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Model-free condition monitoring with confidence

ABSTRACT Computational Self-awareness can improve performance, robustness, and adaptivity of a system. As a key element of self-awareness, observation quality is critical to gain a correct and comprehensive understanding of the system, its own state, and the environment. This is of more importance in systems, where contextual information plays a crucial role in the functional operation of the system. In this paper, the authors introduce confidence as a quality metric of observation and leverage it to improve the correct identification of states of a system. To evaluate the impact of this factor on the context-aware monitoring system at hand and to show the generality of the approach, we conduct a series of tests with and without confidence for condition monitoring of an industrial AC motor and an experimental water pipe system. Our experiments show that confidence not only improves the quality of system performance but also simplifies the system architecture and enhances its robustness. These findings support the recent initiatives of paying more attention to observation as an important factor in self-awareness and, consequently, the performance of systems. The proposed system facilitates condition monitoring of various industrial systems and is easily deployable as it does not require a deep domain knowledge.