Predictive maintenance (or prognostics) is critical to any engineering systems to avoid system breakdown, in particular, for complex systems. With the recent advances on pervasive computing, prognostics can be easily embedded in any devices and systems.

When smart machines are networked and remotely monitored, and when their data is modeled and continually analyzed with sophisticated embedded systems, it is possible to go beyond mere “predictive  aintenance” to intelligent “prognostics”, the process of pinpointing exactly which components of a machine are likely to fail, and when and autonomously trigger service and order spare parts. This paper addresses the paradigm shift in modern maintenance systems from traditional “fail and fix” practices to “predict and prevent” methodology. Click here to read more

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