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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The transactions of The Korean Institute of Electrical Engineers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
