Accession Number:

AD1104028

Title:

Cloud Enabled Machines with Data Driven Intelligence

Descriptive Note:

Technical Report,12 Nov 2016,30 Sep 2018

Corporate Author:

PENNSYLVANIA STATE UNIV STATE COLLEGE STATE COLLEGE United States

Personal Author(s):

Report Date:

2019-07-09

Pagination or Media Count:

53.0

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 machinesand 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 apublic high-performance cloud computing system Develop parallel and distributed machine learning algorithms for online diagnosis and prognosis inadditive and subtractive manufacturing as well as motors and bearings.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE