Quality-Oriented Dynamic Sparse Latent Variable Detection Approach for Industrial IoT Based on Joint Spatial and Temporal Decomposition

Quality-oriented fault detection facilitates intelligent inspection, operation optimization, and product quality improvement in the industrial Internet of Things. Data-driven latent variable-based approaches are among the mainstream methods in this field. However, traditional latent variable methods establish static relational models, which are less suited to the dynamic characteristics of industrial processes. To address this issue, dynamic latent variable methods, such as dynamic inner partial least squares (DiPLS), have been proposed. Despite this, the DiPLS method only enhances the descriptive capability of the partial least squares (PLS) method for dynamic characterization and does not overcome the inherent limitations of the PLS method in quality-oriented fault detection. Specifically, the residual subspace obtained by the PLS still contains quality-related information, which undoubtedly increases the false alarm rates and missed alarm rates in subsequent detection. Additionally, the DiPLS method does not address the problem of overfitting during the modeling process. To deal with the above issues, this article proposes a quality-oriented dynamic sparse latent variable method for efficient fault detection with respect to quality indicators. This method introduces a weight matrix to sparsity the coefficient matrix, enhancing the interpretability of the model while addressing the overfitting problem that may occur during the DiPLS modeling process. Furthermore, an alternating direction method of multipliers-based technology is studied to solve the corresponding optimization problem. To further refine the orthogonal decomposition of the process variable space, a joint temporal and spatial decomposition strategy is presented. This strategy accomplishes the orthogonal decomposition of the process variable space based on latent variable and time lag components, extracting the latent variables accordingly. Subsequently, the detection statistics and logic are derived using the Bayesian fusion. Finally, the effectiveness of the proposed method is verified through a numerical simulation and two industrial cases.