Human-machine shared driving (HMSD) has emerged as a crucial transitional paradigm before the widespread adoption of fully autonomous vehicles. However, existing research typically only considers either human-dominated or human-machine equal relationships, neglecting the fact that these two relationships alternate during driving, which leads to a gap between theory and reality. To address this issue, this study proposes a large language model (LLM)-based game equilibrium selection approach for human-machine shared driving authority allocation. Firstly, a game equilibrium selection model is developed to seamlessly transition between Stackelberg equilibrium and Nash equilibrium, addressing human-dominated and human-machine equal relationships, respectively. The selection process is implemented using an LLM, which bases its decisions on scenario understanding. To enhance the LLM’s scenario understanding performance, a set of indicators capturing human-machine conflicts, driver involvement, and collision risks is introduced as prior knowledge. Furthermore, an LLM-based scenario-understanding module is designed to embed knowledge into the LLM and enable it to function effectively within the HMSD system. Finally, a human-in-the-loop experiment is conducted to validate the proposed strategy. The results show that LLMs can understand the provided knowledge, flexibly adapt to different scenarios, and accurately grasp human-machine interactions. Moreover, the proposed strategy effectively reduces human-machine conflicts, better satisfies driver intentions, and reduces driver workload, showcasing the potential of LLM-based decision-making in human-machine interaction.

To improve the reliability and interpretability of industrial process monitoring, this article proposes a causal graph spatial-temporal autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning module based on spatial self-attention mechanism (SSAM) and a spatial-temporal encoder-decoder module utilizing graph convolutional long short-term memory (GCLSTM). The SSAM learns correlation graphs by capturing dynamic relationships between variables, while a novel three-step causal graph structure learning algorithm is introduced to derive a causal graph from these correlation graphs. The algorithm leverages a reverse perspective of causal invariance principle to uncover the invariant causal graph from varying correlations. The spatial-temporal encoder-decoder, built with GCLSTM units, reconstructs time series process data within a sequence-to-sequence framework. The proposed CGSTAE enables effective process monitoring and fault detection through two statistics in the feature space and residual space. Finally, we validate the effectiveness of CGSTAE in process monitoring through the Tennessee Eastman process (TEP) and a real-world air separation process (ASP).

Fault root cause location is crucial role for industrial system safe operation. However, most industrial processes are time-varying due to operating mode changes, demand transitions, and equipment degradation, causing system causality evolution. These variations can reduce the performance of conventional fault root cause location methods. To address this issue, a spatial–temporal adaptive causality graph generation method (STACGG) is proposed to update the causality graph for fault root cause location. In the STACGG method, an edge aggregation masking operator is designed, in which the common part of the causality can be well inherited, while the customized part is online learned within the allowable maximum causality variation range, thus achieving steady adaptive progressive causality graph update. First, the most similar historical causality graphs are matched by contrasting the temporal and spatial characteristics of the pairwise node feature. Then, an edge aggregation-based graph generator (EAGG) is developed to identify a compact edge structure between the edge intersection and the edge union of these similar causality graphs. Enabling the edge intersection as the learning lower bound means inheriting common part of causality while taking the edge union as the learning upper bound represents steadily learn the customized part within the maximum causality variation range. To achieve it, the EAGG is formulated as an optimization task that maximizes the mutual information entropy between a GNN’s fault detection and the possible edge structure distribution. Then, to enhance the expression power and the interpretability of the causality graph, the posterior data information and the prior physical knowledge are combined as the inductive bias and the learning bias to fine-tune the causality graph for graph performance boosting. Finally, the fault root cause location performance is validated on two real-word industrial cases.

In industrial automation and intelligence, fault tolerance mechanisms have always been an attractive topic. To develop soft sensors with fault tolerance for different types of faults and unforeseen new faults, this article proposes a two-dimensional group distributionally robust optimization (2D-GDRO) framework for fault-tolerant soft sensor modeling. We propose to describe the potential distributions of new fault conditions with an uncertainty set and optimize the soft sensor model by minimizing the worst-case risk over the uncertainty set. Considering the restricted representation range of the uncertainty set constructed directly from a mixture distribution of a limited number of existing fault conditions in the training set, a two-dimensional uncertainty set is designed at the group dimension and the sample dimension. To efficiently train a fault-tolerant soft sensor within the 2D-GDRO framework, we introduce a triple-interleaved optimization algorithm. This algorithm integrates mini-batch stochastic gradient descent, exponentiated gradient ascent, and group-wise SoftMax techniques. Finally, the fault tolerance of the 2D-GDRO framework based soft sensor is verified using the Tennessee-Eastman process and the real three-phase flow facility. The experimental results show that 2D-GDRO outperforms other training frameworks in average soft sensing accuracy under new fault conditions.

Estimating traffic state, particularly traffic volume, is crucial in Intelligent Transportation Systems (ITS). Due to the absence or malfunction of detectors, some road segments are undetected, leading to a complete absence of volume data and thereby weakening the traffic monitoring capability of ITS. The existing estimation methods are either inapplicable to this scenario or yield poor results due to a lack of available data, which will compromise the traffic monitoring capability of ITS. To handle it, this work proposes a novel Road Network Similarity-based Transfer Learning method (RNS-TL) for real-time traffic estimation. Firstly, the Small-scale Road Network Similarity Evaluation Module (SSEM) is initially proposed which aims to identify the most similar road segments and their small-scale road networks for the undetected segments, serving as the source domain for transfer learning. Then, based on SSEM, a transfer learning framework is proposed where a traffic estimation model trained on the source domain is fine-tuned for the target undetected road segment. Finally, the results from two real-world traffic cases show that the estimation errors, MAE and RMSE, for the proposed method are 7.813 and 6.383, and 10.689 and 8.892, respectively, outperforming all comparison methods.

In multiunit industrial plant-wide processes, how to simultaneously model the dynamic correlations within units and the graph-structured interactions between units has not been explored by existing distributed monitoring methods. To address this issue, this work proposes a novel hybrid-order graph embedded distributed encoder-decoder (HGDED) for plant-wide process monitoring. Firstly, the whole process is decomposed into multiple operation units and characterized as a directed graph based on process knowledge. Then, distributed node encoder and decoder are developed under the sequence-to-sequence framework to capture the intra-unit temporal dependence. Between the encoder and decoder, a novel hybrid-order graph convolutional network (HGCN) is designed to simultaneously embed the inter-unit spatial dependence. Through multireceptive field graph convolution and spatial recurrent updates, HGCN can effectively exploit hybrid-order neighbor information to capture multi-cascaded interactions between units. Moreover, a spatial order optimizer is innovatively proposed to automatically learn the optimal discrete orders for different nodes, which helps HGCN better capture significant features from higher-order neighbor nodes. With concurrent analysis of temporal and spatial dependencies, the representation learning ability of HGDED is enhanced, thereby improving the monitoring performance. Finally, the effectiveness of the proposed method is demonstrated through the Tennessee Eastman process and a real-world air separation process.Note to Practitioners—The complex process characteristics and topology structure have imposed considerable challenges on monitoring multiunit industrial plant-wide processes. In practice, it is necessary to extract both the intra-unit temporal dependence and the inter-unit spatial dependence for distributed monitoring. Existing studies fail to consider these two important characteristics at the same time, which may lead to information loss and performance degradation. In this work, the proposed HGDED not only develops distributed node encoder and decoder to model the dynamic correlations within units, but also incorporates a HGCN to capture the interactions between units. In particular, HGDED explicitly models the whole process into a directed graph, which can characterize structural relationships in a more reasonable and fine-grained way. Considering the multi-cascaded information transmission between units, the HGCN is designed to exploit hybrid-order neighbor information for graph embedding, in which the optimal discrete orders are automatically determined by the spatial order optimizer. As a consequence, HGDED can better model process behaviors and extract more representative features to improve monitoring performance. In addition, HGDED follows an end-to-end training fashion, which is convenient for engineers to implement. The proposed method has been validated on two industrial cases, and it is suitable for monitoring various practical industrial plant-wide processes.

Due to the high-stakes nature of industrial processes, there is an immediate and pressing need on soft sensors for stability and interpretability. In this regard, causality-inspired modeling aims to learn causal features corresponding to the direct causes of quality variables, exhibiting great potential in terms of both stability and interpretability. However, most existing causality-inspired methods overlook temporal modeling and domain knowledge integration, which hinders their real-world application in industrial soft sensing. To this end, this article proposes a novel causality-inspired stable long short-term memory (Stable-LSTM), which leverages Bayesian-based causal structure inference and incorporates domain knowledge as a prior to enhance the performance stability and physical interpretability of soft sensors. After extracting temporal features via long short-term memory (LSTM), a Bayesian-based causal structure inference approach is developed by leveraging variational inference to learn the underlying hidden causal structure within the industrial processes. Through a hidden explanation of domain knowledge, a prior distribution is placed on the hidden causal structure, which will greatly enhance the physical interpretability and facilitate the exploration for true causality. Moreover, we also introduce a global sample reweighting strategy to remove spurious correlations and reveal causal effects between time series hidden features and quality variables. Finally, the performance stability and physical interpretability of the proposed Stable-LSTM are verified using a three-phase flow facility and a m-phenylenediamine distillation process. The results show that the Stable-LSTM achieves the highest soft sensing accuracy under distribution shift, and the inferred causal structure exhibits the greatest consistency with the domain knowledge, when compared with the seven existing methods.

In multiunit industrial plant-wide processes (MIPPs), data-driven distributed process monitoring methods have played an important role in ensuring process safety and reliability. However, the existing studies fail to leverage the relational knowledge about the underlying process structure of MIPPs, which may lead to inaccurate process modeling and degradation of the monitoring performance. To tackle these issues, this work proposes a novel knowledge-enhanced distributed graph autoencoder for plant-wide process monitoring. Firstly, a posterior graph structure learning module (PGSL) is designed for relational knowledge discovery, which explicitly characterizes the dependencies between operation units in MIPPs into a directed graph. Prior knowledge is introduced into PGSL as a learning bias that steers the posterior graph to adhere to the underlying physics. Then, a novel distributed graph autoencoder (DGAE) is developed to encode both the local information within each unit and the global information between units for distributed process monitoring. The discovered posterior knowledge is embedded as a relational inductive bias in DGAE to enhance the capability of unsupervised representation learning, thereby improving the monitoring performance. Finally, the effectiveness of the proposed method is demonstrated through the Tennessee Eastman process and a real-world air separation process.

Causality in an industrial process provides insights into how various process variables interact and affect each other within the system. It reveals the underlying mechanisms of industrial processes, which ensures predictive reliability and facilitates physical interpretability. However, existing causality-based techniques have limitations, as they neglect the temporal factor in causal description, introduce spurious causal associations in causal discovery, and fail to consider the spatial-temporal synchronicity in causal utilization. To address these issues, this article proposes a spatial-temporal causality modeling approach. A novel spatial-temporal causal digraph (STCG) is proposed to describe causal dependencies among process variables, which considers both spatial and temporal factors encompassing causal relationships and time delays. The STCG identification procedure is formulated as a Markov decision process, and knowledge-data guided reinforcement learning is developed to acquire the optimal identification policy and avoid spurious causal associations. With the identified STCG, a graph attention gate recurrent unit (GAGRU) is constructed for spatial-temporal process modeling, which is able to capture the synchronous evolution of industrial data in spatial-temporal dimensions. Finally, the effectiveness of the proposed modeling approach is verified by applying to two real industrial cases, including soft sensing for a sulfur recovery unit and anomaly detection for an argon distillation system. The experimental results demonstrate that the STCG-based industrial process modeling outperforms classical and state-of-the-art comparison methods in terms of reliability and interpretability.

Each freeway stretch’s traffic states are indispensable in freeway traffic modeling, surveillance, and control. However, the unmeasured ramp pairs always exist in real-world freeway systems, and how to estimate the flows of those ramps is a longstanding and tricky issue. Set the stretch with intact traffic states as Source Stretch while the stretch with the unmeasured ramp pair as Target Stretch; existing work tries to train the non-transfer machine learning model like Random Forest by Source Stretch and act on Target Stretch. However, the estimation accuracy of non-transfer machine learning models could not be guaranteed because the mainstream traffic state distributions of the above two stretches are not the same, and the model structure is too simple to capture traffic flow’s temporal dependencies. Note the great success of transfer learning in distribution-changed situations; this paper addresses this issue via transfer learning and deep learning. First, the Gated Recurrent Unit-Based Ramp Flow Estimator is designed to establish the relationship between the mainstream traffic states and ramp flows in Source Stretch. Then, taking the trained estimator as the backbone, we propose the Deep Domain Adaptation to match the marginal distribution difference between Source Stretch and Target Stretch; design the Model Transfer to reduce the conditional distribution difference (i.e., estimator difference) between Source Stretch and Target Stretch. The two approaches both improve the performance of the Source Stretch’s estimator in Target Stretch. Finally, we evaluate the processed approach in two real-world freeway traffic datasets and observe satisfactory results.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Sensor degradation seriously hinders the practical application of soft sensors. To reduce the negative effect of sensor degradation, in this article, we propose a robust domain adaptation mixture of Gaussian processes (DA-MGP) for online soft sensor modeling of multimode processes. Based on the decomposition of industrial data into a group of Gaussian domains, Gaussian domain discrepancy (GDD) is designed for domain adaptation and process mode recognition. After recognizing the process mode based on GDD, a Gaussian domain adaptation is presented to correct the drifted online input data by domain mapping, which can significantly improve the robustness of the soft sensor against sensor degradation. Furthermore, the domain mapping matrix is utilized as a transferred basis function for a local transferred Gaussian process component, which is used for robust soft sensor modeling. Additionally, an online block processing framework is adopted when the DA-MGP-based soft sensor is applied in online quality prediction. Finally, the TE benchmark process and a real industrial polypropylene process are employed to verify the effectiveness of the proposed method. In the designed five cases of sensor degradation, the DA-MGP-based soft sensor shows its strong robustness against sensor degradation.

In most classical multi-operation scheduling and control models, an assumption that different operations of a given job cannot be processed simultaneously is usually accepted. But in real-life applications, many different circumstances exist (i.e., with job overlaps). When overlapping of jobs is permitted, the paper reduces the complex problem of scheduling production of N types of products, each with n jobs, in an open shop with m unreliable machines, into simpler problems. The paper extends the application of a flow model on infinite horizon, of a single failure prone machine to build a system model of from orders arriving to completion of productions on infinite horizon, of a single failure prone machine to build a system model from orders arriving to completion of productions of finite horizon. After presenting an asymptotic optimal scheduling policy, based on discussing the model of the system, the optimal control production policy, etc., the paper presents the production planning of the system and its new heuristic iterative algorithm to determine the selection of setup times and the production order of products, etc.