Industrial Prognostic

Industrial prognostics aims to predict the remaining useful life of industrial assets by analyzing sensor data and other relevant information, enabling proactive maintenance and preventing costly failures. Current research emphasizes advanced machine learning techniques, including deep learning models (e.g., recurrent neural networks) and federated learning approaches to handle diverse and potentially private data from multiple sources, often incorporating contextual information to improve prediction accuracy. These advancements are crucial for optimizing industrial operations, reducing downtime, and improving overall efficiency and safety across various sectors.

Papers

October 14, 2024

A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals
Yuqi Su, Xiaolei Fang
Federated Learning Prognostic Prediction Prognostic Model Degradation Representation Dimensional Signal Industrial Prognostic

May 9, 2024

Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
Yuqi Su, Xiaolei Fang
Deep Learning Prognostic Model Failure Analysis Individual Production Asset Full Degradation Industrial Prognostic

December 6, 2023

Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management
Huan Wang, Yan-Fu Li, Min Xie
Language Model Health Management ChatGPT Like LLM Industrial Prognostic

May 13, 2023

A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
Anushiya Arunan, Yan Qin, Xiaoli Li, Chau Yuen
Federated Learning Feature Matching Prognostic Model Industrial Prognostic

January 12, 2023

Explicit Context Integrated Recurrent Neural Network for Sensor Data Applications
Rashmi Dutta Baruah, Mario Muñoz Organero
Recurrent Neural Network Sensory Data Context Neural Network Industrial Prognostic

Industrial Prognostic

Papers

A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals

Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes

Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management

A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction

Explicit Context Integrated Recurrent Neural Network for Sensor Data Applications