{"title":"智能校准和监测:利用人工智能改进基于 MEMS 的惯性传感器校准","authors":"Itilekha Podder, Tamas Fischl, Udo Bub","doi":"10.1007/s40747-024-01531-y","DOIUrl":null,"url":null,"abstract":"<p>Micro-electro-mechanical systems (MEMS)-based sensors endure complex production processes that inherently include high variance. To meet rigorous client demands (such as sensitivity, offset noise, robustness against vibration, etc.). products must go through comprehensive calibration and testing procedures. All sensors undergo a standardized and sequential calibration process with a predetermined number of steps, even though some may reach the correct calibration value sooner. Moreover, the traditional sequential calibration method faces challenges due to specific operating conditions resulting from manufacturing discrepancies. This not only extends the calibration duration but also introduces rigidity and inefficiency. To tackle the issue of production variances and elongated calibration time and enhance efficiency, we provide a novel quasi-parallelized calibration framework aided by an artificial intelligence (AI) based solution. Our suggested method utilizes a supervised tree-based regression technique and statistical measures to dynamically identify and optimize the appropriate working point for each sensor. The objective is to decrease the total calibration duration while ensuring accuracy. The findings of our investigation show a time reduction of 23.8% for calibration, leading to substantial cost savings in the manufacturing process. In addition, we propose an end-to-end monitoring system to accelerate the incorporation of our framework into production. This not only guarantees the prompt execution of our solution but also enables the identification of process modifications or data irregularities, promoting a more agile and adaptable production process.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart calibration and monitoring: leveraging artificial intelligence to improve MEMS-based inertial sensor calibration\",\"authors\":\"Itilekha Podder, Tamas Fischl, Udo Bub\",\"doi\":\"10.1007/s40747-024-01531-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Micro-electro-mechanical systems (MEMS)-based sensors endure complex production processes that inherently include high variance. To meet rigorous client demands (such as sensitivity, offset noise, robustness against vibration, etc.). products must go through comprehensive calibration and testing procedures. All sensors undergo a standardized and sequential calibration process with a predetermined number of steps, even though some may reach the correct calibration value sooner. Moreover, the traditional sequential calibration method faces challenges due to specific operating conditions resulting from manufacturing discrepancies. This not only extends the calibration duration but also introduces rigidity and inefficiency. To tackle the issue of production variances and elongated calibration time and enhance efficiency, we provide a novel quasi-parallelized calibration framework aided by an artificial intelligence (AI) based solution. Our suggested method utilizes a supervised tree-based regression technique and statistical measures to dynamically identify and optimize the appropriate working point for each sensor. The objective is to decrease the total calibration duration while ensuring accuracy. The findings of our investigation show a time reduction of 23.8% for calibration, leading to substantial cost savings in the manufacturing process. In addition, we propose an end-to-end monitoring system to accelerate the incorporation of our framework into production. This not only guarantees the prompt execution of our solution but also enables the identification of process modifications or data irregularities, promoting a more agile and adaptable production process.</p>\",\"PeriodicalId\":10524,\"journal\":{\"name\":\"Complex & Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complex & Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s40747-024-01531-y\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-024-01531-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Smart calibration and monitoring: leveraging artificial intelligence to improve MEMS-based inertial sensor calibration
Micro-electro-mechanical systems (MEMS)-based sensors endure complex production processes that inherently include high variance. To meet rigorous client demands (such as sensitivity, offset noise, robustness against vibration, etc.). products must go through comprehensive calibration and testing procedures. All sensors undergo a standardized and sequential calibration process with a predetermined number of steps, even though some may reach the correct calibration value sooner. Moreover, the traditional sequential calibration method faces challenges due to specific operating conditions resulting from manufacturing discrepancies. This not only extends the calibration duration but also introduces rigidity and inefficiency. To tackle the issue of production variances and elongated calibration time and enhance efficiency, we provide a novel quasi-parallelized calibration framework aided by an artificial intelligence (AI) based solution. Our suggested method utilizes a supervised tree-based regression technique and statistical measures to dynamically identify and optimize the appropriate working point for each sensor. The objective is to decrease the total calibration duration while ensuring accuracy. The findings of our investigation show a time reduction of 23.8% for calibration, leading to substantial cost savings in the manufacturing process. In addition, we propose an end-to-end monitoring system to accelerate the incorporation of our framework into production. This not only guarantees the prompt execution of our solution but also enables the identification of process modifications or data irregularities, promoting a more agile and adaptable production process.
期刊介绍:
Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.