Batch Process Fault Research Articles

Design of Simulation System for Batch Process

Process monitoring and fault diagnosis of batch process is a research focus in the industrial control field. In this paper, penicillin fermentation is taken as the research background, a visual batch process simulation system is designed based on mathematical models of an actual production process. By introducing different fault signals to the penicillin fermentation simulation process, the designed system can be used to simulate the real penicillin fermentation production process clearly. In the end, an ideal experimental simulation data for batch process fault diagnosis is provided.

Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring

A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity.

Batch Process Monitoring and Fault Diagnosis Based on Multi-Time-Scale Dynamic PCA Models

Dynamics are inherent characteristics of batch processes, which can be divided into short time-scale dynamics within a batch duration and long time-scale dynamics across several batches. The interactions between process variables make different types of dynamics confounded. Under such situations, it is difficult to perform efficient fault diagnosis. In this paper, a batch process monitoring scheme is proposed to separate different types of process variations for modeling and perform monitoring and fault diagnosis with multi-time-scale dynamic principal component analysis (PCA) models. Simulation results show that the fault diagnosis efficiency is enhanced.

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

A Stage-Based Monitoring Method for Batch Processes with Limited Reference Data

A method is proposed for batch process monitoring and fault diagnosis, starting from a single batch reference data and updating with accumulation of successive batches. A moving data window method is adopted for exploring local covariance structure, stage division, and the development of monitoring models. The application to an injection molding process shows the effectiveness and feasibility of the proposed method for batch industry.

Assessing the Impact of Parametric Uncertainty on the Performance of Model-Based PCA

In model-based PCA (MBPCA), principal component analysis is carried out on the residual process measurements that cannot be predicted using a physical model. In principle, this approach can improve the detection and identification of unmeasured disturbances and faults in non-stationary and batch processes. Since process knowledge is required for the implementation of MBPCA, the uncertainty associated with the process model will inevitably affect the achievable performance. This paper presents a method for estimating the impact of parametric uncertainty on the performance of MBPCA and demonstrates its application on the monitoring of a continuous stirred tank reactor.