Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/app112311516
Keywords
RUL; DCAE; CNN; rolling bearing; health indicator
Categories
Funding
- National Natural Science Foundation of China [51875498, 51475405]
- Natural Science Foundation of Hebei Province, China [E2018203339, F2020203058]
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This paper proposes a method for predicting the remaining service life of equipment through a combination of DCAE and CNN, with a focus on rolling bearings. The results demonstrate that the constructed HI can effectively characterize the bearing's degradation state and accurately predict the bearing's degradation trend.
Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method's effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.
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