| --- |
| license: cc-by-4.0 |
| author: Christopher Alberto Hamonangan |
| --- |
| |
| # Overview |
|
|
| IMC-Sensor-Telemetry evaluates methods for detecting mechanical failures and |
| thermodynamic resonance anomalies in smart industrial machinery using |
| multi-sensor IoT telemetry. |
|
|
| ## Dataset information |
|
|
| | Property | Description | |
| | --------------------------- | ---------------------------------------------------------- | |
| | Dataset name | IMC-Sensor-Telemetry | |
| | Full name | Industrial Machinery Condition Monitoring | |
| | Publisher | BhinnekaAI | |
| | Dataset Creator | Christopher Alberto Hamonangan | |
| | Domain | Smart manufacturing and industrial IoT | |
| | Data modality | Structured numerical tabular data | |
| | Feature count | 5 numerical features | |
| | Data type | Synthetic | |
| | Task | Machinery failure and anomaly detection | |
| | Expected anomaly proportion | Approximately 13.0% | |
| | Label convention | `0` = normal operation; `1` = impending mechanical failure | |
|
|
| # Dataset Structure |
|
|
| ## random-seed |
|
|
| - Directory: random-seed |
| - Files: 5 files, 5,000 observations each. |
|
|
| ## seed42 |
|
|
| - Directory: seed42 |
| - Files: 5 files with sizes 100, 1,000, 10,000, 50,000, and 100,000. |
|
|
| # Feature Specification |
|
|
| | Feature | Description | Data Type | |
| | ------------------- | ----------------------------------- | ------------------------ | |
| | `machine_temp` | Internal operating temperature (°C) | Continuous | |
| | `spindle_vibration` | Spindle vibration amplitude (mm/s) | Continuous (Exponential) | |
| | `current_draw` | Electric motor current draw (Amps) | Continuous | |
| | `oil_pressure` | Lubrication oil pressure (bar) | Continuous | |
| | `motor_rpm` | Motor rotational speed (RPM) | Continuous | |
|
|
| # Mathematical Formulation |
|
|
| Normal machine temperature is governed by thermodynamic relationships tied to |
| rotational speed: |
|
|
| machine_temp = 0.02 * motor_rpm + 15 + N(0, 1.5) |
|
|
| Anomalies represent phase shifts or thermodynamic breakdowns where temperature |
| spikes independently of motor speed. |
|
|
| # Loading the Dataset |
|
|
| ```python |
| import pandas as pd |
| |
| df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv") |
| X = df.iloc[:, :-1].values |
| y = df['is_machine_failure'].values |
| ``` |
