File size: 2,815 Bytes
3c035eb cddc07f 3c035eb cddc07f a991eb0 cddc07f a991eb0 cddc07f a991eb0 cddc07f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
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
``` |