Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 29, 2024 - June 01, 2024
The purpose of this research is to realize the predictive failure diagnosis of hydraulic actuators of dust trucks using a recurrent neural network - autoencoder (RNN-AE). In this paper, the performance of principal component analysis (PCA) and RNN-AE are compared as methods for extracting time-domain and frequency-domain features from the acceleration data of a hydraulic actuator, mainly in terms of compression performance. As a result, a total of 15 time-domain and frequency-domain features were extracted from the side acceleration data of the vehicle body, and it was found that the compression performance of PCA and RNN-AE was almost equal for the 15-dimensional features. A future work will be to apply it to calculating the degree of abnormality from the compressed feature values to realize a predictive failure diagnosis of hydraulic actuators.
