Today manufacturing system needs comprehensive maintenance scheme that enable the zero down time system failure. To accomplish this, various researches have been carried out with the emphasis of preventive maintenance. Condition based maintenance (CBM) is one kind of preventive system and it is used to diagnose the faults only when they are occurred. Recently study of prognostic and diagnostic enhancement to the condition based maintenance system become so intensive. Though current CBM system technologies are very rapidly progressing, the system has some inheriting limitations because every almost of the research frameworks are only focused on the integration of the CBM based prognosis system and traditional wired factory flow control system.
Such system has scalability issues because most of the framework previously studied are based on the centralized control system and it doesn’t meet the distributed nature of the CBM system. For the system level maintenance, the condition assessment will be from sub system level, equipment level, component level and sometimes different methods of sensing can be used on one subcomponent.
There some places which are impossible to access the monitoring data such rotation machinery or hazardous locations. It is also so difficult for where the system is mobile and reconfigurations must be carried out from time to time. According to the study, the current mostly used sensors in condition based system have low life time. It implies that reinstallations and reconfigurations will be matters.
Size and cost will be the other factors here because in the traditional system: sensing devices happens to be relatively large to the sensed components. Wiring cost and ….
So to overcome these constraints, the paper proposes so called “ embedded intelligence “, self configurable, self- healing, tiny wireless sensor networks nodes based design framework for the preventive or proactive condition based monitoring maintenance system.
Under such a unified framework, we pay special attention to the most important awareness issues in wireless sensor networks such as real-time dynamic awareness, energy awareness, spatiotemporal awareness and reconfigurable awareness.
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