Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.

A missing variable approach for decentralized statistical process monitoring

The main focus of the current work is on the investigation and application of a missing variable approach in principal component analysis (PCA) model for decentralized process monitoring purpose. Given that the widely studied PCA algorithm can recover the correlations between measured variables, a missing variable approach is employed for computing score estimation error and residual estimation error from the developed PCA model. Through assuming but only one variable is missing in sequence, the residual between the actual and estimated components is generated and then monitored instead of the original data. The presented method implements a missing variable based offline modeling and online monitoring in a decentralized manner. Generally, the generated residual is expected to follow or at least become much closer to a Gaussian distribution, the resulted model has no restriction on Gaussian distributed dataset and can achieve salient monitoring performance in contrast to its counterparts. Finally, its superiority and effectiveness have been demonstrated by conducting comparisons on two industrial examples.