--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - equipment-failure - predictive-maintenance - reliability - mtbf - mttr - oil-and-gas - rotating-equipment - pump - valve - compressor - vibration - thermal-monitoring - lubrication - iso-14224 - iso-10816 - iso-4406 - api-rp-580 - api-rp-691 - isa-18-2 - eemua-191 - sae-arp4761 - condition-monitoring pretty_name: "OIL-038 — Synthetic Equipment Failure Dataset (Sample)" size_categories: - 100K **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. --- ## 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. --- ## 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. --- ## 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.** --- ## 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. --- ## Loading ```python 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: ```python 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 --- ## 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}. --- ## 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}. --- ## 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](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).