Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems

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.