Fault diagnosis is important for ensuring the safe and stable operation of industrial equipment. Due to the characteristics of high dimensionality, nonlinearity and timing in modern industrial processes, the problems brought about by traditional fault diagnosis methods are becoming increasingly evident. Deep learning-based fault diagnosis demonstrates excellent performance. However, most fault diagnosis models are insufficient in feature extraction and cannot remove more redundant feature information. Therefore, a new model integrating dual-channel attention convolutional neural network and eXtreme Gradient Boosting (XGBoost) algorithm was developed. Among them, the attention convolutional neural network can enhance features through channel attention and spatial attention, reduce redundant information, and improve diagnostic performance. To obtain the optimal model, the Adam optimizer with better reliability is used to optimize the model. Finally, the optimal feature information collected by the attention convolutional neural network is passed to the XGBoost classifier for classification. Extensive experiments on two benchmark datasets, the Tennessee Eastman (TE) process and a real-world industrial coking furnace, demonstrate that the proposed method achieves superior diagnostic accuracy of 95.64% and 97.44%. The proposed framework shows great potential for deployment in practical industrial scenarios where fast and accurate fault detection is essential.

In industrial fault diagnosis, traditional methods grapple with challenges, such as nonstationarity, nonlinearity, high dimensionality, and strong coupling. To address these issues, we propose an end-to-end fusion model based on multiscale residual convolutional channel attention and transformer model (MRCC-Transformer). This approach initially leverages a multiscale residual convolutional neural network (CNN) to extract data features across various scales, thereby preventing model degradation and autonomously learning and integrating abundant fault information from multiple monitoring variables. Subsequently, a channel attention mechanism (CAM) is introduced to prioritize focus on pertinent convolutional channels to enhance the network’s effectiveness and discriminative capacity. Furthermore, the Transformer is employed to establish dependencies among distinct features to enhance fault diagnosis accuracy. Lastly, the input data is classified for fault diagnosis. The efficacy of the proposed method was validated through simulation experiments on the Tennessee-Eastman (TE) process and an industrial coking furnace. Comparative results demonstrate that the proposed method significantly improves the accuracy of fault diagnosis.

The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order to solve the above problems, this article proposes an end-to-end multiscale feature learning method based on model fusion, which can simultaneously extract multiscale spatial features and temporal features of data, effectively reducing the loss of feature information. First, this article combines the convolutional neural network (CNN) with residual learning and designs a multiscale residual network (MRCNN) to extract high-dimensional nonlinear spatial features of different scales in the data. Then, the extracted features are input into the long and short-term memory (LSTM) network to further extract the temporal features of the data. After the fully connected layer, it is input into the classifier for final fault classification. The residual learning in MRCNN can effectively avoid the problem of model degradation and improve the training efficiency of the model. Through the fusion of MRCNN and LSTM, we can significantly improve the feature extraction ability of the model, thereby greatly improving the diagnosis effect. In the final case experiment, the method improved the comprehensive diagnostic accuracy of the Tennessee-Eastman (TE) process and industrial coking furnace datasets to 94.43% and 97.80%, respectively, which was significantly better than the existing deep learning model and proves the effectiveness and superiority of this method.

Deep learning technology has been widely used in fault diagnosis for chemical processes. However, most deep learning technologies currently adopted only use a single network stack or a certain network stack with multilayer perceptron (MLP) behind it. Compared with traditional fault diagnosis technologies, this method has made progress in both the diagnosis accuracy and speed, but due to the limited performance of a single network, the accuracy or speed cannot meet the requirements to the greatest extent. In order to overcome such problems, this article proposes a fault diagnosis method using deep learning multimodel fusion. Different from previous deep learning diagnosis methods, this method uses long short-term memory (LSTM) and convolutional neural network (CNN) to extract features separately. The extracted features are then fused and MLP is taken as the input for further feature compression and extraction, and finally the diagnosis results will be obtained. LSTM has long-term memory capabilities, the extracted features have temporal characteristics, and CNNs have a good effect on the extraction of spatial features. The proposed method integrates these two aspects for diagnosis such that the features finally extracted by the network have both spatial and temporal characteristics, thereby improving the network's diagnostic performance. Finally, a TE chemical process and an industrial coking furnace process are taken for simulation testing. It is proved that the performance of this method is superior to existing deep learning fault diagnosis methods with simple sequential stacking for unilateral feature extraction.

Driving cycles have a great influence on vehicles’ fuel economy, control performance and drivability. In this paper, vehicle speed is considered and twelve statistical features for driving pattern recognition are selected to obtain the characteristics of driving cycles. To extract the statistical features online from the speed distribution information, the sampling and updating windows are set and the number of features is reduced. Moreover, since the structure and parameters of neural network are crucial to the classifier, the structure and parameters of neural network, the sampling and updating window size, the feature subset selection are simultaneously optimized by genetic algorithm (GA) to improve the classifying accuracy and simplify neural network structure. The hybrid encoding/decoding and the structure operator are designed to optimize the whole neural network classifier. Four typical driving patterns, i.e., congested urban road, flowing urban road, suburban and highway, are selected based on multiple driving cycles. Simulation results show that the classifiers with GA optimized features have more powerful classification capability than principle component analysis (PCA) and kernel PCA (KPCA) based on k-nearest neighbor, support vector machine neural network classifiers and KPCA Convolutional neural network classifier. The proposed classifier obtains the satisfying classification accuracy with faster real-time classifying speed.

The design of a novel model-based infinite horizon linear quadratic tracking control (IHLQ) is studied for chemical processes in the presence of partial actuator failures in this article. In order to acquire enhanced system performance against various uncertainties, an improved extended state-space formulation in which the state observer is not needed anymore is derived from the original process model through using process data first. By utilizing such improved model, extra adjustments on the process output changes and input changes are achieved and smoother dynamics for the controlled system are anticipated. Then, an IHLQ control is designed, where more degrees of freedom are obtained by additional weighting coefficients in the related performance index, hence modified control performance is yielded. With the existence of actuator faults, the process is theoretically an uncertain system. Therefore, robustness conditions for controller in terms of process parameter uncertainty are proposed using the Lyapunov theory. The validity of the proposed IHLQ is tested on the regulation of the injection velocity in an injection molding process.

Asynchronous switching between the controller and the active subsystems in multiphase batch processes may cause the systems to be unstable around the switching instants. In view of this, an average dwell-time method-based iterative learning control (ILC) scheme is proposed in this paper. First, the multiphase process is represented as an equivalent closed-loop two-dimensional (2-D) switched system composed of stable and unstable subsystems, based on which new relevant concepts on the stability of the switched system are given. Second, using an average dwell-time method, the ILC law is designed to guarantee the system exponentially stable. Minimum running time for the stable subsystems and maximum running time for the unstable ones are obtained. Lastly, depending on the maximum time for the unstable subsystems, the idea of putting the controller switching step forward is proposed. In this way, the asynchronous switching is removed such that the unstable subsystem can be avoided. The case study on an injection molding process demonstrates the effectiveness and superiority of the proposed method in comparison with the existing 2D-MPC and one-dimensional traditional control methods.

To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design; second, a new error compensation strategy is introduced in the controller design to improve the ensemble control performance. The two merits are combined together to form a new control strategy where the model predictive control and iterative learning control are united based on the 2-D framework. By employing the novel model formulation and the system error compensation approaches, the effects caused by uncertainty in the batch processes can be eliminated gradually from cycle-to-cycle such that the desired control performance will be obtained finally. The effectiveness of the proposed 2D-MPILC is tested on the packing pressure in the injection molding batch process.

To cope with the control problem of industrial processes with partial actuator failure and disturbance, an improved synthetic minmax optimization design-based H∞linear quadratic (LQ) tracking control strategy is presented in this paper. A new state space model that integrates the dynamics of the process states and the tracking error is first formulated as the basic dynamic process representation. By introducing the new state space model formulation, a new minmax optimization of the H∞norm of the system performance is presented for LQ tracking control design, where more degrees of freedom are offered through the simultaneous adjusting of the tracking dynamics and the state regulation in the cost function, resulting in the enhanced control performance over traditional H∞LQ tracking control. Meanwhile, the nominal closed-loop system stability and robustness under uncertainty are discussed and the monotonicity conditions that are sufficient for both nominal and robust system stability are derived. The case studies on an injection molding process and a nonlinear batch reactor under partial actuator failures and disturbance show the effectiveness of the proposed strategy.

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

To cope with control issues of the networked control system under packet losses and uncertainty, an improved state space model-based linear quadratic (LQ) control is proposed in this paper. By extending the state vector with the set-point tracking error, the novel state space model provides more degrees of freedom for the relevant controller design through adjusting the corresponding weighting coefficients of the state variables and tracking error separately. Under such advantages, the proposed LQ scheme provides improved control performance compared with conventional LQ strategy in which only the original state variables can be weighted. A case study on the temperature regulation of an industrial coke furnace under uncertainty and packet losses is introduced to verify the effectiveness of the proposed control scheme in comparison with conventional LQ strategy.

In this paper, an improved linear quadratic tracking control strategy using a new receding horizon expanded state space (ESS) model is proposed. Unlike traditional state space model, the new ESS model first facilitates the combination of the system state variables and tracking errors, then a subsequent new receding horizon improved linear quadratic tracking control (RHLQTC) is designed with more degrees of freedoms for the adjustment of control parameters, which yields improved control performance compared with traditional RHLQTC. Finally, simulations on a typical reverse process are done in comparison with traditional RHLQTC in terms of both servo and regulatory performance, where results show that the proposed controller provides improved performance under both situations.

A novel adaptive energy management strategy is proposed for real-time power split between fuel cells (FCs) and supercapacitors (SCs) in a hybrid electric vehicle in view of the fact that driving patterns greatly affect fuel economy. The driving pattern recognition (DPR) is achieved based on the features extracted from the historical velocity window with a multilayer perceptron neural network. After the DPR has been obtained, an adaptive fuzzy energy management controller is utilized for power split according to the required power for vehicle running. In order to prolong the FC lifetime while decreasing the hydrogen consumption, a genetic algorithm is applied to optimize critical factors such as adaptive gains and fuzzy membership function parameters for several standard driving cycles. In the proposed method, the future driving cycles are not required and the current driving pattern can be successfully recognized, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions. Compared with conventional energy management systems, the proposed framework can ensure the state of charge of SCs within the desired limit.

To cope with the control of batch processes under uncertainty, a novel two-dimensional (2-D) predictive functional iterative learning control (ILC) scheme is developed. By introducing a new model formulation and error compensation approach, the proposed strategy in which predictive functional control and ILC are combined through the 2-D framework compensates for the influence caused by various uncertainty gradually between cycles in the batch processes. Meanwhile, the improved state-space model is also employed effectively to enhance the control performance. Through the independent weighting on the state variables and the set-point tracking error in the performance index, additional degrees of freedom can be offered for the controller design. The effectiveness of the proposed 2-D method is tested on the injection velocity regulation in an injection molding process.

The combination of the fuel cell (FC) and supercapacitor for a hybrid electric vehicle (HEV) has the benefit of compensating for the slow dynamic response and avoiding reactant starvation of FC. Energy management system (EMS) is critical to HEV and a fuzzy controller plus low-pass filter is proposed to prolong the FC lifetime and decrease the hydrogen consumption. The constrained biobjective optimization problem for fuzzy EMS is then solved by an improved genetic algorithm (GA), where the decimal and rule base encoding, constraint handling, the pruning and maintain operator are designed to optimize both the fuzzy rule base and the parameters of the membership functions. Simulation results of highway fuel economy certification test, urban dynamometer driving schedule, and new European drive cycle illustrate that the proposed approach can smooth the output of FC with robustness and be implemented in real time, which decreases 19% current variation with about 10% increase of H2consumption.

This paper proposes the implementation of hybrid data driven modeling and predictive functional control (PFC) strategy for regulation of oxygen in an industrial coke furnace. A comprehensive model that incorporates simple step-response test and nonlinear optimization using neural network is first developed through process operation data. Then, a nonlinear PFC is designed to improve the dynamic response and steady operation. The proposed PFC overcomes the disadvantages of proportional-integral-derivative or linear advance control strategies because the developed process model yields better process dynamics prediction and facilitates the subsequent PFC controller to improve process operation. In addition, the linear iterative form of the controller design is implemented such that engineers can easily apply it to industrial processes. Results are shown by way of simulations and experimental tests to demonstrate the effectiveness of the proposed strategy.

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

In this study, a predictive-p control system is developed for the level process in an industrial coke fractionation tower. Such processes typically have integrating and nonlinear dynamics causing the performance of conventional control designs and tuning to be poor or to require significant effort in practice. The process model is derived using data of step-response test and control implementation is designed through a new state-space structure. The closed-loop control system contains both the improved predictive control and P control. The performance of the proposed control for regulatory/servo, disturbance rejection, and measurement noise problems are studied and the obtained results show that the control system is of simple implementation with more robustness and provides better responses than conventional predictive control.

This paper proposes an enhanced model predictive control (MPC) using a new state space structure for temperature control of an industrial coke furnace. The advantage of the proposed controller lies in the fact that its implementation only requires a simple step-response process model, whereas controller design can be based on state space formulation to improve temperature regulation. To ensure control performance effectiveness under model/process mismatch and uncertainties, model predictions and the cost function optimization are done on the basis of a new improved state space model. The proposed MPC is applied to an industrial coke furnace, where the outlet temperature in the radiation room is regulated. Simulation comparisons with traditional state space MPC are illustrated first. Then experimental results are shown in comparison with the original proportional-integral differential (PID) controller, demonstrating the effectiveness of the proposed methodology.