A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing
In industrial process control systems, there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online. The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables. This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors, which are applied to the benchmarked Tennessee-Eastman process (TEP) and a real wind farm case. The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods. First, the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks. Second, the multiscale feature extraction layers can powerfully extract dataset characteristics. Finally, the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.
