簡易檢索 / 詳目顯示

研究生: 王韻捷
Wang, Yun-Chieh
論文名稱: 使用非侵入式檢測方法進行微型PM2.5感測器健康評估之研究
A Non-Intrusive Diagnostic Approach for Low-Cost PM2.5 Sensor Health Assessment
指導教授: 陳伶志
Chen, Ling-Jyh
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 43
中文關鍵詞: 微型感測器PM2.5物聯網老化分析音頻流量
英文關鍵詞: Microsensor, PM2.5, Internet of Things, Aging analysis, Audio frequency, Flow
DOI URL: http://doi.org/10.6345/NTNU202000346
論文種類: 學術論文
相關次數: 點閱:187下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 第一章 緒論 1 第二章 相關文獻探討 4 第一節 感測器老化 4 第二節 感測器校正 5 第三節 品質偵測 7 第三章 研究方法 9 第一節 資料來源 9 第二節 參數收集 10 3.2.1 流量 10 3.2.2 聲音訊號 11 3.2.3 感測值與曝露量 11 3.2.4 硬體介紹與設計 12 第三節 關聯分析 14 3.3.1 對比分析 14 3.3.2 迴歸分析 15 第四節 健康評估方法 16 第五節 預測方法 16 第四章 實驗與結果 18 第一節 共點分析 18 4.1.1 實驗結果 18 第二節 實地訪查分析 20 4.2.1 分析與結果 22 4.2.1.1 基頻與運作時間、曝露量分析 22 4.2.1.2 流量與運作時間、曝露量分析 26 4.2.1.3 結果 28 第三節 健康評估 29 第四節 曝露量預測 32 4.4.1 曝露量預測方法 32 4.4.1 曝露量預測法正確率 34 4.4.3 汰換預警清單 35 第五章 結論與未來展望 37 參考文獻 38 附 錄 43

    [1]LASS(Location Aware Sensing System)是一套開源和公益的「環境感測器網路系統」 https://lass-net.org/
    [2]行政院環保署,空氣品質指標(2019) https://taqm.epa.gov.tw/taqm/tw/b0201.aspx
    [3]W. Jiao, et. al. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensorperformance in a suburban environment in the southeastern United States. Atmospheric Measurement Techniques, 9(11), 5281-5292. 2016.
    [4]K. E. Kelly, et. al. Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491-500, 2017
    [5]S. Nakahara, et. al. Examination of suppression of stray light of small PM2. 5 sensor. In IEEE International Conference on Consumer Electronics, 2017
    [6]空氣品質監測網. https://taqm.epa.gov.tw/taqm/tw/b0905.aspx
    [7]K. Stock. Spectral ageing pattern of handled silicon photodiodes. Measurement, 5(3), 141-144, 1987
    [8]T. C. E. Jones. The long term stability of the response of silicon photodiodes. In IEE Colloquium on Developments in near Infra-Red Visible Ultraviolet Detectors (pp. 6-1). IET, 1991
    [9]R. F. Zarr. Precision method for laser diode emission control. Electronic Design, 52(17), 34-35, 2004
    [10]S. D.Lester, et. al. High dislocation densities in high efficiency GaN-based light-emitting diodes. Applied Physics Letters, 66(10), 1249-1251, 1995
    [11]A. Usui, et. al. Thick GaN epitaxial growth with low dislocation density by hydride vapor phase epitaxy. JAPANESE JOURNAL OF APPLIED PHYSICS PART 2 LETTERS, 36, L899-L902, 1997
    [12]O. Moseler, R. Isermann. Application of model-based fault detection to a brushless DC motor. IEEE Transactions on industrial electronics, 47(5), 1015-1020, 2000
    [13]A. Lewis, P. Edwards. Validate personal air-pollution sensors. Nature News, 535(7610), 29, 2016
    [14]Air Sensor Guidebook. US Environmental Protection Agency http://tdenviro.com/wp-content/uploads/2017/05/AirSensorGuidebook.pdf
    [15]A. C. Rai, et. al. End-user perspective of low-cost sensors for outdoor air pollution monitoring. Science of The Total Environment, 607, 691-705, 2017
    [16]M. Gao, J. Cao, E. Seto. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi’an, China. Environmental pollution, 199, 56-65, 2015
    [17]N. Castell, et. al. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?. Environment international, 99, 293-302, 2017.
    [18]W. Jiao, et. al. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensorperformance in a suburban environment in the southeastern United States. Atmospheric Measurement Techniques, 9(11), 5281-5292. 2016.
    [19]Evaluation of Emerging Air Pollution Sensor Performance https://www.epa.gov/air-sensor-toolbox/evaluation-emerging-air-pollution-sensor-performance
    [20]M. Isaac. Regulatory considerations of lower cost air pollution sensor data performance. Environ. Manage7.4 (2014): 32-37, 2014
    [21]Y. Wang, et. al. Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Science and Technology, 49(11), 1063-1077, 2015
    [22]K. E. Kelly, et. al. Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental
    Pollution, 221, 491-500, 2017
    [23]Y. Wang, et. al. Calibration of a low-cost PM2. 5 monitor using a random forest model. Environment international, 133, 105161, 2019
    [24]D. Park, S.B. Kwon, Y. Cho. Development and calibration of a particulate matter measurement device with wireless sensor network function. International Journal of Environmental Monitoring and Analysis, 1(1), 15-20, 2013
    [25]E. Esposito, et. al. Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems. Sensors and Actuators B: Chemical, 231, 701-713, 2016
    [26]E.S. Cross, et. al. Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements. Atmospheric Measurement Techniques, 10(9), 3575-3588, 2017.
    [27]Z. Al Barakeh, et. al. Development of a normalized multi-sensors system for low cost on-line atmospheric pollution detection. Sensors and Actuators B: Chemical, 241, 1235-1243, 2017
    [28]C.H. Lee, Y.B. Wang, H.L. Yu. An efficient spatiotemporal data calibration approach for the low-cost PM2. 5 sensing network: A case study in Taiwan. Environment international, 130, 104838. 2019
    [29]J. Moon, S. Kum, S. Lee. A Heterogeneous IoT Data Analysis Framework with Collaboration of Edge-Cloud Computing: Focusing on Indoor PM10 and PM2. 5 Status Prediction. Sensors, 19(14), 3038, 2019.
    [30]Z. Idrees, et. al. Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems:A Comprehensive System Analysis, Design Considerations and Development. Sensors, 18(9), 3021, 2018.
    [31]R. Isermann. Supervision, fault-detection and fault-diagnosis methods—an introduction. Control engineering practice, 5(5), 639-652, 1997
    [32]S. Ostlund, et. al. Condition monitoring of pantograph contact strip, 2008
    [33]Y. Hara, et. al. A system for PCB automated inspection using fluorescent light. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(1), 69-78, 1988
    [34]G. A. West. A system for the automatic visual inspection of bare-printed circuit boards. IEEE transactions on systems, man, and cybernetics, (5), 767-773, 1984
    [35]Z. Wang, et. al. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles. Applied energy, 196, 289-302, 2017
    [36] R. L. de Araujo Ribeiro, et. al. Fault detection of open-switch damage in voltage-fed PWM motor drive systems. IEEE Transactions on Power Electronics, 18(2), 587-593, 2003
    [37]S. M. Jung, et. al. An MRAS-based diagnosis of open-circuit fault in PWM voltage-source inverters for PM synchronous motor drive systems. IEEE Transactions on Power Electronics, 28(5), 2514-2526, 2012
    [38]S. J. Lee, et. al. An intelligent and efficient fault location and diagnosis scheme for radial distribution systems. IEEE transactions on power delivery, 19(2), 524-532, 2004
    [39]X. Gong, W. Qiao. Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals.IEEE Transactions on Industrial Electronics, 60(8), 3419-3428, 2013
    [40] B. S. Yang, K. J. Kim. Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20(2), 403-420, 2006
    [41] A. Mendes, A. Cardoso. Voltage source inverter fault diagnosis in variable speed AC drives, by the average current Park’s vector approach. In IEEE International Electric Machines and Drives Conference, 1999
    [42] H. Ma, L. Wang. Fault diagnosis and failure prediction of aluminum electrolytic capacitors in power electronic converters. In Annual Conference of IEEE Industrial Electronics Society, 2005
    [43] R. Mah, A. C. Tamhane. Detection of gross errors in process data. AIChE Journal, 28(5), 828-830, 1982
    [44] D. DeCoste. Automated learning and monitoring of limit functions. In International Symposium on Artificial Intelligence, Robotics, and Automation in Space, 1997
    [45] M. Staroswiecki, G. Comtet-Varga. Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica, 37(5), 687-699, 2001
    [46] A. L. Dexter, D. Ngo. Fault diagnosis in air-conditioning systems: a multi-step fuzzy model-based approach.HVAC&R Research, 7(1), 83-102, 2001
    [47] R. J. Patton, J. Chen, C. J. Lopez-Toribio. Fuzzy observers for nonlinear dynamic systems fault diagnosis.In IEEE Conference on Decision and Control, 1998
    [48] J. McGhee, I. A. Henderson, A. Baird. Neural networks applied for the identification and fault diagnosis of process valves and actuators. Measurement, 20(4), 267-275, 1997
    [49] E. Kaszkurewicz, A. Bhaya, N. F. F. Ebecken. A fault detection and diagnosis module for oil production plants in offshore platforms. Expert Systems with Applications, 12(2), 189-194, 1997
    [50] C. Chang. Satellite diagnostic system: An expert system for intelsat satellite operations. In European Aerospace Conference, 1992
    [51]F. Ciceri, L. Marradi. Event diagnosis and recovery in real-time on-board autonomous mission control. In International Eurospace-Ada-Europe Symposium (pp. 288-301). Springer, Berlin, Heidelberg, 1994
    [52]M. Rolincik, et. al. An expert system for diagnosing environmentally induced spacecraft anomalies, 1992
    [53] F. Hutter, R. Dearden. Efficient on-line fault diagnosis for non-linear systems. In International symposium on artificial intelligence, robotics and automation in space. 2003
    [54] P. Robinson, et. al. Applying model-based reasoning to the fdir of the command and data handling subsystem of the international space station, 2003
    [55] H. Yoshida, S. Kumar, Y. Morita. Online fault detection and diagnosis in VAV air handling unit by RARX modeling. Energy and Buildings, 33(4), 391-401, 2001
    [56] L. I. Smith. A tutorial on principal components analysis. 2002
    [57] M. Kano, et. al. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Computers & chemical engineering, 26(2), 161-174, 2002
    [58] M. Misra, et. al. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers &Chemical Engineering, 26(9), 1281-1293, 2002
    [59] P. Nomikos, J. F. MacGregor. Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1), 41-59, 1995
    [60] B. M.Wise, N. B. Gallagher. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6), 329-348, 1996
    [61] J. Gertler. Fault detection and diagnosis in engineering systems. Routledge, 2017
    [62] S. Wang, F. Xiao. AHU sensor fault diagnosis using principal component analysis method. Energy and Buildings, 36(2), 147-160, 2004
    [63]Liu, Hao-Min, et al. "A System Calibration Model for Mobile PM2. 5 Sensing Using Low-Cost Sensors." 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2017.
    [64]ERKAYA, S., & ULUS, Ş. Investigation of Fan Fault Problems Using Vibration and Noise Analysis.
    [65]李学聪, 华伦次, 万频, & 李军. (2009). 散热风扇质量检测分析系统. 计算机工程与应用, 45(36), 219-221.
    [66] 蕭文龍,多變量分析最佳入門實用書(第二版)
    [67] Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. springer.
    [68] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
    [69]Gresh, T. (2018). Compressor performance: aerodynamics for the user. Butterworth-Heinemann.
    [70]振動噪音產學技術聯盟
    http://aitanvh.blogspot.com/2017/12/rotating-speed-frequency.html
    [71]袁中新,工業型都會區空氣污染物暴露評估研究---總計劃暨子計畫:金屬工業粒狀空氣污染物暴露評估https://www.epa.gov.tw/DisplayFile.aspx?FileID=A37BFCCFD0F8DACB&P=ce9c0cb1-58e6-42e2-90f6-9c40ddbc100f

    無法下載圖示 本全文未授權公開
    QR CODE