---
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
```