Skip to main content
GitHub

3D Printer Fault Detection (IoT Sensors)

Industrial IoT
124 views
1 min read
License

Abstract

"Sensor logs (accelerometer, temperature) from a 3D printer capturing normal operation vs. various failure modes like nozzle clogging."

Description

Dataset Summary

Data collected from an Ultimaker 3D printer equipped with IoT sensors to detect failures in real-time.

Sensors

  • Accelerometer: Attached to the print head (X, Y, Z vibration).
  • Temperature: Nozzle and Bed temperature readings.

Fault Types

Includes labeled data for: Layer shifting, Nozzle clogging, and Filament runout.

Data Preview

Time_SecAcc_XAcc_YAcc_ZTemp_NozzleLabel
0.010.04-0.121.02200.5Normal
0.020.05-0.111.01200.4Normal

Showing first few rows for preview

Cite This Dataset 

GitHub (2026). 3D Printer Fault Detection (IoT Sensors). [Dataset]. GitHub. https://github.com/topics/3d-printing-data

Source: GitHub (2026)

Indexed by IoTDataset.com on Jan 12, 2026

Ready to Start Your Research?

Download this dataset directly from the official repository and start building your next breakthrough project.

Download Dataset

Related Topics & Keywords

#3d printing #fault detection #vibration #accelerometer #temperature #additive manufacturing

Share This Research

More in Industrial IoT

View All
Industrial IoT Government

NASA C-MAPSS Turbofan Engine Degradation — 4 Sub-datasets, 21 Sensors [Run-to-Failure]

NASA Prognostics Center run-to-failure simulation dataset for turbofan engines. Four operational sub-datasets with 21 sensor channels and 3 operational settings. TXT/CSV format. Primary benchmark for Remaining Useful Life (RUL) estimation.

Apr 10, 2026
Industrial IoT University

CWRU Bearing Fault Dataset — 2HP Motor Vibration, 4 Fault Diameters [12k & 48k Hz]

Benchmark bearing vibration dataset from Case Western Reserve University with drive-end and fan-end faults at 4 severity levels. Sampled at 12 kHz and 48 kHz. MATLAB MAT and CSV formats. Used for fault diagnosis and vibration-based condition monitoring.

Apr 10, 2026
Industrial IoT UCI

AI4I 2020 Predictive Maintenance — Milling Machine Sensor Failures [10,000 Records]

Synthetic IIoT dataset reflecting real milling machine predictive maintenance scenarios. 10,000 records with 14 features including air temperature, process temperature, rotational speed, torque, and 5 labeled failure types. CSV format. Ideal for multi-label fault classification.

Apr 10, 2026
Industrial IoT Kaggle

Bosch Production Line Performance — Assembly Line Fault Detection [1.18M Parts]

One of Kaggle's largest IIoT manufacturing datasets with 1.18 million parts measured across Bosch's assembly lines. Thousands of anonymized sensor features split across numeric, categorical, and date files. CSV format. Used for quality control and failure prediction.

Apr 10, 2026