Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. ...First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.
In order to deal with the modeling and monitoring issue of large-scale industrial processes with big data, a distributed and parallel designed principal component analysis approach is proposed. To ...handle the high-dimensional process variables, the large-scale process is first decomposed into distributed blocks with a priori process knowledge. Afterward, in order to solve the modeling issue with large-scale data chunks in each block, a distributed and parallel data processing strategy is proposed based on the framework of MapReduce and then principal components are further extracted for each distributed block. With all these steps, statistical modeling of large-scale processes with big data can be established. Finally, a systematic fault detection and isolation scheme is designed so that the whole large-scale process can be hierarchically monitored from the plant-wide level, unit block level, and variable level. The effectiveness of the proposed method is evaluated through the Tennessee Eastman benchmark process.
Just-in-time learning (JITL) is one of the most widely used strategies for soft sensor modeling in nonlinear processes. However, traditional JITL methods have difficulty in dealing with data samples ...that contain missing values. Meanwhile, data noises and uncertainties have not been taken into consideration for relevant sample selection in existing JITL approaches. To overcome these problems, a new probabilistic JITL (P-JITL) framework is proposed in this brief. In P-JITL, variational Bayesian principal component analysis is first utilized to handle missing values and extract Gaussian posterior distributions of latent variables. Then, symmetric Kullback-Leibler divergence is creatively employed to measure the dissimilarity of two distributions for relevant sample selection in the JITL framework. Finally, a nonlinear regression model, Gaussian process regression, is carried out to model the nonlinear relationship between the output and the extracted latent variables. In this way, the proposed probabilistic JITL (P-JITL) is able to deal with missing data and select relevant samples more accurately. To evaluate the effectiveness and flexibility of P-JITL, comparative studies between P-JITL and traditional deterministic JITL (D-JITL) are carried out on a numerical example and an industrial application example, in which missing data are simulated with percentages from 0% to 50%. The results show that P-JITL can provide more accurate prediction accuracy than D-JITL in each scenario considered.
Industrial process plants are instrumented with a large number of redundant sensors and the measured variables are often contaminated by random noises. Thus, it is significant to discover the general ...trends of data by latent variable models in the probabilistic framework before soft sensor modeling. However, traditional probabilistic latent variable models such as probabilistic principal component analysis are mostly static linear approaches. The process dynamics and nonlinearities have not been well considered. In this paper, a novel weighted linear dynamic system (WLDS) is proposed for nonlinear dynamic feature extraction. In WLDS, two kinds of weights are proposed for local linearization of the nonlinear state evolution and state emission relationships. In this way, a weighted log-likelihood function is designed and expectation-maximization algorithm is then used for parameter estimation. The feasibility and effectiveness of the proposed method is demonstrated with a numerical example and an industrial process application.
The principal component regression (PCR) based soft sensor modeling technique has been widely used for process quality prediction in the last decades. While most industrial processes are ...characterized with nonlinearity and time variance, the global linear PCR model is no longer applicable. Thus, its nonlinear and adaptive forms should be adopted. In this paper, a just-in-time learning (JITL) based locally weighted kernel principal component regression (LWKPCR) is proposed to solve the nonlinear and time-variant problems of the process. Soft sensing performance of the proposed method is validated on an industrial debutanizer column and a simulated fermentation process. Compared to the JITL-based PCR, KPCR, and LWPCR soft sensing approaches, the root-mean-square errors (RMSE) of JITL-based LWKPCR are the smallest and the prediction results match the best with the actual outputs, which indicates that the proposed method is more effective for quality prediction in nonlinear time-variant processes.
Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of ...the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods.
Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data ...mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.
Most traditional soft sensors are built offline and only to be used online. In modern industrial processes, the operation condition is changed frequently. For these time-varying processes, online ...soft sensor modeling is required, since the prediction result is highly related to other components of the process control system. In the present paper, a comparative study of three different just-in-time-learning (JITL) methods for online soft sensor modeling is carried out, which are based on partial least squares (PLS), support vector regression (SVR) and least squares support vector regression (LSSVR). Different from traditional soft sensors which model the process through a global and offline manner, the JITL-based method exhibits an online local model structure, depending on which the change of the process can be well tracked. Besides, the process nonlinearity can also be addressed under this modeling framework. As a further contribution of this paper, a real-time performance improvement strategy is proposed to enhance the online modeling efficiency of the JITL-based soft sensor. For performance evaluation, two industrial case studies are provided.
Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. However, traditional JITL approaches mainly focus on equal sample sizes between process ...(input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised weighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and weight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding weights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results.
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