Control Method of Cold and Hot Shock Test of Sensors in Medium
In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6536 |
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| author | Jinming Tian Yue Zeng Linhai Ji Huimin Zhu Zu Guo |
| author_facet | Jinming Tian Yue Zeng Linhai Ji Huimin Zhu Zu Guo |
| author_sort | Jinming Tian |
| collection | DOAJ |
| description | In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and −40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test. |
| first_indexed | 2024-03-11T00:40:04Z |
| format | Article |
| id | doaj.art-681a77122b1f4bd7a250fb44538cb0b2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:04Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-681a77122b1f4bd7a250fb44538cb0b22023-11-18T21:19:06ZengMDPI AGSensors1424-82202023-07-012314653610.3390/s23146536Control Method of Cold and Hot Shock Test of Sensors in MediumJinming Tian0Yue Zeng1Linhai Ji2Huimin Zhu3Zu Guo4School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222000, ChinaIn order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and −40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test.https://www.mdpi.com/1424-8220/23/14/6536environmental test chambercold and hot shock testK-meanssupport vector machineneural networksensor test |
| spellingShingle | Jinming Tian Yue Zeng Linhai Ji Huimin Zhu Zu Guo Control Method of Cold and Hot Shock Test of Sensors in Medium Sensors environmental test chamber cold and hot shock test K-means support vector machine neural network sensor test |
| title | Control Method of Cold and Hot Shock Test of Sensors in Medium |
| title_full | Control Method of Cold and Hot Shock Test of Sensors in Medium |
| title_fullStr | Control Method of Cold and Hot Shock Test of Sensors in Medium |
| title_full_unstemmed | Control Method of Cold and Hot Shock Test of Sensors in Medium |
| title_short | Control Method of Cold and Hot Shock Test of Sensors in Medium |
| title_sort | control method of cold and hot shock test of sensors in medium |
| topic | environmental test chamber cold and hot shock test K-means support vector machine neural network sensor test |
| url | https://www.mdpi.com/1424-8220/23/14/6536 |
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