Journal of Process Control最新文献

State estimation for the Markov jump linear system (MJLS) is a intractable task when the unpredictable measurement loss exists. Although the conventional methods, such as interacting multiple-model method, are widely used in MJLS, their performance still depends on the known transition probability matrix (TPM). In this article, a novel adaptive state estimation method is proposed for MJLS with unknown measurement loss and TPM based on variational Bayesian inference. Specifically, under system state dynamic and measurement loss are independent, the system state, measurement loss probability and TPM are jointly inferred. In particular, when the stochastic measurement loss occurs, a selective learning mechanism is used to the updating of TPM. The efficiency and superiority of the proposed method is verified by a numerical example and a fermenter process compared with the existing methods.

Presently, the demand for precise process monitoring during geological drilling has increased dramatically. However, there exists complex dynamic characteristics due to the various forms of changes in operation conditions. A large number of false alarms are usually triggered when using the conventional static-based monitoring methods. In this paper, two types of dynamic behaviors are comprehensively considered, including the dynamic behaviors caused by the operating parameters adjustment and the operating mode switching, and then, a full condition monitoring method is proposed for the drilling process based on just-in-time learning (JITL)-aided slow feature analysis (SFA). On one hand, the JITL local modeling strategy is improved and adopted to deal with the dynamic behavior due to the operating mode switching. Specifically, a sequence spatiotemporal similarity analysis method is developed to improve the local modeling performance. On the other hand, the SFA-based concurrent monitoring of static deviations and dynamic anomalies is realized to cope with the dynamic behavior due to the operating parameters adjustment. Several industrial cases based on actual drilling data are conducted, which illustrate the effectiveness and superiority of the proposed method.

Process monitoring detects faults and issues alerts when faults occur. It has become an integral part of ensuring the safety and quality of industrial processes. Existing mainstream process-monitoring methods often separate data from knowledge, forming distinct systems. However, data and knowledge exhibit complementary characteristics, and using them together can contribute to enhancing monitoring performance. Furthermore, the importance of fault data has not been adequately emphasized. Within this fault data, valuable fault features contribute significantly to process monitoring. In light of these considerations, we propose a process-monitoring method based on temporal knowledge graphs and supervised contrastive learning,which can fully use knowledge, data, and fault information to improve the monitoring performance of the model. First, a temporal knowledge graph is constructed, in which knowledge and data are organically integrated through qualitative knowledge and quantitative data calculations to enhance the interpretability and accuracy of the graph. Second, spatiotemporal features are extracted from the temporal knowledge graph at multiple levels through differentiable graph pooling. Finally, a monitoring statistic is constructed, and fault information is introduced into the statistic through supervised contrastive learning, using fault information to enhance monitoring performance of the model. The fault detection rate on the float-glass production process reaches 95%.

A new robust nonlinear model predictive control (RNMPC) heuristic is proposed, specifically developed to be i) easy to implement, ii) robust against constraint violations and iii) fast to solve. Our proposed heuristic samples from the disturbance distributions and performs $n$-steps-ahead Monte Carlo (MC) simulations to calculate the back-off where $n$ is a small number, typically one. We show two implementations of our heuristic. The Automatic Back-off Calculation NMPC (ABC-NMPC) uses MC simulations on a process model to calculate the back-off, and explicitly states the back-off in a standard NMPC problem. Our second implementation, the MC Single-Stage NMPC (MCSS-NMPC), directly includes the disturbance distribution in the optimization problem, making it an implicit back-off method. Our methods are robust against constraint violation in the next time-step, under certain assumptions. In the presented case-study, our proposed RNMPC methods outperform the popular multi-stage NMPC in terms of robustness and/or computational cost. We suggest several further modifications to our RNMPC methods to improve performance, at the cost of increased complexity.

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

Zero-shot learning (ZSL) can diagnose unseen faults without corresponding training data, which has aroused the researchers’ interest. However, a prevailing challenge in most existing ZSL approaches is their limited effectiveness in distinguishing similar unseen faults. This paper proposed a category-tree-guided hierarchical knowledge transfer zero-shot fault diagnosis (CTZSD) method, which is a coarse-to-fine zero-shot fault diagnosis framework to alleviate this problem. To embody the similarities between fault categories, the concept of fault category tree is proposed, for which a data-attribute collaborative tree construction mechanism (DATC) is designed. Rather than relying solely on semantic knowledge, DATC involves data, which carries richer information, to complement the category similarity evaluation. A hierarchical knowledge transfer zero-shot fault diagnosis mechanism (HKT) is subsequently developed, utilizing the established category tree to gradually narrow down the options, thereby promoting the recognition of similar unseen faults. The mechanism treats the diagnostic outcomes and model parameters from coarse-grained tasks as knowledge and transfers them to fine-grained tasks for guidance, realizing a coarse-to-fine diagnosis. Aiming at providing discriminative information to further distinguish similar unseen faults, attention modules are integrated within HKT. These modules assess attribute weight, thereby directing the model’s focus toward the discriminative attributes of similar unseen faults. Experiments on a real TPP industrial process demonstrate that the proposed CTZSD outperforms other traditional ZSL methods in distinguishing similar unseen faults, improving the average accuracy by at least 19.7%.

The accurate soft sensing of f-CaO content in cement clinker is crucial for the cement industry. However, existing methods require improvements in terms of effectiveness, practicality, and computational efficiency for industrial applications. Responding to these needs, this paper proposes a self-adaptive multisource information fusion framework (SA-MSIFF) for f-CaO content soft sensing. The SA-MSIFF utilizes a dynamic rotary kiln model for independent and real-time calcination state estimation and mechanistic feature generation, along with a dilated 3D convolution and attention-based network for direct feature extraction from flame image sequences. Subsequently, a temporal–spatial feature extraction and fusion (TSFE&F) network is introduced to utilize the multisource feature series for f-CaO content soft sensing. Offline experiments validate the SA-MSIFF’s ability to adaptively extract features from multisource information. Compared to its previous version, MSIFF, the SA-MSIFF achieves a considerable 89.65% reduction in framework training time and an 8.22% decrease in soft sensing error. The SA-MSIFF’s effectiveness is also demonstrated in its engineering applications.

Momentum wheel bearing is a critical component within satellite systems, and its condition monitoring not only extends the operational lifespan of the satellite but also ensures the seamless fulfillment of its mission objectives. Various data-driven techniques have been introduced to assimilate health-related information. However, these techniques neglect the significant challenges posed by robust disturbance and volatility of degradation process, resulting in suboptimal evaluation performance. To address these issues comprehensively, this paper proposes a novel approach named canonical variable fluctuation analysis (CVFA) to facilitate precise health monitoring of momentum wheel bearings by concurrent analysis of static deviation and dynamic oscillation. Firstly, three quantifiable standards of consistency, accuracy and sensitivity are defined to select the degradation trend-related indices from multi-domain features, which provides an automatic and objective feature selection method. Subsequently, CVFA is developed to realize feature reduction and extracts the dynamic information from the features with strong disturbance and high fluctuation. Two Fluctuation (F) statistics are defined to characterize the health degradation trend by integrating both static deviation and dynamic volatility within a sliding window. Afterwards, autoregressive moving average (ARMA) model is constructed on the basis of F statistics for short-term prognostication, which enables proactive detection of degradation trends. Lastly, by integrating two F statistics, a health degree (HD), which is independent of parameter adjustments, is defined to intuitively represent bearing health status. The efficacy and superiority of the proposed method are substantiated through validation and analysis conducted using accelerated life tests of bearings.

Predicting quality-relevant process variables is of paramount importance in optimizing and controlling chemical processes. Probabilistic Slow Feature Analysis (PSFA), a potent data-driven technique, plays a pivotal role in deducing quality indices by abstracting gradual variations in processes distinctly characterized by pronounced inertia. Nevertheless, PSFA’s predictive efficacy encounters a substantial bottleneck due to the assumption of a single operating condition, compromising its accuracy, particularly in industries represented by switching operating conditions. To surmount this limitation, this study proposes an innovative approach that enriches PSFA with multi-operating condition process data and limited labels within a Bayesian framework, effectively combining continuous and discrete first-order Markov chains to capture the processes’ inertia and dynamic shifts. The proposed method updates latent posterior distributions and model parameters iteratively via the Expectation–Maximization algorithm. The effectiveness of the proposed methodology is verified through a numerical case and industrial hydrocracking process data.

Microalgae cultivation for energy production is a promising avenue for converting solar light into sustainable biofuel. Solar processes are however subjected to the permanent fluctuations of light and medium temperature. Accurate temperature prediction of the culture medium turns out to be critical for optimising growth conditions. In this study, we introduce a reduced-model approach derived from existing models, turning the complex heat transfer modelling problem into an identification problem. The resulting generic model, called the Simplified Auto Tuning Heat Exchange (SATHE) model, has a clear and simple structure, offering a balance between accuracy and computational complexity. The SATHE model is versatile and contains the necessary terms to catch a large variety of heat transfer problems, while the parameters can be identified from experimental data. We first prove the parameter identifiability and then propose an identification strategy, based on the gradient computation, to identify the model’s underlying parameters. We further validate the SATHE model performance in two distinct reactors across various seasons. Finally, we discuss the potential of online applications with a continuous self-tuning strategy to keep optimal predictive performances. This work lays the foundation for enhanced control strategies in large-scale cultivation systems.