TY  - JOUR
PB  - American Chemical Society
SN  - 24701343
EP  - 9512
AV  - none
N1  - cited By 10
SP  - 9496
TI  - Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis
Y1  - 2022///
JF  - ACS Omega
A1  - Ali, H.
A1  - Maulud, A.S.
A1  - Zabiri, H.
A1  - Nawaz, M.
A1  - Suleman, H.
A1  - Taqvi, S.A.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126618204&doi=10.1021%2facsomega.1c06839&partnerID=40&md5=6ab8fdb9682fc482cc58da920444c016
VL  - 7
IS  - 11
N2  - The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures. © 2022 The Authors. Published by American Chemical Society.
ID  - scholars16968
ER  -