xpertsystems/oil038-sample

OIL-038 — Synthetic Equipment Failure Dataset (Sample)

A schema-identical preview of OIL-038, the XpertSystems.ai synthetic equipment-failure and predictive-maintenance dataset for oil & gas rotating and stationary assets (pumps, valves, compressors). The full product covers ~6,500 facilities across a 3-year horizon with ~6 million telemetry rows. This sample is the generator's demo mode (20 facilities × ~18 assets × 90 days) covering all 16 product tables.

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.

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What's inside

16 CSV tables covering the complete equipment-reliability lifecycle: facility master → asset master → failure events (3 equipment groups) → telemetry (vibration / thermal / lubrication / environmental / alarms) → maintenance work orders → root cause analysis → downtime → reliability KPIs → sensor drift → pre-built ML labels.

Table	Rows (sample)	What it represents
facility_master.csv	20	10-type facility master with environment, corrosivity, maturity
equipment_master.csv	~340	6-type asset master with baseline MTBF, criticality, maintenance strategy
pump_failures.csv	~130	Centrifugal + PD pump failures with cavitation, seal, bearing signals
compressor_failures.csv	~90	Reciprocating + centrifugal compressor failures with surge, vibration
valve_failures.csv	~175	Control + isolation valve failures with leakage, actuator response
vibration_signals.csv	~30,500	Daily vibration telemetry: RMS velocity, FFT peak, kurtosis, crest factor
thermal_profiles.csv	~30,500	Daily thermal telemetry: temperature, stress index, cooling, cycle counts
lubrication_analysis.csv	~4,400	Weekly oil samples: quality, contamination, water ppm, ISO 4406 codes
maintenance_work_orders.csv	~400	8-type repair categories with labor hours, parts delays
root_cause_analysis.csv	~400	RCA method, immediate/systemic causes, contributing factors
downtime_events.csv	~400	Shutdown type, downtime hours, production loss, repair cost
reliability_metrics.csv	~340	Per-asset MTBF, MTTR, availability, reliability score, risk rank
sensor_drift.csv	~1,000	6 sensor types with drift %, calibration status
environmental_conditions.csv	~30,500	Daily environmental telemetry: ambient, humidity, salt fog, H₂S
alarm_events.csv	~15,500	7-class alarm taxonomy with severity, response delay, flood flag
equipment_failure_labels.csv	~340	Pre-built ML labels: 30d/90d failure probability, priority, action

Total: ~125,000 rows, ~9.4 MB. The full OIL-038 product is ~6 million rows.

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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 baseline MTBF benchmarks.
• SAE ARP rotating equipment maintenance standards — typical MTTR ranges.
• PSAM / SAE rotating equipment performance — availability benchmarks.
• ISO 4406:2021 Hydraulic fluid cleanliness code + Noria Lubrication Practices — lubricant water content thresholds.
• ARC Advisory Group Predictive Maintenance Maturity Survey — sensor-based detection share benchmarks.
• Reliability Web Maintenance Strategy Survey — proactive maintenance strategy share.
• API RP 580 Risk-Based Inspection — criticality-tier distribution.
• ISO 14224:2016 Reliability and Maintenance Data — work-classification taxonomy.
• ISO 10816 Mechanical vibration evaluation — vibration severity classes.
• ISA 18.2 / EEMUA 191 — alarm management taxonomy.

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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 baseline MTBF (hours)	4,000–15,000	4,119	SAE ARP4761 / API RP 691
M02	Median MTTR (hours)	8–24	17.4	SAE ARP rotating equipment
M03	Median availability (floor)	≥ 0.92	0.992	PSAM / SAE
M04	Lubrication water ppm median (ceiling)	≤ 250	77	ISO 4406 / Noria
M05	Sensor-based detection share (floor)	≥ 0.40	0.578	ARC Advisory PdM
M06	Proactive maintenance share (floor)	≥ 0.40	0.516	Reliability Web survey
M07	Critical+High criticality share	0.40–0.60	0.516	API RP 580 RBI
M08	Failure-mode taxonomy coverage	17–25	25	ISO 14224 / API RP 691
M09	Repair-type taxonomy coverage (floor)	≥ 8	8	ISO 14224:2016
M10	Alarm-code taxonomy coverage (floor)	≥ 7	7	ISA 18.2 / EEMUA 191

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

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Suggested use cases

• Predictive maintenance modeling — train classifiers that predict failure_probability_30d or failure_probability_90d from vibration, thermal, lubrication, and environmental telemetry. Pre-built labels in equipment_failure_labels.csv.
• Remaining useful life (RUL) regression — use health_index time series in vibration_signals.csv joined with failure events for prognostics models. Each asset has a degradation trajectory across the 90-day sample window.
• Fault classification across 25 fault modes — pump (cavitation, bearing wear, seal leak, …), compressor (surge, rotor imbalance, …), and valve (actuator failure, stiction, …) failure modes are decomposable by equipment type for multi-task learning.
• Alarm-flood and ISA 18.2 alarm-management benchmarking — 15K alarm events × 7 codes × 5-class severity supports nuisance-alarm filtering, rationalization, and prioritization research.
• MTBF / MTTR / availability benchmarking — per-asset reliability metrics in reliability_metrics.csv enable Weibull / exponential reliability function fitting and what-if maintenance-strategy ROI modeling.
• Sensor-fusion and anomaly detection — vibration + thermal + lubrication
  • environmental telemetry are pre-aligned per asset per timestamp, enabling multi-modal anomaly detection benchmarks.
• Maintenance strategy ROI — maintenance_strategy × availability cross-tab enables direct quantification of reactive vs. time-based vs. condition-based vs. predictive program ROI.

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Loading

from datasets import load_dataset

# Load failure events
pumps = load_dataset(
    "xpertsystems/oil038-sample",
    data_files="pump_failures.csv",
    split="train",
)
# Load telemetry (largest tables; consider streaming)
vibration = load_dataset(
    "xpertsystems/oil038-sample",
    data_files="vibration_signals.csv",
    split="train",
)
labels = load_dataset(
    "xpertsystems/oil038-sample",
    data_files="equipment_failure_labels.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/oil038-sample",
    filename="reliability_metrics.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

All 16 tables join on:

• facility_id → facility ↔ equipment ↔ all telemetry/failure/alarm tables
• equipment_id → equipment ↔ failures ↔ telemetry ↔ reliability ↔ labels
• failure_id → failure events ↔ work orders ↔ RCA ↔ downtime

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Schema highlights

equipment_master.csv — equipment_id, facility_id, equipment_type (6-class: centrifugal_pump / positive_displacement_pump / control_valve / isolation_valve / reciprocating_compressor / centrifugal_compressor), manufacturer_family (6-class), installed_date, equipment_age_years, criticality ∈ {low, medium, high, critical}, rated_capacity, design_margin, normal_operating_pressure_psi, normal_operating_temp_f, maintenance_strategy ∈ {reactive, time_based, condition_based, predictive}, sensor_coverage_score, baseline_mtbf_hours.

Failure events — 18 fault modes split by equipment family:

• Pumps (centrifugal + PD): cavitation, bearing_wear, seal_leak, shaft_misalignment, impeller_erosion, motor_overload, valve_plate_wear, overpressure, lubrication_failure, drive_failure
• Valves (control + isolation): actuator_failure, stiction, seat_leakage, positioner_fault, stem_corrosion, packing_leak, seal_degradation, blocked_operation
• Compressors (reciprocating + centrifugal): surge, valve_failure, rod_load_exceedance, lubrication_failure, cylinder_overheat, rotor_imbalance, fouling, high_vibration

Plus 14-class root_cause (normal_wear, poor_lubrication, process_upset, corrosion, maintenance_error, operator_error, design_limit_exceeded, contamination, thermal_cycling, misalignment, foreign_object_damage, sensor_drift, high_humidity, H2S_exposure).

vibration_signals.csv — rms_velocity_mm_s (ISO 10816 unit), fft_peak_hz, kurtosis, crest_factor, health_index (per-asset degradation trajectory), load_factor. Daily samples per asset.

lubrication_analysis.csv — oil_quality_score, contamination_index, water_ppm, particle_count_iso_code ∈ {16/14/11, 17/15/12, 18/16/13, 19/17/14, 20/18/15, 21/19/16} (ISO 4406 cleanliness codes), viscosity_change_pct.

alarm_events.csv — alarm_code ∈ {HIGH_VIB, HIGH_TEMP, LOW_OIL_QUALITY, PRESSURE_DEVIATION, SENSOR_DRIFT, SURGE_RISK, CAVITATION_RISK}, severity ∈ {info, low, medium, high, critical} (ISA 18.2), response_delay_minutes, acknowledged_flag, alarm_flood_flag.

equipment_failure_labels.csv — pre-built ML labels per asset: failure_probability_30d, failure_probability_90d, criticality_score, risk_rank_score, predictive_maintenance_priority ∈ {low, medium, high, urgent}, recommended_action ∈ {normal_monitoring, increase_monitoring, schedule_maintenance, immediate_inspection}.

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Calibration notes & limitations

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

1. Telemetry health-index trajectory. The vibration and thermal telemetry use an aggressive degradation drift model — across the 90-day sample window, the mean health_index drops to ~0.40 and ~80% of vibration RMS values exceed ISO 10816 Class 4 alarm thresholds (≥ 4.5 mm/s). This is intentional: the sample compresses a longer degradation horizon into 90 days so ML utility is high (positive-class density for failure classifiers). For studies that require steady-state healthy operations, filter to health_index > 0.65 or use the early telemetry window (first 14 days). The full product uses a slower degradation drift over 1,095 days.

2. Alarm acknowledgment rate. Sample acknowledgment rate (~0.78) is below the ISA 18.2 mature target of ≥ 0.85. This is a function of the stress-coupled acknowledgment probability in the generator and is intentional (sample window is degradation-heavy, so operators are modeled as overloaded). Filter to severity ∈ {high, critical} to recover the mature acknowledgment rate.

3. Alarm flood rate. The 12.5% flood rate in alarm_events.csv is slightly above EEMUA 191's ≤ 10% mature target — same root cause as acknowledgment rate. Useful for training alarm-flood detection models (positive-class density). The full product targets 6–9% flood rate at production scale.

4. Pre-built ML label priority skew. predictive_maintenance_priority concentrates on low (44%) and medium (56%) at sample scale, with high and urgent near zero. This is because risk_rank_score distributes between 0.25–0.50 for most assets at the 90-day demo window. For balanced multi-class training, use failure_probability_90d directly with custom quantile thresholds, or use the full product's 3-year window which exhibits the full priority distribution.

5. No-failure asset share. ~29% of assets have zero failures across the 90-day sample (Poisson zero-events). This is correct probabilistic behavior, but means median observed_mtbf_hours is the time-horizon itself (2,160 hours) for those assets. Filter to failure_count > 0 when computing reliability KPIs.

6. Deterministic seeding. All 16 tables are deterministic on --seed. Catalog default is seed 42. Seed sweep verifies Grade A+ across {42, 7, 123, 2024, 99, 1}.

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Commercial / full product

The full OIL-038 product covers 6,500 facilities × 38 assets across a 1,095-day horizon (6 million telemetry rows total), with calibrated degradation drift, ISA 18.2 / EEMUA 191-aligned alarm acknowledgment and flood rates, and a balanced priority-label distribution. 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.