Intelligent Remote Monitoring and Maintenance Systems

Intelligent Remote Monitoring and Maintenance Systems

Chengliang Liu (Shanghai Jiao Tong University, China) and Xuan F. Zha (National Institute of Standards and Technology, University of Maryland, USA and Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-59904-249-7.ch016
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Internet-based intelligent fault diagnosis and maintenance technologies are keys for enterprises to achieve global leadership in market competition and manufacturing productivity for business in the 21st century. e-Products, e-Manufacturing and e-Service have been the goals of enterprises: 1) Next generation products must be network-based products—e–products. The vast developments of IT technology based hardware and software make the controller of internet based products cheaper; 2) Common facilities such as internet and World Wide Web, 3G (GPS, GPRS and GIS) make e-maintenance or e-service cheaper and easier; and 3) “Server-web-user” methodology makes e-manufacturing possible, convenient and efficient. To achieve these goals, smart software and NetWare are needed to provide proactive maintenance capabilities such as performance degradation measurement, fault recovery, self-maintenance, and remote diagnostics. This chapter presents methodologies and techniques for the development of an Internet server controller based intelligent remote monitoring and maintenance system. Discussion involves on how to make innovations and develop products and manufacturing systems using internet-based intelligent technologies and how to ensure product quality, coordinate activities, reduce costs and change maintenance practice from the breakdown reaction to prevention. A hybrid intelligent approach using hardware and software agents (watchdog agent) is adopted. The server controller is web-enabled and its core is an embedded network model. The software agent is implemented through a package of Smart Prognostics Algorithms. The package consists of embedded computational prognostic algorithms developed using neural network based, time-series based, wavelet-based and hybrid joint time-frequency methods, etc. and a software toolbox for predicting degradation of devices and systems. The effectiveness of the proposed scheme is verified in a real testbed system

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