Abstract
Data-driven soft sensors are usually used to predict quality-related but
hard-to-measure variables in industrial systems. However, the
acceptable prediction performance mainly relies on the premise that
training data are sufficient for model training. To acquire more
training data, this paper proposes an adversarial transfer learning
(ATL) methodology to enhance soft sensor learning. Firstly, a
hierarchical transfer learning algorithm, which integrates a feature
extraction method with model-based transfer learning, is proposed to
refine the useful hidden information from both historical variables and
samples. Then, a novel adversarial learning network is designed to
prevent the deterioration of transferred results at each transfer
learning stage. Thirdly, a Granger causality analysis (GCA)-based
rationale analyzer is added to unfold the internal causality among input
variables and between input and output variables simultaneously.
Finally, the effectiveness of the proposed soft sensor and the rationale
analyzer is validated in a simulated wastewater plant, Benchmark
Simulation Model No.2 (BSM2), and a full-scale oxidation ditch (OD)
wastewater plant. The experimental results demonstrate that the
ATL-based soft sensor can achieve more accurate prediction in terms of
RMSE and R, and the GCA-based rationale analyzer can provide a visual
explanation for the corresponding model and prediction results.
Original language | English |
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Article number | 3000610 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 72 |
Number of pages | 10 |
ISSN | 0018-9456 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- Soft sensor
- Adversarial transfer learning
- Granger causality analysis
- Historical data
- Industrial systems