A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. (April 2017)

• Record Type: Journal Article
• Authors: Wu, Dazhong; Liu, Shaopeng; Zhang, Li; Terpenny, Janis; Gao, Robert X.; Kurfess, Thomas; Guzzo, Judith A.
• Is Part Of: Journal of manufacturing systems. Volume 43:Part 1(2017)
• Page Start: 25
• Page End: 34
• Publication Date: 2017-04
• Subjects: Fog computing -- Machine learning -- Industrial internet of things -- Prognosis -- Cyber-Manufacturing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2017.02.011
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A fog computing-based framework for data-driven machine health and process monitoring in cyber-manufacturing is introduced. The framework consists of four integral elements, including a workflow, wireless sensor networks, communication protocols, and predictive analytics. An online process monitoring system that is capable of collecting real-time machine condition data and monitoring the vibrations and energy consumption of pumps is demonstrated through a case study. A machine learning algorithm (i.e., random forests) is implemented on the Amazon Elastic Compute Cloud (EC2) to create predictive models on scalable high performance computing resources. The cloud-based machine learning algorithm is demonstrated using a tool wear prediction example in milling operations. Abstract: Small- and medium-sized manufacturers, as well as large original equipment manufacturers (OEMs), have faced an increasing need for the development of intelligent manufacturing machines with affordable sensing technologies and data-driven intelligence. Existing monitoring systems and prognostics approaches are not capable of collecting the large volumes of real-time data or building large-scale predictive models that are essential to achieving significant advances in cyber-manufacturing. The objective of this paper is to introduce a new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis. This framework utilizes wireless sensor networks, cloud computing, and machine learning. A proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics. Experimental results are provided to demonstrate capabilities and utility of the framework such as how vibrations and energy consumption of pumps in a power plant and CNC machines in a factory floor can be monitored using a wireless sensor network. In addition, a machine learning algorithm, implemented on a public cloud, is used to predict tool wear in milling operations.