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Abstract

The paper proposes a new, robust cluster-based classification technique for Novelty Identification
in sensor networks that possess a high degree of correlation among data streams. During normal
operation, a uniform cluster across objects (sensors) is generated that indicates the absence of
novelties. Conversely, in presence of novelty, the associated sensor is clustered distinctly from the
remaining sensors, thereby isolating the data stream which exhibits the novelty. It is shown how
small perturbations (stemming from noise, for instance) can affect the performance of traditional
clustering methods, and that the proposed variant exhibits a robustness to such influences. Moreover,
the proposed method is compared with a recently reported technique, and shown that it performs
365% faster computationally. To provide an application case study, the technique is used to identify
emerging fault modes in a sensor network on a sub-15MW industrial gas turbine in presence of other
abrupt, but normal changes that visually might otherwise be interpreted as malfunctions.

Available Versions of this Item

• Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems. (deposited 09 Jun 2021 12:39)
  • Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems. (deposited 13 May 2021 10:42) [Currently Displayed]