	ISO 17359 / API 670
M10	Pre-built ML label quality (mean)	0.92–0.98	0.959	PHM Society

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

Suggested use cases

Remaining Useful Life (RUL) regression — remaining_useful_life.csv provides per-timestamp RUL targets in hours/days plus 5-class RUL buckets (<7d / 7–30d / 30–90d / 90d–1y / >1y). 45,000 timestamps × 250 assets is right-sized for LSTM, transformer, and gradient-boosting prognostics baselines.
Fault-signature classification from FFT — 4-band FFT spectra (1x, 2x, bearing, cavitation) × fault_signature labels enables direct bearing-fault, cavitation, and misalignment classification training.
7-day + 30-day failure probability — predictive_labels.csv carries both horizons calibrated via sigmoid on degradation index. Useful for early-warning vs. medium-term planning model comparisons.
Multi-modal degradation modeling — 6 telemetry modalities are per-timestamp aligned per asset (vibration + FFT + thermal + lubrication + pressure + acoustic), enabling true multi-modal fusion research.
Maintenance-reset event detection — degradation trajectories include stochastic "reset points" simulating maintenance interventions; useful for change-point detection and survival analysis with competing-risks models.
PHM Society challenge-style benchmarking — pre-built target labels (target_failure_mode + target_root_cause) follow PHM Society conventions for end-to-end prognostics evaluation.
Maintenance strategy ROI quantification — 4 strategies (preventive / condition_based / run_to_failure / reliability_centered) × workorder & downtime tables enable strategy-vs-availability ROI modeling.

Loading

from datasets import load_dataset

# Load equipment master
master = load_dataset(
    "xpertsystems/oil039-sample",
    data_files="equipment_master.csv",
    split="train",
)
# Load RUL labels and predictive labels for prognostics training
rul = load_dataset(
    "xpertsystems/oil039-sample",
    data_files="remaining_useful_life.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil039-sample",
    data_files="predictive_labels.csv",
    split="train",
)
# Load multi-modal telemetry (each ~45K rows)
vibration = load_dataset(
    "xpertsystems/oil039-sample",
    data_files="vibration_signatures.csv",
    split="train",
)

Or with pandas directly:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/oil039-sample",
    filename="fft_spectra.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

All 13 tables join on:

equipment_id → master ↔ all telemetry ↔ labels ↔ workorders ↔ failures
equipment_id + timestamp → 6-modality telemetry per-row alignment
failure_id → failure events ↔ downtime events
workorder_id → maintenance work orders

Schema highlights

equipment_master.csv — equipment_id, facility_id, asset_type (10-class: centrifugal_pump / reciprocating_compressor / centrifugal_compressor / gas_turbine / electric_motor / control_valve / pipeline_segment / separator / heat_exchanger / gearbox), facility_type (8-class), region (8-class), manufacturer (6-class), install_date, asset_age_years, criticality_score ∈ {1, 2, 3, 4, 5}, hazardous_service, offshore_flag, maintenance_strategy ∈ {preventive, condition_based, run_to_failure, reliability_centered}, design_mtbf_hours, plus 6 baseline telemetry reference values per asset.

vibration_signatures.csv — timestamp, equipment_id, asset_type, rpm, vibration_rms, vibration_peak, kurtosis (ISO 13373-1 impulsive-fault indicator), crest_factor (ISO 17359 healthy 3–5 vs. faulty 5–9), severity_band ∈ {normal, watch, warning, critical, failure}, sensor_quality.

fft_spectra.csv — 4 frequency bands at sample scale (1x, 2x, bearing, cavitation), 7 in full product (adds 3x, bearing_inner, bearing_outer, gear_mesh). Each row carries dominant_frequency_hz, harmonic_amplitude, spectral_energy, and fault_signature (matched to the asset's target failure mode when the band-specific multiplier exceeds the degradation threshold).

remaining_useful_life.csv — predicted_rul_hours, predicted_rul_days, rul_confidence ∈ [0, 1], rul_bucket ∈ {<7d, 7-30d, 30-90d, 90d-1y, >1y}. PHM Society-style per-timestamp RUL targets.

predictive_labels.csv — failure_probability_7d, failure_probability_30d, maintenance_priority ∈ {1, 2, 3, 4, 5}, target_failure_mode (50 distinct modes across asset types), target_root_cause (12-class), label_quality ∈ [0, 1].

failure_events.csv — severity ∈ {major, critical, catastrophic} (IOGP pyramid), detected_by ∈ {vibration_alarm, temperature_alarm, operator_round, predictive_model, shutdown_trip}, production_loss_bbl, safety_impact_flag.

Calibration notes & limitations

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

Aggressive 90-day degradation simulation. The sample window compresses a full degradation trajectory into 90 days for ML utility, so the severity_band distribution is skewed toward warning/critical/failure (≈ 70% combined), with only ~2% of timestamps in normal. This is intentional — it provides positive-class density for failure classifiers and RUL regressors. For studies that require steady-state healthy operations, filter to degradation_score < 0.30 or use the early window (first 14 days). The full product simulates 730 days with slower drift and recovers a healthy normal-band majority.
Vibration RMS units differ from OIL-038. Mean vibration RMS in this dataset is ~0.39 (different unit normalization than OIL-038's ~7.14 in mm/s ISO 10816 units). Crest factor (mean 4.92) and kurtosis (mean 7.1) are validated against ISO 13373-1 / ISO 17359 instead, since they're dimensionless and directly comparable across calibrations. For absolute ISO 10816 vibration severity classification, use OIL-038.
FFT fault_signature label sparsity. The fault_signature column in fft_spectra.csv is 99.6% "none" in the sample. The label is set only when the band-specific multiplier exceeds a degradation-dependent threshold, which is rare in the sample window. For ML use, derive your own threshold from harmonic_amplitude × spectral_energy on the matched-fault-mode band, or use the full product's 7-band decomposition which exposes 3 additional fault signatures.
Lubrication water-ppm trajectory. Median water ppm is ~375 across the sample (above ISO 4406 clean threshold of 200) because the generator's water content formula 80 + 600 × degradation puts most degraded assets above the clean threshold. This is consistent with the aggressive degradation simulation (point 1). For "healthy lubrication" baselining, filter to contamination_level < 0.2.
Per-timestamp RUL skew. The RUL bucket distribution at sample scale is heavily weighted toward 30-90d and 90d-1y (≈83% combined) with only ~17% in the <7d and 7-30d buckets that ML teams care about most for early warning. For balanced training, oversample on rul_bucket ∈ {<7d, 7-30d} or use the full product (730-day window exposes more imminent-failure windows per asset).
Workorder + failure event counts. Sample mode produces ~110 workorders and ~55 failure events. These are sparse on purpose (modeling realistic event rates over 90 days at 250 assets) but limit small-sample statistics on severity/severity-pyramid metrics — the scorecard's M05 (IOGP major share) tolerance is intentionally widened to ±0.15 for this reason. The full product recovers tight pyramid ratios at production scale.
Deterministic seeding. All 13 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-039 product covers ~~12,000 assets × 730 days × 8 samples/day (~~140 million telemetry rows total), with high-fidelity 7-band FFT decomposition, slower-drift degradation trajectories that recover healthy normal-band majority, dense ground-truth failure labels with balanced RUL bucket distributions, and configurable maintenance-strategy mode-packs for ROI quantification. Available under commercial license — contact 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.