Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications

Abstract

Over recent years, a significant amount of research has been dedicated to the development of Prognostics and Health Management (PHM) models. However, less attention is paid towards implementing the developed models to real-world applications to serve the needs of industry. In order to successfully implement a PHM system, a systematic approach is required to deploy the developed analytic tools (algorithms, software and agents) using a scalable hardware platform. In this paper, different PHM deployment platforms including stand-alone PC, embedded and cloud-based platforms are benchmarked. Then, a unified strategy for deploying the developed PHM tools using each of these platforms is presented. A smart deployment platform selection method using Quality Function Deployment (QFD) is also introduced. Following that, several case studies from different applications are provided as examples to demonstrate the capabilities and limitations of each deployment platform.

Intelligent Factory Agents with Predictive Analytics for Asset Management

“…However, the configuration of SA, KA, and EA depend on the specific asset and its condition, available resources, data analysis concerns, and other constraints. Description of the three agents included for implementing predictive manufacturing (Lee et al, 2013a). The software architecture of such agentbased PHM platforms is shown in Figure 19.4 (Lee et al, 2013a).…”

“…Figure 19.6 An industrial agent platform based on an embedded system (Lee et al, 2013a). It is also challenging to take advantage of the PLC controller processors to synchronize and trigger the external data acquisition.…”

“…In such deployment platforms, KA (shown on the left side of in Figure 19.4) is responsible for loading the PHM models that are already developed by the system. SA (shown at the bottom of Figure 19.6) interfaces with the control system to provide feedback based on the obtained asset health information and avoids the progression of the potential faults in the system (Lee et al, 2013a). SA (shown at the bottom of Figure 19.6) interfaces with the control system to provide feedback based on the obtained asset health information and avoids the progression of the potential faults in the system (Lee et al, 2013a).…”

“…Cloud computing is considered an important step in the advancement of IT technology and provides significant computational and storage resources that were not accessible before. Three levels have been suggested for this structure: a manufacturing service channel, an in-factory resource management, and an intelligent service platform (Lee et al, 2013a). Also, along with the rapid advancement of technologies around smart phones, tablets, and wireless networks there has been a boom in interactive applications and service delivery methods for use in platforms such as smart phones, tablets, and so on Chun and Maniatis, 2009).…”

“…Moreover, such PHM workflow needs to be selfadaptive in order to work for different monitoring cases (Lee et al, 2013a). Moreover, such PHM workflow needs to be selfadaptive in order to work for different monitoring cases (Lee et al, 2013a).…”