Process monitoring of nonlinear uncertain systems based on Part Interval Stacked Autoencoder and Support Vector Data Description - Wu, Qiqi; Lu, Weipeng; Yan, Xuefeng (Applied soft computing, 2022)
Chemical process measurement may cause inaccuracy and uncertainty in some parts of the measurement data due to equipment aging and environmental conditions, such as temperature, humidity, gas pressure, gas flow, and other related factors. Uncertain data of this kind commonly fluctuate in an interval centered on the true value. Given the uncertain characteristics of some parts of the measurement data, the certain of some parts, and the nonlinearity of the industrial process, the present paper proposes a novel algorithm of process monitoring and fault diagnosis called Part Interval Stacked Autoencoder and Support Vector Data Description (PISAE–SVDD). The loss function of Stacked Autoencoder (SAE) is improved in this algorithm. In certain measurement data, the reconstruction error value is the mean square error of the original input data and output data. By contrast, uncertain measurement data provide a certain allowable range for reconstruction error value. The error value of measurement data is considered as zero within this range. The reconstruction error value of measurement data beyond the allowable range is calculated in the same way as for certain measurement data. At the same time, the characteristic information of the industrial process is extracted through the strong nonlinear characterization ability of SAE. Support Vector Data Description is then used to obtain the control limit of the fluctuation range of the normal working condition based on the SAE feature information data extracted from normal samples. This approach is adopted to realize the process monitoring of the industrial process with partial uncertain measurement. To detect fault, this algorithm was applied to the numerical simulation, the Tennessee Eastman process, and the process in industrial wastewater treatment plants, and then compared with other advanced algorithms. The results indicate the excellence and high efficiency of the PISAE–SVDD algorithm in the field of process monitoring. •A novel process monitoring model based on PISAE and SVDD is proposed.•The loss function of PISAE is defined to consider both uncertain and certain data.•PISAE is proposed to identify the feature of the nonlinear uncertain systems.•Use SVDD to obtain control limits of the feature information.