Hybrid Intelligent Fault Diagnosis Model Based on Improved MPCA-V for Sensors in a Laboratory-Scale Wastewater Treatment Process

Abstract

The study objective is to propose a hybrid fault diagnosis method for a laboratory-scale sequential batch reactor (SBR) wastewater treatment process based on time-varying covariance and variable-wise unfolded MPCA method (MPCA-V), which can detect the fault batch, determine the fault time simultaneously, and further identify the fault source. To establish and validate the MPCA-V model, 50 normal batches and 55 batches including 7 fault batches were employed separately. Furthermore, the classical MPCA (MPCA-B) model was introduced for comparison. For the three detected fault batches, with the MPCA-V model, not only the fault occurring time and fault source were located and identified by the contribution degree calculation of each variable to the T 2 and SPE statistics simultaneously but also the fault detection rate was averaged as 90%, which was much higher than that of MPCA-B (67%). Introducing time dependency and correlation in a laboratory-scale SBR process gives the work practical significance and breakthrough.