Fault Detection and Diagnosis using Reconstruction-based DiGLPP: Application to Industrial Distillation System

The rapid advancement of Industry 4.0, artificial intelligence, and big data sensor technologies has made industrial systems highly complex and dynamic. Classical fault detection and diagnosis (FDD) techniques depend on insufficient information and variables with equivalent uncertainty. This paper introduced an advanced dynamic inner reconstruction-based contribution with global-local preservation projection (DiGLPP-RBC) for fault detection and diagnosis. Firstly, inner data statistics are extracted to develop an augmented matrix, which is used to characterize the dynamic latent variable using the DiGLPP framework. Secondly, reconstruction-based contribution (RBC) is used to determine fault contribution. The proposed method employs Hotelling’s T 2 and squared prediction error ( SPE ) to detect and diagnose variable contributions and kernel density of faults in the ethanol-water industrial distillation system. The proposed framework’s robustness is compared with traditional baseline frameworks such as dynamic inner principal component analysis (DiPCA) and bi-directional long short-term memory-autoencoder (BiLSTM-AE). The results indicate that the DiGLPP-RBC technique detects, identifies, and diagnoses irregularities and faults more effectively and reliably than traditional approaches.