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Image-Based Prognostics Using Deep Learning Approach
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DOI：10.1109/TII.2019.2956220.png)
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Abstract
En 中文
This article proposes two methods based on deep learning for estimating time-to-failure (TTF) of an industrial system using its degradation image. This provides an effective tool for predictive maintenance practitioners toward digitization of maintenance processes in Industry 4.0 transformation. Both methods utilize the long short-term memory (LSTM) networks for capturing temporal information. First methodology consists of two convolutional layers preceding a single LSTM layer to extract compact information from the individual images and rescue LSTM network from curse of dimensionality. Then, an LSTM layer estimates the TTF value from the extracted features. In the second approach, the dimension of the individual images are decreased by a fully connected neural network, which is trained as an autoencoder. A separate LSTM network is trained and run over this lower dimensional space. The strength of suggested architectures is shown using simulation data and a dataset of infrared image streams collected from rotating machinery. The performance comparison of proposed methods and other methods is also provided.
Keywords:
Streaming media
Feature extraction
Convolution
Degradation
Machine learning
Data mining
Neural networks
Deep learning
image prognostics
Industry 4
0
predictive maintenance
remaining useful life (RUL) estimation
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