--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - vibration - fft - condition-monitoring - predictive-maintenance - sensor-data - oil-and-gas - rotating-equipment - iso-10816 - iso-20816 - iso-17359 - iso-13373 - iso-14224 - iso-4406 - api-rp-670 - api-rp-580 - iso-18436 - signal-processing - 3-axis-vibration - harmonic-analysis pretty_name: "OIL-040 — Synthetic Vibration & Sensor 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-040 vs OIL-038 vs OIL-039 — what's different OIL-038, OIL-039, and OIL-040 are three complementary upstream-asset PdM products covering different research workloads: | Dimension | **OIL-040** (this dataset) | OIL-039 (PHM/RUL) | OIL-038 (failure events) | |---|---|---|---| | Primary focus | **3-axis vibration + FFT signal processing** | Per-timestamp RUL prognostics | Failure-event analytics + reliability KPIs | | 3-axis vibration (X/Y/Z) | Yes (horizontal-dominant) | RMS only | RMS only | | FFT spectra | 32-bin (sample), 96-bin (full) | 4-band (sample), 7-band (full) | None | | ISO 10816 calibration | Yes — median RMS in normal/alert band | Different unit normalization | ISO 10816 absolute units | | Sensor pack tiers | 4-tier (basic / standard / advanced / edge_ai) | None | None | | Pre-built labels | anomaly + fault_class + severity + rare_event + 30d target | RUL hours/days + 7d/30d failure prob | 30d/90d failure probability | | Best for | Signal-processing ML, FFT-based fault classification, ISO 10816 severity work | RUL regression, prognostics benchmarks | Reliability KPI fitting, MTBF/MTTR | Buy or download **all three** for complete PdM coverage. They share the upstream-asset, ISO 14224 / API RP 580 / API RP 670 calibration heritage. --- ## What's inside 12 CSV tables covering 3-axis vibration + 5 supporting telemetry modalities + workorders + failures + per-record health/RUL/labels + 32-bin FFT spectra. | Table | Rows (sample) | What it represents | |---|---:|---| | `equipment_master.csv` | 80 | 12-type asset master with sensor pack, criticality, primary fault mode | | `vibration_timeseries.csv` | 9,600 | 3-axis (X/Y/Z) RMS mm/s + crest factor + kurtosis per timestamp | | `temperature_telemetry.csv` | 9,600 | Thermocouple temperature, gradient, overheat flag | | `pressure_telemetry.csv` | 9,600 | Pressure psi, delta, spike flag | | `acoustic_signals.csv` | 9,600 | Acoustic dB, ultrasonic energy, anomaly score | | `lubrication_analysis.csv` | 9,600 | Viscosity index, contamination, water ppm, lubrication risk | | `maintenance_workorders.csv` | ~80 | 6-type maintenance work orders with priority, downtime, notes quality | | `failure_events.csv` | ~5 | Per-failure mode + severity + repair cost + production loss | | `health_scores.csv` | 9,600 | Per-record health index, degradation score, condition state | | `remaining_useful_life.csv` | 9,600 | Per-record RUL days, 30d failure probability, maintenance recommendation | | `vibration_labels.csv` | 9,600 | Anomaly + fault class + severity + rare event flag + 30d target | | `fft_spectra.csv` | ~307,000 | 32-bin FFT (5–1000 Hz) with rotational harmonics + fault-defect frequency energy | Total: ~388,000 rows, ~40 MB. The full OIL-040 product is ~80 million rows with 96-bin FFT decomposition. --- ## 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: - **ISO 10816 / ISO 20816** Mechanical vibration evaluation — vibration severity bands (normal / alert / alarm / shutdown) for Class II rotating equipment. - **ISO 17359** Condition monitoring of machines — crest factor severity bands. - **ISO 13373-1 / ISO 13373-2** Vibration condition monitoring — kurtosis, spectrum analysis. - **ISO 18436-2** Vibration analyst certification conventions — horizontal / vertical / axial axis amplitude relationships. - **API RP 670** Machinery Protection Systems — FFT decomposition standards and rotational harmonic boost relationships (1x, 2x, 3x, 4x rpm). - **API RP 580** Risk-Based Inspection — criticality-tier distributions. - **ISO 14224:2016** Reliability and Maintenance Data — equipment taxonomy and maintenance work classification. - **ISO 4406:2021** Hydraulic fluid power: cleanliness code thresholds. - **ARC Advisory PdM Maturity Survey** + **ISA-95 / OSDU** — advanced sensor pack deployment baselines. - **Noria Lubrication Practices** — water content thresholds. --- ## 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 | Vibration RMS median (mm/s) | 1.5–4.5 | **2.38** | ISO 10816 / ISO 20816 | | M02 | Crest factor (mean) | 2–6 | **3.30** | ISO 17359 / ISO 13373-1 | | M03 | Kurtosis (mean) | 2–6 | **4.35** | ISO 13373-1 | | M04 | Lubrication water ppm (ceiling) | ≤ 250 | **112** | ISO 4406 / Noria | | M05 | Horizontal-axis dominance (X/RMS) | 0.85–1.15 | **1.006** | ISO 18436-2 / API RP 670 | | M06 | Criticality tier ≥ 3 share | 0.60–0.80 | **0.775** | API RP 580 RBI | | M07 | Maintenance-type coverage (floor) | ≥ 6 | **6** | ISO 14224:2016 | | M08 | FFT bin coverage (floor) | ≥ 32 | **32** | API RP 670 / ISO 13373-2 | | M09 | Asset-type taxonomy (floor) | ≥ 12 | **12** | ISO 14224 | | M10 | Advanced sensor pack share (floor) | ≥ 0.25 | **0.388** | ARC Advisory / OSDU | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** --- ## Suggested use cases - **FFT-based fault classification** — 32-bin FFT spectra include classic rotational harmonic peaks (1x, 2x, 3x, 4x rpm) and fault-specific defect frequencies (6.3x rpm bearing tone, 14x rpm gear-mesh tone, 420 Hz cavitation/surge band). Train CNN-on-spectrogram or 1D-conv classifiers. - **ISO 10816 vibration severity classification** — per-record RMS in mm/s is calibrated to the standard's Class II band, enabling direct alert / alarm / shutdown classifier training without unit conversion. - **3-axis anomaly detection** — X/Y/Z axis decomposition with the classic horizontal-dominant ratio (X > Y > Z) makes this dataset suitable for geometry-aware anomaly models and axis-mixing experiments. - **Crest factor + kurtosis impulsive fault detection** — both metrics are ISO-calibrated and per-record, enabling bearing-fault and gear-mesh detection benchmarking against ISO 13373-1 thresholds. - **Multi-modal sensor fusion** — 6 telemetry modalities (vibration + temperature + pressure + acoustic + lubrication + health) are per-`record_id`-aligned for tight multi-modal experiments. - **Sensor-pack tier ROI** — `sensor_pack` field (basic / standard / advanced / edge_ai) on each asset enables ROI quantification of advanced PdM hardware against detection rate and failure cost. - **Rare-event detection** — `rare_event_flag` in `vibration_labels.csv` flags spike events (vibration ×1.7–3.8 multipliers) calibrated to fault-mode-dependent rates; useful for imbalanced-class ML training. - **RUL regression** — `rul_days` is per-record and calibrated against health index + failure probability; alternative to OIL-039's RUL bucket formulation. --- ## Loading ```python from datasets import load_dataset master = load_dataset( "xpertsystems/oil040-sample", data_files="equipment_master.csv", split="train", ) vibration = load_dataset( "xpertsystems/oil040-sample", data_files="vibration_timeseries.csv", split="train", ) fft = load_dataset( "xpertsystems/oil040-sample", data_files="fft_spectra.csv", split="train", ) labels = load_dataset( "xpertsystems/oil040-sample", data_files="vibration_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/oil040-sample", filename="fft_spectra.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` All 12 tables join on: - `equipment_id` → master ↔ all telemetry ↔ FFT ↔ workorders ↔ failures ↔ labels - `record_id` → tight per-timestamp join across all 6 telemetry modalities + labels + health + RUL - `timestamp` → temporal join across asset/record streams The shared `record_id` makes multi-modal fusion experiments straightforward: join on `record_id` to get every modality at the same instant for the same asset. --- ## Schema highlights **`equipment_master.csv`** — `equipment_id`, `facility_id`, `facility_type` (7-class: upstream / offshore / midstream / refinery / lng / petrochemical / tank_farm), `asset_type` (12-class: centrifugal_pump / reciprocating_compressor / gas_turbine / steam_turbine / electric_motor / gearbox / pipeline_booster / drilling_mud_pump / lng_refrigeration_compressor / refinery_process_pump / blower / offshore_lift_motor), `manufacturer` (6-class), `model`, `install_date`, `age_years`, `criticality` ∈ {1, 2, 3, 4, 5}, `rated_rpm`, `bearing_count`, `sensor_pack` ∈ {basic, standard, advanced, edge_ai}, plus 4 baseline reference values per asset and `primary_fault_mode` (12-class). **`vibration_timeseries.csv`** — `record_id`, `equipment_id`, `timestamp`, `rpm`, `axis_x_mm_s`, `axis_y_mm_s`, `axis_z_mm_s` (horizontal-dominant ISO 18436 convention), `vibration_rms_mm_s` (ISO 10816 unit), `crest_factor` (ISO 17359), `kurtosis` (ISO 13373-1). **`fft_spectra.csv`** — Per-record × 32-bin FFT decomposition (5–1000 Hz linear), each row has `frequency_hz`, `amplitude`, `phase_angle`. Amplitude includes base lognormal noise + rotational harmonic boosts at {1x, 2x, 3x, 4x} rpm/60 + fault-defect frequency boosts: bearing tone at 6.3x rpm (bearing_wear, lubrication_loss), gear-mesh tone at 14x rpm (gear_mesh_wear), and 420 Hz cavitation/surge band. **`vibration_labels.csv`** — `record_id`, `equipment_id`, `timestamp`, `anomaly_label` (binary), `fault_class` (12-class), `severity_level` (5-class: normal / low / medium / high / critical), `rare_event_flag`, `target_failure_30d`. **`maintenance_workorders.csv`** — `maintenance_type` (6-class: inspection / lubrication / bearing_replacement / alignment / sensor_calibration / overhaul), `priority` (4-class: low / medium / high / emergency), `downtime_hours`, `technician_notes_quality` ∈ {complete, partial, missing}. --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Custom HF preview sizing.** The default generator `sample` mode produces ~326 MB (250 assets × 30 days × 24 samples/day × 32 FFT bins = ~1.9M FFT rows). The HF preview is reduced to **80 assets × 30 days × 4 samples/day** to stay under 50 MB while preserving every table, schema, and the scorecard's industry-anchored calibration validity. For higher time-density studies, override sizing with the underlying generator's `--samples-per-day` and `--n-assets` flags, or use the commercial full product. 2. **Anomaly label rate is ~99%.** In `vibration_labels.csv`, the `anomaly_label` (binary) is set to 1 whenever `condition_state != normal`, and the severity-label thresholds combined with the fail_prob distribution put ~99% of records in low/medium/high/critical bands. This is **a labeling-convention artifact, not a positive-class density claim**. For binary anomaly classification work, **use `severity_level` directly** (5-class) or **threshold `failure_probability_30d > 0.70`** to recover a balanced positive class (~5% of records). The full product ships a threshold-tuned binary label variant. 3. **Overheat flag is 0 in the sample.** `temperature_telemetry.csv`'s `overheat_flag` triggers above 115°C, but at the 30-day window most assets don't reach that threshold. For overheat-detection studies, lower the threshold to 95°C in your downstream pipeline, or use the full product's 365-day window which exposes more thermal-overload events. 4. **Only 9 of 12 fault modes appear at sample scale.** With 80 assets and 55% `normal` primary fault, only 9 of the 12 fault modes are represented in any given seed's sample. For full taxonomy coverage, **use multiple seeds and concatenate**, or use the full product (15,000 assets sees all 12 fault modes with statistically representative density). 5. **Small failure-event count.** With 80 assets × 30 days, the sample produces ~5–10 failure events depending on seed. Failure-severity distributions are not reliably estimable at this scale (small-sample variance). **For severity-pyramid analytics**, use OIL-038 (rich failure-event tables) or OIL-039 (sigmoid-calibrated 7d/30d probabilities). 6. **FFT amplitude scale.** FFT amplitudes are in normalized units derived from `vibration_rms_mm_s` × harmonic-boost factors. They are NOT absolute G or m/s². For absolute-unit FFT work, calibrate against the `vibration_rms_mm_s` baseline. 7. **Deterministic seeding.** All 12 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-040** product covers ~15,000 assets × 365 days × 24 samples/day × 96-bin FFT decomposition (~80 million rows total), with threshold-tuned binary anomaly labels, full 12-fault-mode coverage at production scale, and longer-horizon thermal-overload events. 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).