---
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).