--- 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 **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. --- ## 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 ```python 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: ```python 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: 1. **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. 2. **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. 3. **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. 4. **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`. 5. **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). 6. **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. 7. **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](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).