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Intelligent Maintenance Systems capture invisible customer need, thus bridging the gap between customer requirements and service needs. Once a dominant design emerges, innovative activity is generally directed towards improving the process by which the dominant design is delivered or searching for a new breakthrough disruptive design. A dominant design can be generated using a systematic tool created and devised by Prof. Jay Lee, namely the innovation matrix. The matrix is divided into nine grids or quadrants that are distinguished by different combinations of customers (representing markets) and needs (representing products/services). Some needs are explicit, and others are latent. Articulated or explicit needs are separated into those needs that have been met and those that have not. Thus, there are three rows for determining the current status of needs. Similarly, the customers/markets are separated into two categories, the identified portion of which is divided into customers who are currently being served, and those who have yet to be. Successfully combining this matrix a QFD-based application space analysis enables systematic thinking and generation of opportunities for service innovation. The innovation matrix can be populated via group brainstorming; however, this is not the best way to generate a dominant design. Systematic thinking aids in guiding one to consider specific ideas one at a time. Figure 5 presents the innovation matrix for the Center for Intelligent Maintenance Systems. The matrix highlights both the short-term and long-term visions of IMS in terms of intelligent maintenance activities. 4 CASE STUDIES 4.1 Application on Intelligent Equipment Services The objective of this project is to provide an integrated life cycle knowledge management platform that will offer systematic aid for decision making. The system will identify, update, and display automatically in a decreasing order of failure risks, all the engines of the company at customer sites with their corresponding level of degradation, the potential root causes and their remaining life time to the next breakdown. A remote monitoring system [3] for heavy equipment is used to observe and register events and signals from each engine via satellite communication or through the internet. Health information for all engines are processed locally on-board and sent back to the design centre through a tether-free satellite connection. For visualization of results, a monitoring tool is developed that depicts a dynamic table interacting with the design centre knowledge base through the remote monitoring tools. Such synchronization of information offers a closedloop life cycle design using product maintenance information for engines. This case study enabled the implementation of the total chain of IMS’s remote monitoring system in a real world application. It enabled the company to achieve the near-zero downtime for its products and engines and offered an initiative for closed-loop life cycle management based on product maintenance information. The results obtained from this case study as well as the systematic methodology utilized to acquire them, can be extended from engines to other components such as transmission systems. 12 Vol 23 No 3 AMMJ Figure 5 Innovation Matrix for Intelligent Maintenance Systems (Reference: Prof. Jay Lee www.dominantinnovation.com)
13 Vol 23 No 3 AMMJ Intelligent Maintenance Systems 4.2. Application on Factory Energy Services This is an on-going project that aims on establish a Precision Energy Management Systems (PEMS) to increase factory energy transparency and awareness as well aid in reduction of electrical energy consumption levels by linking IMS tools and techniques to company products, processes, and business level. There are three key objectives: • PRODUCT: the PEMS must be able to define measure, analyze, and predict energy consumption per product manufactured, in such a way that each product leaving the process line can have an “Energy Label” that resembles the amount of energy consumed in manufacturing this product. • PROCESS: the PEMS must be able to use the developed energy metrics and parameters for detailed operational analytics. In other words, the PEMS will assess both equipment-level and process-level degradation and integrity. Such a correlation between maintenance activities on the shop floor and energy consumed through the machines and processes is an essential part of the PEMS initiative and is a large gap in today’s industry operations. • PLANT: the PEMS will enable facility-wide energy transparency, so that manufacturers can expand their current performance metrics of optimized quality, cost, and lead time to incorporate energy. Modeling and optimization of energy costs can also lead to improved manufacturing resource utilization. 5 CONCLUSIONS This paper presented the concept of intelligent maintenance systems as well as a systematic methodology and tools that have been effectively utilized to implement novel and innovative maintenance service systems in a diverse set of industries. The paper also presented two case studies to highlight the usage of intelligent maintenance systems in different industries. In conclusion, the benefits of an intelligent maintenance system will deliver new service functions to different customers for different products. Predictive maintenance services of assets will enable better information flow for better decision making. For a manufacturing business, predictive maintenance service of equipment will extend the life of equipment, increase uptime, increase product quality, decrease costs and increase overall productivity. For the product designer, predictive maintenance service of the designed products will enable a collaborative product closed-loop life cycle management. ACKNOWLEDGEMENTS NSF Industry/University Cooperative Research Center has been supported by NSF since 2001. Since its inception, it has been supported by over 60 companies globally, including P&G, GE Aviation, Toyota, GM, Caterpillar, Nissan, Parker Hannifin, Harley Davidson, AMD, Siemens, CISCO, Omron, Toshiba, Komatsu, Syncrude, Spirit Aerosystems, National Instruments, ITRI Taiwan, PMC, Advantech, TechSolve, Kistler, API, Army Research Lab., Intel, USPS, Hitachi, Rockwell Automation, etc. This support provided a solid foundation for IMS development and implementation. REFERENCES [1] Center for Intelligent Maintenance Systems (IMS), http://www.imscenter.net [2] Hai Q, Lee J, (2007) ‘Near-zero downtime: Overview & Trends’, Reliable Plant Magazine & Lean Manufacturing Journal [3] Lee J (1998) ‘Teleservice Engineering in Manufacturing: Challenges and Opportunities,’ International Journal of Machine Tools & Manufacture, Vol. 38, Number 8, pp. 901-910. [4] Lee J et al. (2003) ‘Watchdog Agent: Infotronics-based Prognostics Approach for Product Performance Degradation Assessment and Prediction.’ Advanced Engineering Informatics (2003) [5] Lee J (2003) ‘Smart Products and Service Systems for e-Business Transformation,’ Special Issues on “Managing Innovative Manufacturing,” International Journal of Technology Management, pp. 45-52, Vol. 26, No. 1. [6] Lee J (2003) ‘e-Manufacturing Systems: Fundamentals and Tools,’ Int. Journal of Robotics and Computer-integrated Manufacturing, Vol 9. Issue 6, pp 501-507. [7] Lee J, Ni J, Djurdjanovic D, Qui H., Liao H, (2006) ‘Intelligent Prognostics Tools and E-Maintenance,’ Int. Journal of Computer in Industry, Volume 57, Issue 6. [8] Lee J (2008). Dominant Innovation Design for Product and Service Systems. PowerPoint Lecture Course Presentations. University of Cincinnati, Cincinnati, OH. Sept-Dec 2008. [9] Lee J, AbuAli M, Deng C, Tsan C (2009) ‘Product Lifecycle Management Using Embedded Infotronics: Methodology, Tools, and Case Studies’, Int. J. Knowledge Engineering and Data Mining (IJKEDM), (Accepted) [10] Lee J, Yan C, Lapira E, Al-Atat H, and AbuAli M (2009) ‘A Systematic Approach for Predictive Maintenance Service Design: Methodology and Applications’, Int. J. Internet Manufacturing Services (IJIMS), (Accepted) The full Proceedings of COMADEM 2009 are available for sale. Please contact aarnaiz@tekniker.es
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