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標題: 利用共振檢測法判別蛋殼裂痕並應用支撐向量機方法驗證之研究
Study on Cracks Detection of Eggshells by Using Resonant Inspection Method and Identified by Support Vector Machine
作者: 馮珮宣
Pei-Hsuan Feng
關鍵字: 共振檢測法
Resonant Inspection
Support Vector Machine.
引用: 1. 白世煜。2000。利用聲波檢測鴨蛋外殼裂痕之研究。碩士論文。國立中興大學農業機械工程研究所。 2. 白世煜、紀偉民、李仲暐、鄭經偉。2001。利用聲波檢測鴨蛋外殼裂痕之研究。農林學報, Volume 50, Issue 4, Page(s) 79-102. 3. 王建雄。2005。鳳梨內部品質線上檢測系統之研發。碩士論文。嘉義大學生物機電工程研究所。 4. 艾群。2000。音波檢測鳳梨內部品質之評估模式. 5. 沈明輝。1999。鴨蛋振動頻譜分析.。碩士論文。台中:國立中興大學生土木工程學系。 6. 周奕廷。2010。脈衝激振技術於檢測文蛤貝殼裂縫之研究。碩士論文。國立嘉義大學生物機電工程研究所。 7. 林鼎凱。2001。蛋殼裂痕位置與檢測點關係之研究。碩士論文。國立中興大學農業機械工程研究所。 8. 陳怡琪。2014。非破壞性鴨蛋蛋殼厚度量測技術之基礎研究。碩士論文。國立中興大學生物產業機電工程研究所。 9. 張勝善。1994。蛋品加工學。增訂版。P524-560。台北:華香園出版社。 10. 馬大猷,楊訓仁。1996。聲學漫談。初版,p8-13。台北:牛頓出版社。 11. 鄭經偉、李蒼郎、沈明輝。1998。振動頻譜應用於鴨蛋外殼裂痕檢測之研究。農業機械學刊。 12. Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. (2007). Rolling Element Bearings Multi-Fault Classification Based on the Wavelet Denoising and Support Vector Machine. Mechanical Systems and Signal Processing, 21(7), 2933-2945. 13. Bono, R. W., Sorensen, S. A., & Stultz, G. R..2010. New Methodology Applying Resonant Inspection for Quality Testing of Very Small Powder Metal Components. 14. Chang, C.-C., & Lin, C.-J. (2011). Libsvm: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27. 15. De Ketelaere, B., Coucke, P., & De Baerdemaeker, J. (2000). Eggshell Crack Detection Based on Acoustic Resonance Frequency Analysis. Journal of Agricultural Engineering Research, 76(2), 157-163. 16. Deng, X., Wang, Q., Chen, H., & Xie, H. (2010). Eggshell Crack Detection Using a Wavelet-Based Support Vector Machine. Computers and electronics in agriculture, 70(1), 135-143. 17. Garcia, G. V., R. Osegueda, and D. Meza.1999. 'Damage Detection Comparison Between Damage Index Method and ARMA Method,' Proceeding of The 17th International Modal Analysis Conference, Vol. 1, pp. 93-598. 18. Haoqi, L., & Fei, R. (2014). Resonant Ultrasound Spectroscopy Offers Unique Advantages as a Nondestructive Test Method. Advanced Materials & Processes, 172(10), 17-19. 19. Ketelaere B.De,P. Coucke and J. De Baerdemaeker.2000.Eggshell crack detection based on acoustic resonance frequency analysis. Silcoe Research Institute.76:157-163 20. LU, W., ZHANG, C., LUO, H., DING, W., & WANG, L. (2015). Micro-Cracks Detection of Eggshells Based on a Magnetostrictive Transducer. 21. Lai, C., Sun, X., Dasch, C., Harmon, G., & Jones, M. 2011. Quantify Resonance Inspection with Finite Element-Based Modal Analyses. Journal of Vibration and Acoustics, 133(3), 031004. 22. Rao, S. S., & Yap, F. F.1995. Mechanical Vibrations (Vol.4): Addison-Wesley Reading. 23. Youden, W. J. 1950. Index for rating diagnostic tests. Cancer 3(1): 32-35 24. Wang, J., Jiang, R., & Yu, Y. (2004). Relationship between Dynamic Resonance Frequency and Egg Physical Properties. Food research international, 37(1), 45-50. 25. Stultz, G. R., Bono, R. W., & Schiefer, M. I. (2005). Fundamentals of Resonant Acoustic Method Ndt Advances in Powder Metallurgy and Particulate Materials 3.
摘要: 蛋殼裂痕不但會影響其保鮮的時間,也會降低加工製品的成功率,因此本研究將使用共振檢測法的原理為基礎,並以支撐向量機驗證之。該原理乃利用麥克風及加速規記錄敲擊訊號數據,由頻譜分析儀經快速傅立葉轉換FFT(Fast Fourier Transform)後進行訊號分析。以FFT頻譜分辨鴨蛋裂痕,藉由比較完整蛋與裂痕蛋之共振頻率、振幅的變化,找出完整蛋與裂痕蛋之間的差異。其中使用麥克風跟加速規作為感測器,對鴨蛋裂痕進行檢測,再以支撐向量機(Support vector machine, SVM)驗證測試結果,皆可成功鑑別,但以麥克風作為感測器檢測效能最佳。結果顯示,完整蛋的特徵頻率平均為4130~5500 Hz且其振幅0.16~0.20V,而裂痕蛋之頻譜訊號雜亂,無明顯特徵頻率,其振幅最高值在0.06V,作為特徵判斷以SVM進行蛋殼裂痕分類,其訓練組與測試組準確率可達99%及98%。麥克風應用於有髒?之完整蛋與裂痕蛋,若是不敲擊在稻穀及一坨濕軟的污土上,以SVM結果顯示,其訓練組與測試組準確率可達100%及100%。本研究結果顯示,使用共振檢測法判斷蛋殼裂痕為一有效之方法。
Cracks of eggshells will not only affect the preserved time, but also reduce the successful rate of the processed products. Therefore, this study will base on the theory of resonant inspection, and it was verified by the Support Vector Machine(SVM). The principle is that recording the signal data by using microphone and accelerator. Then, it used the FFT analyzer by fast Fourier transform to execute the signal analysis. To distinguish perfect and cracked eggs, it was found by comparing the resonant frequency and amplitude used microphone and accelerometer as the sensor, Secondly, the results was verified by Support Vector Machine both of method are successful. However, the microphone sensor is better. The results showed that the characteristic frequency of the perfect egg was 4130 ~ 5500Hz and its amplitude was 0.16 ~ 0.20V.However, the spectrum of the cracked egg was messy with no obvious characteristic frequency, and the maximum amplitude was 0.06V. This feature was judged by SVM, and the identification accuracy can reaches to 99% and 98% for training set and the testing set. If that is not knock on the paddy or lump of soft soil on the eggshell, Microphones are used in dirty eggs and crack eggs, and the results of SVM accuracy could reaches to 100% and 100% for training set and the testing set. The conclusion is that resonance detection method to determine the cracks of eggshells is an effective method.
文章公開時間: 2020-07-25
Appears in Collections:生物產業機電工程學系



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