Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Muhammad Nawaz; Abdulhalim Shah Maulud; Haslinda Zabiri; Syed Ali Ammar Taqvi; Alamin Idris

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Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework
- Chinese Journal of Chemical Engineering Vol. 29, Issue 1, Pages: 253-265(2021)
- Affiliations：
  
  1. Department of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar,Perak,Malaysia,32610
  2. Centre of Contaminant Control&Utilization(CenCoU), Universiti Teknologi PETRONAS, Bandar Seri Iskandar,Perak,Malaysia,32610
  3. Department of Chemical Engineering, NED University of Engineering&Technology,Karachi,Pakistan,75270
  4. Department of Chemical and Engineering Sciences, Karlstad University, Karlstad,Sweden
- Author bio：
- Funds：
- DOI：
  CLC：
- Published：2021
- Accepted：
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Muhammad Nawaz, Abdulhalim Shah Maulud, Haslinda Zabiri, Syed Ali Ammar Taqvi, Alamin Idris. Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework[J]. Chinese Journal of Chemical Engineering, 2021, 29(1): 253-265.
DOI：

Muhammad Nawaz, Abdulhalim Shah Maulud, Haslinda Zabiri, Syed Ali Ammar Taqvi, Alamin Idris. Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework[J]. Chinese Journal of Chemical Engineering, 2021, 29(1): 253-265. DOI：

摘要

Abstract

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes

and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring

an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's

and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover

a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower

values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.

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