| Title |
An Abnormalities Detection Model of a Machine Using Spectrogram |
| Authors |
김연준(Youngjun Kim) ; 이석필(Seok-pil Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1274 |
| Keywords |
Spectrogram; Machines; Abnormal; LSTM; Predictive Maintenance; CNN |
| Abstract |
There are many methods for diagnosing abnormal conditions of machines. Among them we use a method using sound for detecting abnormalities of machines. Experimental data sets were collected at approximately 30 minutes intervals for 2 weeks. The collected data sets are converted into spectrogram images expressed by time, frequency and amplitude with a 5 second time step. In this paper, we propose a learning model created by combining Conv1D for image processing and LSTM for time series data processing to detect abnormal conditions of machines. The comparison test with the existing model combining CNN, Conv1D and GRU shows our method has a promising result. |