Uncertainty-aware soft sensor using Bayesian recurrent neural networks |
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Affiliation: | 1. College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;2. Key Laboratory of Optimal Design of Modern Agricultural Equipment, College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;1. Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA;2. Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA;1. School of Management, Harbin Institute of Technology, Harbin 150001, China;2. School of Architecture, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong 518055, China;3. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China |
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Abstract: | Data-driven soft sensors have been widely used to measure key variables for industrial processes. Soft sensors using deep learning models have attracted considerable attention and shown superior predictive performance. However, if a soft sensor encounters an unexpected situation in inferring data or if noisy input data is used, the estimated value derived by a standard soft sensor using deep learning may at best be untrustworthy. This problem can be mitigated by expressing a degree of uncertainty about the trustworthiness of the estimated value produced by the soft sensor. To address this issue of uncertainty, we propose using an uncertainty-aware soft sensor that uses Bayesian recurrent neural networks (RNNs). The proposed soft sensor uses a RNN model as a backbone and is then trained using Bayesian techniques. The experimental results demonstrated that such an uncertainty-aware soft sensor increases the reliability of predictive uncertainty. In comparisons with a standard soft sensor, it shows a capability to use uncertainties for interval prediction without compromising predictive performance. |
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Keywords: | Soft sensor Uncertainty Bayesian deep learning Recurrent neural networks |
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