Korea Atomic Energy Research Institute

Electronics and Telecommunications Research Institute

Doo Byung Yoon

Cheol Sig Pyo

Nae Soo Kim

Kwi Hoon Kim

Ji Hoon Bae

Se Won Oh

Sun Jin Kim

Wan Seon Lim

Bong Su Yang

Jin Ho Park

Gwan Joong Kim

Soon Sung Moon

An apparatus for detecting a leak sound in plant equipment using time-frequency transformation includes a first microphone measuring an acoustic signal generated in the plant equipment, a second microphone measuring an acoustic signal generated in the plant equipment, and a leak sound detection module detecting a leak sound in the plant equipment using time-frequency transformation of the acoustic signal measured by the first microphone and the acoustic signal measured by the second microphone, wherein a leak sound may be detected even when a transient dispersive noise signal, as well as a periodic noise signal, is large.

Apparatus and method of detecting leak sound in plant equipment using time-frequency transformation

Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.

Apparatus and method for detecting anomaly in plant pipe using multiple meta-learning

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Daejeon, South Korea

Doo Byung Yoon