metadata
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
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