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Identification of defected sensors in an array of amperometric gas sensors
Purpose Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of...
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Published in: | Sensor review 2022-03, Vol.42 (2), p.195-203 |
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creator | Dmitrzak, Marta Kalinowski, Pawel Jasinski, Piotr Jasinski, Grzegorz |
description | Purpose
Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.
Design/methodology/approach
In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.
Findings
The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response.
Originality/value
This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed. |
doi_str_mv | 10.1108/SR-10-2021-0348 |
format | article |
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Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.
Design/methodology/approach
In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.
Findings
The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response.
Originality/value
This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.</description><identifier>ISSN: 0260-2288</identifier><identifier>EISSN: 1758-6828</identifier><identifier>DOI: 10.1108/SR-10-2021-0348</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Accuracy ; Air monitoring ; Air quality ; Business metrics ; Damage detection ; Datasets ; Electrical measurement ; Failure ; Fault diagnosis ; Gas sensors ; Gases ; Humidity ; Performance evaluation ; Poisoning ; Principal components analysis ; Sensor arrays ; Sensors ; Simulation ; Software ; Sulfur dioxide ; Systems stability</subject><ispartof>Sensor review, 2022-03, Vol.42 (2), p.195-203</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c308t-dc36d64f9c258c533bc64a40a272cb9471b565e439cf315b7aac67fce2bb6fb63</citedby><cites>FETCH-LOGICAL-c308t-dc36d64f9c258c533bc64a40a272cb9471b565e439cf315b7aac67fce2bb6fb63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2636245737?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,27901,27902,36037,44339</link.rule.ids></links><search><creatorcontrib>Dmitrzak, Marta</creatorcontrib><creatorcontrib>Kalinowski, Pawel</creatorcontrib><creatorcontrib>Jasinski, Piotr</creatorcontrib><creatorcontrib>Jasinski, Grzegorz</creatorcontrib><title>Identification of defected sensors in an array of amperometric gas sensors</title><title>Sensor review</title><description>Purpose
Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.
Design/methodology/approach
In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.
Findings
The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response.
Originality/value
This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.</description><subject>Accuracy</subject><subject>Air monitoring</subject><subject>Air quality</subject><subject>Business metrics</subject><subject>Damage detection</subject><subject>Datasets</subject><subject>Electrical measurement</subject><subject>Failure</subject><subject>Fault diagnosis</subject><subject>Gas sensors</subject><subject>Gases</subject><subject>Humidity</subject><subject>Performance evaluation</subject><subject>Poisoning</subject><subject>Principal components analysis</subject><subject>Sensor arrays</subject><subject>Sensors</subject><subject>Simulation</subject><subject>Software</subject><subject>Sulfur dioxide</subject><subject>Systems stability</subject><issn>0260-2288</issn><issn>1758-6828</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNptkM1LAzEQxYMoWKtnrwue0-Y76VGKnxSEVs8hyU5kS3dTk-2h_727VA-CMDAD77158EPolpIZpcTMN2tMCWaEUUy4MGdoQrU0WBlmztGEMDWIzJhLdFXKlhDKhOIT9PpSQ9c3sQmub1JXpVjVECH0UFcFupJyqZqucsPk7I6j7to95NRCn5tQfbry67tGF9HtCtz87Cn6eHx4Xz7j1dvTy_J-hQMnpsd14KpWIi4CkyZIzn1QwgnimGbBL4SmXioJgi9C5FR67VxQOgZg3qvoFZ-iu9PffU5fByi93aZD7oZKyxRXTEjN9eCan1whp1IyRLvPTevy0VJiR2B2sx7PEZgdgQ2J2SkBLWS3q_8J_CHMvwH7Umvi</recordid><startdate>20220308</startdate><enddate>20220308</enddate><creator>Dmitrzak, Marta</creator><creator>Kalinowski, Pawel</creator><creator>Jasinski, Piotr</creator><creator>Jasinski, Grzegorz</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7SP</scope><scope>7TB</scope><scope>7U5</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>L7M</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope></search><sort><creationdate>20220308</creationdate><title>Identification of defected sensors in an array of amperometric gas sensors</title><author>Dmitrzak, Marta ; Kalinowski, Pawel ; Jasinski, Piotr ; Jasinski, Grzegorz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-dc36d64f9c258c533bc64a40a272cb9471b565e439cf315b7aac67fce2bb6fb63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Air monitoring</topic><topic>Air quality</topic><topic>Business metrics</topic><topic>Damage detection</topic><topic>Datasets</topic><topic>Electrical measurement</topic><topic>Failure</topic><topic>Fault diagnosis</topic><topic>Gas sensors</topic><topic>Gases</topic><topic>Humidity</topic><topic>Performance evaluation</topic><topic>Poisoning</topic><topic>Principal components analysis</topic><topic>Sensor arrays</topic><topic>Sensors</topic><topic>Simulation</topic><topic>Software</topic><topic>Sulfur dioxide</topic><topic>Systems stability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dmitrzak, Marta</creatorcontrib><creatorcontrib>Kalinowski, Pawel</creatorcontrib><creatorcontrib>Jasinski, Piotr</creatorcontrib><creatorcontrib>Jasinski, Grzegorz</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ABI商业信息数据库</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ABI/INFORM global</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Sensor review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dmitrzak, Marta</au><au>Kalinowski, Pawel</au><au>Jasinski, Piotr</au><au>Jasinski, Grzegorz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of defected sensors in an array of amperometric gas sensors</atitle><jtitle>Sensor review</jtitle><date>2022-03-08</date><risdate>2022</risdate><volume>42</volume><issue>2</issue><spage>195</spage><epage>203</epage><pages>195-203</pages><issn>0260-2288</issn><eissn>1758-6828</eissn><abstract>Purpose
Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.
Design/methodology/approach
In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.
Findings
The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response.
Originality/value
This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/SR-10-2021-0348</doi><tpages>9</tpages></addata></record> |
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source | ABI/INFORM global; Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list) |
subjects | Accuracy Air monitoring Air quality Business metrics Damage detection Datasets Electrical measurement Failure Fault diagnosis Gas sensors Gases Humidity Performance evaluation Poisoning Principal components analysis Sensor arrays Sensors Simulation Software Sulfur dioxide Systems stability |
title | Identification of defected sensors in an array of amperometric gas sensors |
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