{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T15:45:34Z","timestamp":1771861534182,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qatar National Research Fund","award":["NPRP9-330-2-140"],"award-info":[{"award-number":["NPRP9-330-2-140"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.<\/jats:p>","DOI":"10.3390\/s22155564","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T04:59:16Z","timestamp":1658897956000},"page":"5564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4398-6423","authenticated-orcid":false,"given":"Byanne","family":"Malluhi","sequence":"first","affiliation":[{"name":"Chemical Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar"}]},{"given":"Hazem","family":"Nounou","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar"}]},{"given":"Mohamed","family":"Nounou","sequence":"additional","affiliation":[{"name":"Chemical Engineering Program, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.cjche.2020.08.035","article-title":"Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework","volume":"29","author":"Nawaz","year":"2021","journal-title":"Chin. J. Chem. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lachouri, A., Baiche, K., Djeghader, R., Doghmane, N., and Oulitati, S. (2008, January 7\u201311). Analyze and fault diagnosis by multi-scale PCA. 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