BhinnekaAI/IMC-Sensor-Telemetry

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