@article{scholars15962,
            year = {2021},
         journal = {Chinese Journal of Chemical Engineering},
       publisher = {Materials China},
           pages = {253--265},
          volume = {29},
            note = {cited By 26},
             doi = {10.1016/j.cjche.2020.08.035},
           title = {Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework},
          author = {Nawaz, M. and Maulud, A. S. and Zabiri, H. and Taqvi, S. A. A. and Idris, A.},
            issn = {10049541},
        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 T2 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. {\^A}{\copyright} 2020 The Chemical Industry and Engineering Society of China, and Chemical Industry Press},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097665054&doi=10.1016\%2fj.cjche.2020.08.035&partnerID=40&md5=4af3de63bb0dac758ecb5519eb1a0486},
        keywords = {Accident prevention; Chemical industry; Monitoring; Process control; Process monitoring, Comparative studies; Complex chemicals; Detection approach; Detection framework; Exponentially weighted moving average control charts; Monitoring techniques; Multi-scale principal component analysis; Square prediction errors, Fault detection}
}