Manufacturing Datasets

Manufacturing datasets are crucial for developing and evaluating advanced machine learning models to improve efficiency, predict failures, and optimize processes in industrial settings. Current research focuses on creating high-fidelity datasets with diverse data modalities (e.g., sensor readings, images) and addressing challenges like limited data availability and imbalanced classes, often employing deep learning architectures such as Transformers, convolutional neural networks (ResNet, InceptionTime), and recurrent neural networks (LSTM, BiLSTM) for time-series classification and anomaly detection. These efforts are significantly impacting the field by enabling the development of more robust and accurate predictive models for various manufacturing tasks, leading to improved process control, reduced downtime, and enhanced productivity.

Papers

July 9, 2024

A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset
Gyeong Taek Lee, Oh-Ran Kwon
Transformer Based Predictive Model Sensor Data Robust Model Statistical Feature Manufacturing Datasets

January 28, 2024

Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform
Ramy Harik, Fadi El Kalach, Jad Samaha, Devon Clark, Drew Sander, Philip Samaha, Liam Burns, Ibrahim Yousif, Victor Gadow, Theodros Tarekegne, Nitol Saha
Time Series Manufacturing Industry Analog Bit Smart Factory Manufacturing Datasets Industry Scale

October 4, 2023

Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
Mojtaba A. Farahani, M. R. McCormick, Ramy Harik, Thorsten Wuest
Machine Learning MAESTRO Dataset Time Series Classification Smart Manufacturing Industrial Task Manufacturing Datasets

June 9, 2023

Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings
Sunny Katyara, Mohammad Mujtahid, Court Edmondson
Transfer Learning High Quality Real World Object Industrial Environment Sim2Real Transfer Sim2Real Gap Manufacturing Datasets

April 18, 2023

A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions
Hamed Khosravi, Taofeeq Olajire, Ahmed Shoyeb Raihan, Imtiaz Ahmed
Raw Data Multi Objective Sequential Learning Manufacturing Process Manufacturing Datasets

June 13, 2022

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets
Mustafa Abdallah, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi
Anomaly Detection Smart Manufacturing Failure Prediction Cross Sensor Manufacturing Datasets

February 10, 2022

Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
Kyeong-Joong Jeong, Jin-Duk Park, Kyusoon Hwang, Seong-Lyun Kim, Won-Yong Shin
Time Series Deep Anomaly Detection Unveiling Hidden Energy Anomaly Manufacturing Datasets Data Driven Anomaly Detection

Manufacturing Datasets

Papers

A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset

Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform

Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms

Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings

A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets

Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data