Deep Autoencoder With Orthogonal Features for Process Monitoring

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Abstract
With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.
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  • Research and Development

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