Chuangyan Yang

A novel dynamic inner manifold learning algorithm based on forward selection component variables (FSV-DiML) is proposed for industrial process monitoring. The method begins with forward selection component analysis (FSCA) to select monitoring variables, effectively removing redundancies while preserving critical information. DiML is then used to construct a new objective function designed to extract dynamic latent variables with manifold structures from high-dimensional and nonlinear data. This approach enables the separation of process data into dynamic and static subspaces. However, since high-dimensional data often contains low-dimensional manifold structures, neighborhood preserving embedding (NPE) is applied to reduce the dimensionality of each subspace while preserving its manifold structure. Finally, Bayesian inference and fusion techniques are employed to merge various monitoring statistics into a unified monitoring index, further improving fault detection performance. The proposed FSV-DiML model is validated on the Tennessee Eastman and Multiphase Flow Facility processes, demonstrating significant improvements over existing methods in accurately detecting faults in complex industrial processes.

Variational mode decomposition (VMD) is a signal processing technique used to analyze bearing faults by breaking down signals into simpler components. While effective, traditional VMD requires users to manually set key parameters—such as the initial center frequency $\omega ^{1}$, the penalty parameter $\alpha$, and the number of modes $K$—which, if improperly selected, can obscure fault features or reduce analytical clarity. To overcome these limitations, we propose a parametric self-contained VMD (PSVMD) method. Specifically, $\omega ^{1}$ is automatically determined based on the narrowband features of the signal, removing the need for manual selection. Furthermore, adaptive strategies are introduced to estimate both $\alpha$ and $K$ based on the intrinsic properties of the decomposed modes, thereby enhancing accuracy and robustness. Building on PSVMD, we also propose a novel correlation-kurtosis index to effectively distinguish fault-related features from noise. Simulation results and real-world bearing fault data validate the effectiveness and superiority of the proposed method.

Temperature prediction in the tin chemical process (TCP) is crucial for implementing production process monitoring and predictive control. However, TCP’s discrete-continuous hybrid structure, incorporating both binary and continuous variables, poses challenges for traditional prediction models. To overcome these limitations, this study introduces a TCP temperature prediction approach using a trapezoidal rule (TR) and improved stochastic configuration networks (TR-ISCNs). First, the TR transforms discrete variables into continuous forms. Next, the continuous data are used to train improved stochastic configuration networks (ISCNs) to develop a temperature prediction model. Finally, a TR-ISCNs-based model update framework is implemented, enabling the prediction model to leverage recent window data for real-time parameter updates and temperature forecasts as new data becomes available. The proposed method’s effectiveness is demonstrated through application in a real TCP.

Wind turbine blade icing data has the characteristics of multi-source and multi variable. It is difficult to characterize and identify the icing failure with multi-scale features. In this paper, a novel Non-negative Matrix Factorization-Dynamic-inner Canonical Correlation Analysis (NMF-DiCCA )based on GRU and SSAE algorithm is proposed to solve this problem. Firstly, using NMF instead of SVD decomposition method in DiCCA algorithm, the NMF-DiCCA is applied to obtain the dynamic latent variable of time serie. Secondly, the latent structure features S of dynamic latent variable is captured by SSAE. Thirdly, the temporal correlation hidden feature H of dynamic latent variable is extracted by Gated Recurrent Unit (GRU). Finally, the attention weight distribution between latent structure S and temporal correlation hidden feature H is integrated using the attention mechanism, and the fusion feature is reconstructed using the improved SSAE(ISSAE) based on GRU and SSAE.

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.