Cloud-Enabled Machines with Data-Driven Intelligence
Abstract
The advances in cloud computing, Internet of Things (IoT), cyber-physical systems (CPS), and artificial intelligence automatic have the potential to enable fault and failure detection, self-diagnosis, and predictive maintenance. The overcharging goal of this research is to integrate cloud computing, low-cost sensors, machine learning, and signal processing techniques into manufacturing equipment for online machine and process monitoring, diagnosis, and prognosis. The specific objectives of this project are as follows: Develop a generic framework for cloud-based online machine and process monitoring, diagnosis, and prognosis; Develop a private cloud-based data acquisition system that collects massive data from machines and processes using the ICT infrastructure that is solely operated within a corporate firewall; Develop a hybrid cloud platform that integrates the cloud-based data acquisition system with a public high-performance cloud computing system; Develop parallel and distributed machine learning algorithms for online diagnosis and prognosis in additive and subtractive manufacturing as well as motors and bearings.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 09, 2019
- Accession Number
- AD1104028
Entities
People
- Janis Terpenny
Organizations
- Pennsylvania State University