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MGG_001_failure_schema.json

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+{
+  "asset_id": "str",
+  "failure_timestamp": "str",
+  "fault_mode": "str",
+  "fault_onset_timestamp": "str",
+  "days_from_onset_to_failure": "float64",
+  "asset_type": "str",
+  "criticality_class": "str"
+}

MGG_001_registry_schema.json

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+{
+  "asset_id": "str",
+  "plant_id": "str",
+  "line_id": "str",
+  "cell_id": "str",
+  "asset_type": "str",
+  "manufacturer": "str",
+  "model_number": "str",
+  "serial_number": "str",
+  "installation_date": "str",
+  "asset_age_years": "float64",
+  "design_life_years": "int64",
+  "life_consumed_pct": "float64",
+  "criticality_class": "str",
+  "iso_protection_class": "str",
+  "atex_zone": "str",
+  "last_maintenance_date": "str",
+  "maintenance_interval_days": "int64",
+  "days_since_maintenance": "int64",
+  "cumulative_operating_hours": "float64",
+  "nominal_speed_rpm": "float64",
+  "rated_power_kw": "float64",
+  "bearing_model": "str",
+  "lubricant_type": "str",
+  "protocol": "str",
+  "gateway_id": "str"
+}

MGG_001_sensor_schema.json

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+{
+  "asset_id": "str",
+  "plant_id": "str",
+  "line_id": "str",
+  "cell_id": "str",
+  "asset_type": "str",
+  "observation_timestamp": "str",
+  "shift_id": "str",
+  "observation_frequency_hz": "float64",
+  "temp_bearing_drive_end_c": "float64",
+  "temp_bearing_non_drive_end_c": "float64",
+  "temp_motor_winding_c": "float64",
+  "temp_coolant_inlet_c": "float64",
+  "temp_coolant_outlet_c": "float64",
+  "temp_ambient_c": "float64",
+  "temp_delta_bearing_c": "float64",
+  "thermal_gradient_c_per_hr": "float64",
+  "vib_overall_rms_mm_s": "float64",
+  "vib_peak_mm_s": "float64",
+  "vib_crest_factor": "float64",
+  "vib_kurtosis": "float64",
+  "vib_1x_rpm_amplitude": "float64",
+  "vib_2x_rpm_amplitude": "float64",
+  "vib_bpfo_amplitude": "float64",
+  "vib_bpfi_amplitude": "float64",
+  "vib_bsf_amplitude": "float64",
+  "vib_ftf_amplitude": "float64",
+  "vib_high_frequency_db": "float64",
+  "vib_axial_rms_mm_s": "float64",
+  "vib_iso10816_severity_zone": "str",
+  "pressure_inlet_bar": "float64",
+  "pressure_outlet_bar": "float64",
+  "pressure_differential_bar": "float64",
+  "pressure_lube_oil_bar": "float64",
+  "pressure_pulsation_bar_pp": "float64",
+  "flow_rate_m3_hr": "float64",
+  "npsh_available_m": "float64",
+  "cavitation_index": "float64",
+  "current_phase_a_amps": "float64",
+  "current_phase_b_amps": "float64",
+  "current_phase_c_amps": "float64",
+  "current_imbalance_pct": "float64",
+  "voltage_line_to_line_v": "float64",
+  "power_factor": "float64",
+  "active_power_kw": "float64",
+  "reactive_power_kvar": "float64",
+  "apparent_power_kva": "float64",
+  "motor_load_pct": "float64",
+  "thd_voltage_pct": "float64",
+  "thd_current_pct": "float64",
+  "insulation_resistance_mohm": "float64",
+  "mcsa_sideband_db": "float64",
+  "operating_speed_rpm": "float64",
+  "motor_load_pct_operational": "float64",
+  "efficiency_pct": "float64",
+  "oee_availability_pct": "float64",
+  "oee_performance_pct": "float64",
+  "oee_quality_pct": "float64",
+  "oee_overall_pct": "float64",
+  "oil_viscosity_cst": "float64",
+  "oil_contamination_ntu": "float64",
+  "oil_particle_count_iso4406": "str",
+  "hours_since_lubrication": "float64",
+  "health_index": "float64",
+  "rul_predicted_hours": "float64",
+  "rul_confidence_interval_pct": "int64",
+  "failure_mode_active": "str",
+  "failure_mode_severity": "str",
+  "anomaly_label": "int64",
+  "fault_probability_pct": "float64",
+  "alarm_level": "str",
+  "alert_triggered_flag": "bool",
+  "alarm_triggered_flag": "bool",
+  "shutdown_triggered_flag": "bool",
+  "maintenance_recommendation": "str",
+  "sensor_data_quality": "str",
+  "protocol": "str",
+  "gateway_id": "str",
+  "facility_temp_c": "float64",
+  "facility_humidity_rh_pct": "float64"
+}

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - manufacturing
+  - industrial-iot
+  - predictive-maintenance
+  - condition-monitoring
+  - iso-10816
+  - iso-13373
+  - iso-14224
+  - iso-17359
+  - oreda
+  - vibration-analysis
+  - bearing-fault-detection
+  - bpfo
+  - bpfi
+  - mcsa
+  - motor-current-signature-analysis
+  - rul
+  - remaining-useful-life
+  - oee
+  - sensor-fusion
+  - anomaly-detection
+  - smart-factory
+  - industrie-40
+  - centrifugal-pump
+  - induction-motor
+  - cnc
+pretty_name: "MGG-001 — Factory Sensor Dataset (Sample)"
+size_categories:
+  - 10K<n<100K
+---
+
+# MGG-001 — Factory Sensor Dataset (Sample)
+
+A schema-identical preview of **MGG-001**, the XpertSystems.ai synthetic
+**factory IoT sensor cohort** dataset for predictive maintenance ML,
+condition monitoring research, bearing fault detection, motor current
+signature analysis, RUL (Remaining Useful Life) prediction, and
+Industrie 4.0 manufacturing analytics. The full product covers 200
+assets × 90 days × 15-min cadence (~17M sensor observations). This
+sample is HF-sized at 12 assets × 45 days × 1-hour cadence.
+
+> **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 MGG-001 does — and how it opens a new XpertSystems vertical
+
+MGG-001 is the **first Manufacturing & Industrial Systems SKU** in the
+XpertSystems catalog, complementing our existing Oil & Gas vertical (17
+SKUs) and Healthcare/Neurology vertical (10 SKUs). Where Oil & Gas
+targets upstream/midstream operators and Healthcare targets pharma R&D,
+**Manufacturing targets a different industrial buyer category**:
+discrete manufacturers, plant maintenance teams, industrial IoT
+platforms, and the AI-for-manufacturing ecosystem.
+
+| Vertical | SKUs | Primary Audience |
+|---|---|---|
+| Oil & Gas | 17 | Upstream/midstream operators, ISO 14224 / API 689 / OREDA users |
+| Healthcare/Neurology | 10 | Pharma R&D, clinical trial design, biomarker validation |
+| **Manufacturing** | **1+ (this is MGG-001)** | **Plant maintenance, MES/CMMS vendors, AI-for-manufacturing, Industrie 4.0** |
+
+The dataset captures **physics-based, temporally correlated** sensor
+streams from 8 industrial equipment types covering the full Industrial
+IoT (IIoT) sensor stack: temperature, vibration (with ISO 10816 zone
+classification + bearing fault frequencies BPFO/BPFI/BSF/FTF),
+pressure, flow, electrical (3-phase + power quality), process,
+oil/lubricant, and health/RUL indicators.
+
+| Buyer Persona | Use Case |
+|---|---|
+| Predictive Maintenance Platform | Sensor fusion + anomaly detection ML |
+| CMMS / EAM Vendors | Failure mode + maintenance recommendation ML |
+| Industrial IoT Platforms | Multi-protocol (MQTT/OPC-UA/Modbus) data modeling |
+| Industrie 4.0 Researchers | Digital twin training data |
+| Vibration Analysis Specialists | ISO 10816 zone classification + BPFO/BPFI ML |
+| Motor Current Signature Analysis | MCSA sideband + broken rotor bar detection |
+| RUL Prediction Researchers | Weibull-degradation + sensor trajectory ML |
+| Bearing Manufacturers (SKF, Schaeffler) | Bearing fault progression simulation |
+| Smart Manufacturing Analytics | OEE + availability + performance + quality ML |
+
+---
+
+## What's inside — three related CSV files
+
+MGG-001 is a **multi-table relational** dataset (similar pattern to
+HC-NEU-004 Multiple Sclerosis). Three CSV files share `asset_id` as
+join key.
+
+| File | Rows (sample) | Columns | Size |
+|---|---:|---:|---|
+| `mgg001_equipment_registry.csv` | 12 | 25 | ~3 KB |
+| `mgg001_sensor_data.csv` | 12,960 | 79 | ~6.7 MB |
+| `mgg001_failure_events.csv` | 0–3 | 7 | ~250 B |
+
+Schemas are provided in three matching JSON files:
+- `MGG_001_registry_schema.json`
+- `MGG_001_sensor_schema.json`
+- `MGG_001_failure_schema.json`
+
+### Sensor schema module structure (79 columns total)
+
+| Module | Cols | Sensors |
+|---|---:|---|
+| Identification | 8 | asset_id, plant_id, line_id, cell_id, asset_type, timestamp, shift_id, frequency_hz |
+| Temperature | 8 | bearing DE, bearing NDE, motor winding, coolant in/out, ambient, delta, thermal gradient |
+| Vibration | 13 | overall RMS, peak, crest factor, kurtosis, 1× RPM, 2× RPM, BPFO, BPFI, BSF, FTF, high-freq dB, axial RMS, ISO 10816 zone |
+| Pressure | 5 | inlet, outlet, differential, lube oil, pulsation pp |
+| Flow | 3 | flow rate, NPSH, cavitation index |
+| Electrical | 12 | 3 phase currents, imbalance %, line voltage, PF, P/Q/S, motor load, THD V + I, insulation, MCSA |
+| Process | 6 | operating RPM, motor load, efficiency, OEE A/P/Q + overall |
+| Oil | 4 | viscosity, contamination, ISO 4406 code, hrs since lube |
+| Health & RUL | 6 | health index, RUL hours, RUL CI%, fault mode, severity, anomaly label |
+| Alarms | 6 | fault probability, alarm level, alert/alarm/shutdown flags, maintenance rec, sensor quality |
+| Network | 4 | protocol, gateway_id, facility temp, facility humidity |
+
+---
+
+## Calibration sources
+
+Every distribution is anchored to **named international standards** or
+industry benchmarks. The headline anchors are **ISO 10816** (mechanical
+vibration evaluation), **ISO 14224** (reliability/maintenance data
+collection), and **OREDA-2015** (Offshore Reliability Data — MTBF
+distributions). Other anchors:
+
+- **ISO 10816-3 / ISO 10816-7** — vibration severity zones A/B/C/D for
+  Group 1-4 machines.
+- **ISO 13373** — condition monitoring and diagnostics of machines.
+- **ISO 17359** — condition monitoring general guidelines.
+- **ISO 14224** — petroleum, petrochemical, and natural gas industries:
+  collection and exchange of reliability and maintenance data for
+  equipment.
+- **OREDA-2015** — Offshore and Onshore Reliability Data Handbook (5th
+  edition); MTBF, failure mode, and severity distributions for
+  industrial rotating equipment.
+- **NEMA MG-1** — Motors and Generators standard; 3-phase current
+  imbalance limits, motor insulation classes (B/F/H), de-rating.
+- **IEEE 519** — Recommended Practices and Requirements for Harmonic
+  Control in Electrical Power Systems.
+- **IEEE 117 + IEC 60034-1** — motor electrical insulation system
+  temperature classifications.
+- **API 610** — Centrifugal Pumps for Petroleum, Petrochemical and
+  Natural Gas Industries.
+- **API 619** — Rotary-Type Positive-Displacement Compressors.
+- **ISO 4406** — hydraulic fluid power cleanliness code (three-number
+  particle counts).
+- **ISA-18.2 + EEMUA 191 + IEC 62682** — Management of Alarm Systems
+  for the Process Industries.
+- **Nakajima 1988 + SME Industry Benchmarks** — OEE (Overall Equipment
+  Effectiveness) framework and real-world benchmarks.
+- **SKF Bearing Manual + ISO 281** — bearing geometry constants (BPFO,
+  BPFI, BSF, FTF) for 6205, 6305, 6206, NU205 bearings.
+- **Pump Affinity Laws** — Q ∝ N, H ∝ N², P ∝ N³ scaling relationships.
+
+---
+
+## Validation scorecard
+
+The wrapper ships a 10-metric ISO/OREDA/NEMA-anchored scorecard
+(`validation_scorecard.json`) that re-scores the dataset on every
+generation. Default seed 42 result:
+
+| ID | Metric | Target | Observed | Source |
+|---|---|---|---:|---|
+| M01 | ISO 10816 Zone A Share | 0.47–0.97 | **0.707** | **ISO 10816-3/7** |
+| M02 | Bearing DE Temp Mean (°C) | 50–80 | **63.0** | **ISO 14224 / SKF / API 610** |
+| M03 | Motor Winding Temp (°C, CEILING ≤180) | ≤180 | **96.1** | **IEEE 117 / NEMA MG-1 Class F** |
+| M04 | Current Imbalance (%, CEILING ≤10) | ≤10 | **2.87** | **NEMA MG-1** |
+| M05 | Power Factor Mean | 0.79–0.95 | **0.873** | IEEE 519 / utility tariffs |
+| M06 | Motor Load (% of rated) | 55–85 | **65.4** | NEMA MG-1 / IEEE 739 |
+| M07 | OEE Overall (FLOOR ≥40%) | ≥40 | **67.8** | **Nakajima 1988 / SME** |
+| M08 | Alarm Normal Share (FLOOR ≥65%) | ≥65 | **0.806** | **ISA-18.2 / EEMUA 191** |
+| M09 | Critical Fault Tail | 0.00–0.06 | **0.014** | **ISO 13373 / Weibull** |
+| M10 | Shutdown Alarm Rate (CEILING ≤5.5%) | ≤5.5 | **1.36** | **EEMUA 191 / IEC 62682** |
+
+**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**
+
+**Standout calibration**: M05 power factor lands at **0.873 vs target
+0.87 — 0.003 deviation** 🎯. M08 alarm normal share at 80.6% directly
+matches ISA-18.2's "≥80% normal-state" alarm engineering best practice.
+M04 current imbalance at 2.87% is **comfortably under** NEMA MG-1's 10%
+de-rating threshold (well-balanced plant). M09 critical fault tail at
+1.4% reflects effective predictive maintenance catching incipient/minor
+faults before critical.
+
+---
+
+## Suggested use cases
+
+- **Bearing fault detection ML** — BPFO/BPFI/BSF/FTF amplitude bands +
+  envelope analysis × ground-truth fault mode for bearing diagnostic
+  ML training.
+- **ISO 10816 zone classification ML** — vib RMS + crest factor +
+  kurtosis × A/B/C/D zone prediction.
+- **RUL (Remaining Useful Life) prediction** — sensor trajectory ×
+  Weibull degradation × health index for prognostic ML.
+- **Motor Current Signature Analysis (MCSA)** — MCSA sideband + THD +
+  current imbalance × broken rotor bar / electrical stator fault
+  detection.
+- **Cavitation detection** — flow + NPSH + cavitation index + pressure
+  pulsation × cavitation event classification for pump diagnostics.
+- **Anomaly detection benchmarks** — labeled anomaly flag + multi-sensor
+  fusion for unsupervised + supervised anomaly detection comparison.
+- **OEE analytics** — A/P/Q decomposition × equipment type ×
+  shift/criticality for manufacturing efficiency ML.
+- **Multi-protocol IIoT data modeling** — MQTT/OPC-UA/Modbus/Profibus/
+  HART/IO-Link/EtherNet-IP × protocol-specific data quality patterns.
+- **Maintenance recommendation engine** — sensor state + fault severity
+  × maintenance action prediction (none / monitor / inspection /
+  replacement / shutdown).
+- **Digital twin training data** — physics-based generation matches
+  thermal response (first-order exponential), pump affinity laws
+  (Q∝N, H∝N², P∝N³), and bearing fault frequencies for digital twin
+  validation.
+- **Bearing manufacturer R&D** — SKF / Schaeffler / NSK / Timken bearing
+  fault progression modeling for product development.
+
+---
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+registry = load_dataset(
+    "xpertsystems/mgg001-sample",
+    data_files="mgg001_equipment_registry.csv",
+    split="train",
+)
+sensor = load_dataset(
+    "xpertsystems/mgg001-sample",
+    data_files="mgg001_sensor_data.csv",
+    split="train",
+)
+failures = load_dataset(
+    "xpertsystems/mgg001-sample",
+    data_files="mgg001_failure_events.csv",
+    split="train",
+)
+```
+
+Or with pandas directly:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+reg_path = hf_hub_download(
+    repo_id="xpertsystems/mgg001-sample",
+    filename="mgg001_equipment_registry.csv",
+    repo_type="dataset",
+)
+sensor_path = hf_hub_download(
+    repo_id="xpertsystems/mgg001-sample",
+    filename="mgg001_sensor_data.csv",
+    repo_type="dataset",
+)
+registry = pd.read_csv(reg_path)
+sensor = pd.read_csv(sensor_path)
+
+# Join sensor data with asset master data
+full = sensor.merge(registry, on="asset_id", suffixes=("", "_registry"))
+
+# Per-asset trajectory analysis
+for asset_id, sub in sensor.groupby("asset_id"):
+    sub = sub.sort_values("observation_timestamp")
+    # ... fit degradation trajectory, RUL forecast
+```
+
+Three schema JSON files are bundled for pipeline integration:
+
+```python
+import json
+schema_sensor = json.load(open("MGG_001_sensor_schema.json"))
+schema_registry = json.load(open("MGG_001_registry_schema.json"))
+schema_failure = json.load(open("MGG_001_failure_schema.json"))
+```
+
+This dataset is **multi-table relational** — different from most other
+XpertSystems HC/OIL SKUs which use single-table architecture. The
+sensor stream is **longitudinal time-series** (12,960 hourly
+observations across 12 assets × 45 days) while the registry is
+**cross-sectional master data** (one row per asset) and the failure
+event log is **event-stream** (sparse, one row per critical failure).
+
+---
+
+## Schema highlights
+
+### mgg001_equipment_registry.csv (25 columns)
+
+**Identification & location** — `asset_id`, `plant_id`, `line_id`,
+`cell_id`, `asset_type` ∈ {pump_centrifugal, motor_induction,
+compressor_screw, compressor_reciprocating, cnc_machining_center,
+conveyor_belt, gearbox, fan_industrial}.
+
+**Manufacturer & model** — `manufacturer` ∈ {Siemens, ABB, Grundfos,
+Atlas Copco, Fanuc, SKF, Emerson, Schneider Electric, WEG, Mitsubishi
+Electric, Sulzer, KSB, Bosch Rexroth, Parker Hannifin, Nidec},
+`model_number`, `serial_number`, `installation_date`, `asset_age_years`,
+`design_life_years`, `life_consumed_pct`.
+
+**Operational metadata** — `criticality_class` ∈ {critical_production,
+important_production, general_purpose, auxiliary, standby},
+`iso_protection_class` (IP rating), `atex_zone` (ATEX hazard zone).
+
+**Maintenance** — `last_maintenance_date`, `maintenance_interval_days`,
+`days_since_maintenance`, `cumulative_operating_hours`.
+
+**Specifications** — `nominal_speed_rpm`, `rated_power_kw`,
+`bearing_model` ∈ {6205, 6305, 6206, NU205}, `lubricant_type`.
+
+**Networking** — `protocol` ∈ {MQTT, OPC_UA, Modbus_TCP, Profibus,
+HART, IO_Link, EtherNet_IP}, `gateway_id`.
+
+### mgg001_sensor_data.csv (79 columns)
+
+See README "Sensor schema module structure" table above for all 79
+columns organized by module. Key columns:
+
+- `anomaly_label` (binary 0/1, ground truth)
+- `failure_mode_active` ∈ 16 fault modes + None
+- `failure_mode_severity` ∈ {none, incipient, minor, moderate, severe, critical}
+- `vib_iso10816_severity_zone` ∈ {A_new, B_acceptable, C_alarm, D_danger}
+- `alarm_level` ∈ {normal, alert, alarm, danger, shutdown}
+- `maintenance_recommendation` ∈ {none, monitor_increase_frequency,
+  schedule_inspection, schedule_replacement, immediate_shutdown}
+- `oil_particle_count_iso4406` (3-number ISO 4406 cleanliness code,
+  e.g., "17/15/12")
+
+### mgg001_failure_events.csv (7 columns)
+
+`asset_id`, `failure_timestamp`, `fault_mode`, `fault_onset_timestamp`,
+`days_from_onset_to_failure`, `asset_type`, `criticality_class`.
+
+---
+
+## Calibration notes & limitations
+
+In the spirit of honest synthetic data, a few things buyers of the sample
+should know:
+
+1. **Failure event count is intentionally sparse** in the HF preview
+   sample. At seed 42, the 12-asset × 45-day window produces 1 failure
+   event. This reflects **real-world MTBF (OREDA-2015)** — even with
+   elevated failure_rate=0.20 parameter, the time between asset onset
+   and critical failure is typically 30-60 days. At full scale (200
+   assets × 90 days × 15-min cadence), the product produces ~10-30
+   failure events per generation. For demonstration purposes, the
+   sparse event count is consistent with real-world predictive
+   maintenance datasets where critical failures are rare events.
+
+2. **Anomaly rate 18.5% (seed 42) is above the target failure_rate=0.20
+   parameter**. The `failure_rate` parameter controls per-asset
+   probability of *having* a fault state during the observation window;
+   the observed `anomaly_label` rate aggregates faulty-asset
+   observation counts. For larger-window simulation, the two converge.
+
+3. **Asset type weights deviate from configured targets at n=12**
+   (small-sample variance). At full scale (200 assets), the
+   distribution closely matches CONFIG weights (pump_centrifugal 25%,
+   motor_induction 20%, compressor_screw 15%, etc.).
+
+4. **The sensor stream uses 1-hour cadence** in this sample. The full
+   product supports 1-min / 5-min / 15-min / 1-hr sampling — and for
+   high-frequency vibration FFT analysis, 1-min cadence is required.
+   For ML training, 15-min cadence is typically sufficient.
+
+5. **Bearing fault frequencies (BPFO, BPFI, BSF, FTF) are calculated
+   correctly** for the 4 supported bearing models (6205, 6305, 6206,
+   NU205) using ISO 281 geometry constants × shaft frequency. For
+   bearings outside these 4 types, the full product supports custom
+   bearing geometry input.
+
+6. **OEE quality component 90.94% is elevated** above world-class >99%
+   benchmarks. The generator's quality model is conservative; for
+   process-industry quality modeling, the full product calibrates per
+   industry-specific yield rates.
+
+7. **Pump affinity law scaling** (Q ∝ N, H ∝ N², P ∝ N³) is applied to
+   pump_centrifugal asset types only. For positive-displacement
+   compressors, different scaling applies (constant volumetric output
+   below cavitation).
+
+8. **Thermal response model** uses first-order exponential (single time
+   constant τ ~2 hr). More complex thermal models with multiple time
+   constants (bearing + winding + frame) are available in the full
+   product.
+
+9. **Multi-protocol IIoT** is represented as a metadata field
+   (`protocol`); protocol-specific data quality patterns (packet loss,
+   latency, dropouts) are not simulated in this preview. For
+   protocol-aware data quality modeling, the full product includes
+   protocol-specific error injection.
+
+10. **Deterministic seeding.** Wrapper invokes the generator via
+    subprocess with explicit `--seed` parameter; the generator's
+    `np.random.default_rng(seed)` and `random.seed(seed)` ensure full
+    reproducibility. Seed sweep verifies Grade A+ across {42, 7, 123,
+    2024, 99, 1}.
+
+---
+
+## Commercial / full product
+
+The full **MGG-001** product covers 200 assets × 90 days × configurable
+sampling cadence (1-min to 1-hr) producing ~17M sensor observations
+with refined failure event density (10-30 events per generation),
+configurable cohort enrichment (high-failure / low-failure / balanced),
+protocol-specific data quality patterns (MQTT broker disconnects,
+OPC-UA session timeouts, Modbus polling drops), multi-bearing
+configurations beyond the 4 default types, custom asset_type
+extensions (compressors, agitators, mixers, crushers, robots), refined
+OEE quality model per industry vertical (discrete vs process vs
+hybrid), and pre-built feature engineering pipelines for time-series ML
+(rolling statistics, FFT decomposition, envelope analysis, MCSA
+spectral features). Available under commercial license — contact
+[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).
+
+XpertSystems.ai also publishes synthetic data products across **Oil &
+Gas** (17 SKUs, OREDA/ISO 14224/API/IPIECA standards) and
+**Healthcare/Neurology** (10 SKUs, ENROLL-HD/PRO-ACT/TRACK-HD/CLARITY-AD
+clinical trial calibration). Catalog:
+[huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).

mgg001_equipment_registry.csv

@@ -0,0 +1,13 @@
+asset_id,plant_id,line_id,cell_id,asset_type,manufacturer,model_number,serial_number,installation_date,asset_age_years,design_life_years,life_consumed_pct,criticality_class,iso_protection_class,atex_zone,last_maintenance_date,maintenance_interval_days,days_since_maintenance,cumulative_operating_hours,nominal_speed_rpm,rated_power_kw,bearing_model,lubricant_type,protocol,gateway_id
+ASSET-CNC-0001,PLANT-008,LINE-E,CELL-014,cnc_machining_center,KSB,SCH-2152-84,SN-20200127-50534,2020-01-27,6.21,15,41.4,important_production,IP54,zone_22,2026-02-06,90,62,46448.0,1583.0,97.7,NU205,oil_mineral_iso_vg46,IO_Link,GW-110
+ASSET-PUM-0002,PLANT-008,LINE-E,CELL-007,pump_centrifugal,ABB,NID-5010-89,SN-20180726-71012,2018-07-26,7.71,15,51.4,critical_production,IP66,non_hazardous,2026-04-04,90,5,45887.0,2691.0,317.7,6205,oil_mineral_iso_vg46,Profibus,GW-031
+ASSET-CNC-0003,PLANT-008,LINE-B,CELL-009,cnc_machining_center,ABB,SUL-5280-39,SN-20160205-30421,2016-02-05,10.18,15,67.9,general_purpose,IP67,non_hazardous,2026-03-21,60,19,81124.0,4760.0,49.6,NU205,oil_mineral_iso_vg100,HART,GW-020
+ASSET-CON-0004,PLANT-002,LINE-E,CELL-004,conveyor_belt,Bosch Rexroth,SIE-8170-80,SN-20150426-80232,2015-04-26,10.96,15,73.1,general_purpose,IP66,non_hazardous,2026-03-17,60,23,76871.0,122.0,136.8,6206,oil_mineral_iso_vg220,Profibus,GW-114
+ASSET-PUM-0005,PLANT-006,LINE-D,CELL-011,pump_centrifugal,WEG,ABB-6032-80,SN-20240414-37355,2024-04-14,1.99,15,13.3,critical_production,IP55,non_hazardous,2026-02-26,90,42,12706.0,2298.0,383.7,6206,grease_polyurea,Modbus_TCP,GW-199
+ASSET-PUM-0006,PLANT-006,LINE-D,CELL-015,pump_centrifugal,Nidec,MIT-4682-46,SN-20250105-47615,2025-01-05,1.26,15,8.4,critical_production,IP67,non_hazardous,2026-02-18,365,50,7636.0,1890.0,332.6,6205,grease_lithium,IO_Link,GW-145
+ASSET-COM-0007,PLANT-002,LINE-C,CELL-005,compressor_screw,Fanuc,SUL-6671-64,SN-20221214-42562,2022-12-14,3.32,20,16.6,critical_production,IP55,non_hazardous,2026-03-13,365,27,27567.0,2962.0,186.8,6206,oil_mineral_iso_vg46,EtherNet_IP,GW-100
+ASSET-MOT-0008,PLANT-003,LINE-A,CELL-002,motor_induction,Emerson,PAR-2148-50,SN-20081027-73362,2008-10-27,17.46,20,87.3,important_production,IP55,zone_21,2025-11-20,180,140,109856.0,2363.0,899.8,6206,oil_mineral_iso_vg100,Profibus,GW-172
+ASSET-CNC-0009,PLANT-007,LINE-A,CELL-015,cnc_machining_center,Emerson,KSB-1374-25,SN-20160604-54458,2016-06-04,9.85,15,65.7,important_production,IP66,non_hazardous,2025-12-18,180,112,60145.0,7217.0,66.7,6305,grease_polyurea,HART,GW-159
+ASSET-GEA-0010,PLANT-010,LINE-C,CELL-010,gearbox,Schneider Electric,KSB-1584-17,SN-20150811-34825,2015-08-11,10.67,20,53.3,important_production,IP65,non_hazardous,2026-03-30,30,10,77889.0,1814.0,123.8,6205,oil_synthetic_pao,Profibus,GW-095
+ASSET-MOT-0011,PLANT-001,LINE-F,CELL-011,motor_induction,Grundfos,FAN-5985-38,SN-20191222-19771,2019-12-22,6.3,20,31.5,general_purpose,IP54,non_hazardous,2026-03-01,90,39,47425.0,799.0,1652.9,6206,oil_mineral_iso_vg220,IO_Link,GW-188
+ASSET-PUM-0012,PLANT-004,LINE-B,CELL-016,pump_centrifugal,Emerson,BOS-9667-38,SN-20131115-28246,2013-11-15,12.41,15,82.7,important_production,IP67,zone_1,2026-02-27,180,41,80578.0,1506.0,279.7,6206,oil_mineral_iso_vg100,EtherNet_IP,GW-188

mgg001_failure_events.csv

@@ -0,0 +1,2 @@
+asset_id,failure_timestamp,fault_mode,fault_onset_timestamp,days_from_onset_to_failure,asset_type,criticality_class
+ASSET-CNC-0009,2026-05-17T07:14:38,mechanical_looseness,2026-04-19T07:14:38,28.0,cnc_machining_center,important_production