ARTEMIS project

A fAult chaRacterization sysTem basEd on Machine learnIng technologieS

Modern society relies on the availability and smooth operation of complex engineering systems. Examples include critical infrastructures, (e.g. electric power systems, water distributions networks, transportation systems, etc.) manufacturing processes, robotic systems, intelligent buildings, etc. The emergence of networked embedded systems and sensor/actuator networks has made possible the development of several sophisticated monitoring and control applications where a large amount of real-time data about the monitored environment is collected and processed to activate the appropriate actuators and achieve the desired control objectives. Depending on the application, such data may have different characteristics: multidimensional, multi-scale and spatially distributed. Moreover, the data values may be influenced by controlled variables, as well as by external environmental factors. However there are many cases where the collected data exhibit unusual patterns or their time evolution does not correspond to the expected one. For example, some measurements may be missing, sensor performance may be deteriorating due to aging or environmental conditions, sensors may be drifting, etc. In some cases, data coming from different sensors (or actuators), on the same unit (e.g., with different resolution) or residing in a cluster, may become inconsistent. At the same time, the environment can be subject to non-stationary phenomena and the electronics, e.g., the signal conditioning stage, is prone to drifts and soft/hard faults.

The ARTEMIS project aims at a) develop cognitive fault diagnosis technologies that can be effectively applied to monitoring and control applications of uncertain distributed environments, and b) develop a set of adaptation and learning algorithms that can be incorporated into the cognitive fault diagnosis scheme.

The ARTEMIS project on the announcements of the Department of Electronics, Information, and Bioengineering of Politecnico di Milano :)

*ACKNOWLEDGMENT

This work is supported by the Politecnico di Milano International Fellowship Program.