Print Email Facebook Twitter Fault detection and diagnosis to enhance safety in digitalized process system Title Fault detection and diagnosis to enhance safety in digitalized process system Author Kopbayev, Alibek (Memorial University of Newfoundland) Khan, Faisal (Texas A and M University) Yang, M. (TU Delft Safety and Security Science) Halim, S. Zohra (Texas A and M University) Date 2022 Abstract The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety. Subject Deep Neural NetworksFault detection and diagnosisHybrid modelkPCAProcess system safety To reference this document use: http://resolver.tudelft.nl/uuid:c9a2de2b-1897-4daf-91ff-7c0abe99757a DOI https://doi.org/10.1016/j.compchemeng.2021.107609 Embargo date 2022-05-22 ISSN 0098-1354 Source Computers & Chemical Engineering, 158 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 Alibek Kopbayev, Faisal Khan, M. Yang, S. Zohra Halim Files PDF 1_s2.0_S0098135421003872_main.pdf 1.63 MB Close viewer /islandora/object/uuid:c9a2de2b-1897-4daf-91ff-7c0abe99757a/datastream/OBJ/view