On the Use of Seismo-Acoustic Signatures for Power-Level Classification at an Industrial Facility

Date: 4/26/2019

Time: 06:00 PM

Room: Fifth Avenue

In a broad sense, industrial operations may be attributed to the initiation-or-termination of machine processes and changes in operational state configurations (e.g. power consumption, speed level, and frequency). These operational activities, within or adjacent to industrial facilities, generate mechanical energy that may propagate into the earth and air as seismic and acoustic waves, respectively. The types and intensities of the seismo-acoustic signals vary according to machinery and their relative location with respect to sensors. For example, seismo-acoustic signals recorded in close-range to a facility display a mixture of multiple harmonics with corresponding overtones embedded in broad band noise.

We have analyzed continuous seismo-acoustic data from a permanent station nearby the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). HFIR is an 85 MW research reactor and runs on an operational cycle of about 24 days, followed by an outage period. For each cycle, the reactor starts with a set of increasing power-levels (10%, 30%, 50%, 70% and 90%) before reaching full capacity. The recorded seismo-acoustic data provide us an opportunity to monitor power-levels using sensors outside the facility. Seismo-acoustic data corresponding to multiple cycle start-ups are extracted and compared to facility-operation ground–truth information. We will present results on the automatic classification of reactor start-up power-levels obtained by training machine learning models on these continuous seismo-acoustic data.

Presenting Author: Camila A. Ramirez

Authors