Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach
The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6370 |
| _version_ | 1797527702645768192 |
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| author | Elena Quatrini Francesco Costantino David Mba Xiaochuan Li Tat-Hean Gan |
| author_facet | Elena Quatrini Francesco Costantino David Mba Xiaochuan Li Tat-Hean Gan |
| author_sort | Elena Quatrini |
| collection | DOAJ |
| description | The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context. |
| first_indexed | 2024-03-10T09:47:31Z |
| format | Article |
| id | doaj.art-fbce5578bdb4457bb4fabf404617f4f7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:47:31Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fbce5578bdb4457bb4fabf404617f4f72023-11-22T03:08:32ZengMDPI AGApplied Sciences2076-34172021-07-011114637010.3390/app11146370Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary ApproachElena Quatrini0Francesco Costantino1David Mba2Xiaochuan Li3Tat-Hean Gan4Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, ItalyFaculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UKFaculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UKCollege of Engineering, Design and Physical Sciences, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UKThe water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.https://www.mdpi.com/2076-3417/11/14/6370kernel principal component analysisnonlinear systempharmaceutical processfault detectioncondition-based maintenance |
| spellingShingle | Elena Quatrini Francesco Costantino David Mba Xiaochuan Li Tat-Hean Gan Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach Applied Sciences kernel principal component analysis nonlinear system pharmaceutical process fault detection condition-based maintenance |
| title | Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach |
| title_full | Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach |
| title_fullStr | Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach |
| title_full_unstemmed | Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach |
| title_short | Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach |
| title_sort | monitoring a reverse osmosis process with kernel principal component analysis a preliminary approach |
| topic | kernel principal component analysis nonlinear system pharmaceutical process fault detection condition-based maintenance |
| url | https://www.mdpi.com/2076-3417/11/14/6370 |
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