Mingyi Yang

To address the nonlinear dynamics and strong multivariate coupling inherent in complex industrial data, while overcoming the high computational costs and deployment challenges of deep learning, this paper proposes a Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS). First, a Channel Independence (CI) strategy is introduced: by constructing physically isolated feature channels, multivariate inputs are orthogonally decomposed, enabling the model to mine the intrinsic temporal evolutionary patterns of each variable. Building upon this, enhancement nodes are constructed using Radial Basis Functions (RBFs) to capture nonlinear dynamics; moreover, RBF cluster centers are reused as graph anchors to design an efficient manifold regularization algorithm. This algorithm embeds the intrinsic geometric structure of the data into the learning objective via reduced rank approximation, thereby guiding output weights to explicitly reconstruct spatial coupling relationships while preserving manifold consistency. Experimental results on the IndPenSim process demonstrate that CI-GBLS effectively balances prediction accuracy and efficiency. It completes training within seconds, validating its effectiveness for complex time-series data and offering an efficient solution for real-time, high-precision industrial modeling.

Traditional online monitoring systems often rely on high‐quality data samples or external expert knowledge, and their model accuracy degrades significantly when equipment states deviate from expected conditions over time. In the preresearch stage of mechanical equipment, researchers face not only data scarcity and severe class imbalance but also performance degradation caused by nonstationary processes and varying operational conditions. To address these challenges, this paper proposes a novel fusion‐enhanced model based on local neighborhood standardization and recursive quadratic kernel entropy component analysis. The proposed LNS‐RQKECA framework constructs a data‐driven vibration signal analysis model for early fault detection in mechanical equipment under multiple load conditions, relying solely on historical data from normal operation without requiring external experience or prior knowledge. The local neighborhood standardization technique transforms the multiload problem into a single‐modal problem, enabling effective load‐case fusion. To counteract the feature dilution effect induced by sample fusion, the proposed quadratic kernel entropy component analysis employs advanced kernel‐based entropy optimization to enhance feature discriminability through deeper data mining. Additionally, a recursive updating mechanism ensures adaptive model adjustment, allowing the system to handle nonstationary load conditions dynamically. This proposed model is validated using both public datasets and real‐world operational data. Experimental results demonstrate that under multiload conditions, the proposed model achieves strong generalization capability and nonstationary process adaptability with minimal training data. Compared with the traditional method, the false missing rate and false alarm rate are reduced by 12% and 15%, respectively.

In industrial processes, accurate quality prediction is essential for optimizing operations and ensuring product consistency. However, the inherent complexity of industrial processes typically involves a large number of monitoring variables, accompanied by significant challenges such as high nonlinearity and data redundancy. To address this issue, this article proposes the broad learning system combined with feature-weighted stacked target-related Laplacian autoencoder (BLS-FW-STLapAE). The proposed model innovatively incorporates hidden-layer features extracted by the stacked target-related Laplacian autoencoder (STLapAE) into the feature nodes of the broad learning system (BLS). This approach addresses the issue of insufficient feature extraction caused by random mapping in BLS. This integration enables the model to capture both local geometric structures and key target-related features, significantly enhancing predictive performance in complex industrial environments. In addition, mutual information (MI) is introduced in STLapAE to evaluate the correlation between features and the target variable, further improving the model’s ability to capture relevant information about the target variable and ensuring that all relevant information is effectively utilized. Finally, the feasibility and practical value of the proposed algorithm were validated on benchmark public datasets and two real-world industrial applications: penicillin fermentation and solid propellant manufacturing. The superior predictive performance demonstrated across these tests enables operators to proactively adjust process parameters and prevent quality deviations, thereby significantly improving production safety and reducing operational costs in such high-risk chemical engineering domains.

Aiming at the problems of spatial coupling between process parameters and time dependence in actual industrial processes, which lead to insufficient reliability and poor timeliness of process anomaly monitoring, a predictive model of time series graph slicing networks based on dynamic multi-temporal features is proposed, which can effectively reduce false alarms and omissions in process anomalies, and explain the causes of anomalies at the same time. First, a method for extracting and building graphs of multidimensional spatio-temporally coupled time series data based on data dimensionality reduction method and causality calculation is designed to mine the potential associations between strongly coupled process parameters. Second, a dynamic multi-temporal feature graph embedding strategy and a two-channel graph structure learning network are proposed to effectively solve the problem that individual time-dimension features cannot fully utilize the continuity and sequentiality in the time dimension, and to realize the long-time dynamic perception of global variables.Finally, the prediction performance of the proposed method as well as the anomaly monitoring performance is validated using the time series dataset generated by the Matlab-based Tennessee Eastman (TE) process platform as well as the SWaT process dataset.The experimental results show that the proposed model has an average fitting degree of more than 0.99 for the prediction of target variables, which can identify and explain the anomalies more quickly and accurately compared with other typical methods, and significantly improves the reliability and timeliness of the anomaly monitoring of industrial processes.

Aiming at the strong spatio-temporal coupling relationship between data in the actual industrial production process, which leads to the problem of insufficient reliability and poor timeliness of traditional process anomaly monitoring methods, a time series anomaly monitoring model based on the graph similarity network with multi-scale features is proposed, which can react to the anomalies in the process in a timely and effective manner to guarantee production safety. First, a graph-building method for spatio-temporally coupled time-series data using multidimensional time-varying feature map embedding is designed to capture the dependence of the data on time, while the topology of the graph is utilized to learn the spatial coupling of the data; second, a graph similarity-based anomaly monitoring strategy is innovatively proposed to measure the anomalies of the process using the difference degree index between the standard normal process data and the monitoring data. Finally, the proposed method is validated using the standard normal operating condition data of the Tennessee-Eastman (TE) process as well as the standard fault data. The experimental results show that the proposed model can identify anomalies more quickly and accurately than other typical methods, which significantly improves the reliability and timeliness of industrial process anomaly monitoring.

Aiming at the strong spatio-temporal coupling relationship between data in the actual industrial production process, which leads to the problem of insufficient reliability and poor timeliness of traditional process anomaly monitoring methods, a time series anomaly monitoring model based on the graph similarity network with multi-scale features is proposed, which can react to the anomalies in the process in a timely and effective manner to guarantee the production safety.First, a graph-building method for spatio-temporally coupled time-series data using multidimensional time-varying feature map embedding is designed to capture the data’s dependence on time, while the topology of the graph is utilized to learn the spatial coupling of the data; second, a graph similarity-based anomaly monitoring strategy is innovatively proposed to measure the anomalies of the process using the difference degree index between the standard normal process data and the monitoring data. Finally, the proposed method is validated using the standard normal operating condition data of the Tennessee-Eastman (TE) process as well as the standard fault data. The experimental results show that the proposed model can identify anomalies more quickly and accurately than other typical methods, which significantly improves the reliability and timeliness of industrial process anomaly monitoring.

Single-step tightening is simple and fast compared to multi-step tightening, and it is therefore frequently used during the tightening of bolts in flange structures. However, single-step tightening to maintain uniformity is more difficult to achieve. To address this problem, existing methods such as the Elastic Interaction Coefficient Method (EICM) and Tetraparametric Assembly Method (TAM) have been investigated for load uniformity in single-step tightening. In order to improve the computational efficiency and accuracy of the traditional methods, a new single-step tightening method is proposed in this paper. This method can realize the design of the initial preload force simply by measuring the change in bolt load in a specific sequence. It is verified by numerical simulation that this method can realize the uniform distribution of bolt load. In addition, this paper will provide suggestions for the optimal tightening sequence for the single-step tightening method.

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms.