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

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 T2 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.