In this article, a new sensor fault estimation algorithm is proposed for industrial processes described by linear discrete-time systems, where the fault dynamics are modeled as a stochastic process. By performing the variational Bayesian inference, the potential sensor fault, as well as the system states, is estimated simultaneously in a probabilistic framework. It is shown that the target fault signal can be satisfactorily estimated through the proposed method, without knowing the statistics of measurement noise and fault coefficient matrix. The efficiency and superiority of the proposed method are demonstrated through numerical simulations and experimental tests performed on a hybrid tank system.

This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results.

Batch process is difficult to control accurately due to their complex nonlinear dynamics and unstable operating conditions. The traditional methods such as model predictive control, will seriously affect control performance when process model is inaccurate. In contrast, reinforcement learning (RL) provides an viable alternative by interacting directly with the environment to learn optimal strategy. This paper proposes a batch process controller based on the segmented prioritized experience replay (SPER) soft actor-critic (SAC). SAC combines off-policy updates and maximum entropy RL with an actor-critic formulation, which can obtain a more robust control strategy than other RL methods. To improve the efficiency of the experience replay mechanism in tasks with long episodes and multiple phases, a new method of sampling experience called SPER is designed in SAC. In addition, a novel reward function is set for the SPER-SAC based controller to deal with the sparse reward. Finally, the effectiveness of the SPER-SAC based controller for batch process examples is demonstrated by comparing with the conventional RL-based control methods.

Multivariate statistics process monitoring can achieve dimensionality reduction and latent feature extraction on process variables. However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance.