本文選題：六面頂壓機(jī) + 頂錘破裂��； 參考：《鄭州大學(xué)》2015年碩士論文

【摘要】：六面頂壓機(jī)頂錘破裂是人造金剛石合成過程中常見的一種生產(chǎn)事故,若未及時發(fā)現(xiàn),極易造成更大的經(jīng)濟(jì)損失,甚至人員傷亡。但當(dāng)前國內(nèi)外并無頂錘破裂在線檢測的相關(guān)應(yīng)用產(chǎn)品,現(xiàn)階段的檢測手段主要是操作工手工劃錘及有經(jīng)驗(yàn)的工人在生產(chǎn)過程中聽音識別。因此頂錘破裂的在線檢測具有很強(qiáng)的現(xiàn)實(shí)意義,同時也是人造金剛石合成的工業(yè)自動化發(fā)展中的一個關(guān)鍵問題。本文通過對頂錘破裂成因的分析,結(jié)合頂錘工作環(huán)境,提出了一種基于聲發(fā)射的頂錘破裂在線檢測方案。結(jié)合當(dāng)前人工聽音識別方法,將聲發(fā)射檢測信號確定在音頻段,并從理論上論述其合理性,利用工控機(jī)聲卡完成信號采集。完成聲發(fā)射檢測模塊的設(shè)計(jì),包括壓電換能元件的選取、外殼設(shè)計(jì)、電源模塊設(shè)計(jì)以及信號前置放大和濾波電路等信號調(diào)理硬件電路設(shè)計(jì)。利用該傳感檢測模塊采集現(xiàn)場頂錘工作信號,直觀分析可以將其分為3類:低能量的噪音、疑似信號(頂錘預(yù)緊環(huán)由于加熱溫度波動而熱脹冷縮及合成壓力波動所引起的震動信號)、裂錘信號。對頂錘破裂信號特征進(jìn)行前期研究。采用頻譜分析、STFT及EMD經(jīng)驗(yàn)?zāi)B(tài)分解方法分析,發(fā)現(xiàn)疑似信號在200Hz~400Hz、2KHz~4KHz頻率段特征明顯,采用EMD經(jīng)驗(yàn)?zāi)B(tài)分解取得的HHT邊際譜清晰顯示裂錘故障頻率存在于13KHz~16KHz。通過C++和Matlab混合編程方法完成系統(tǒng)軟件設(shè)計(jì)。采用C++編寫軟件程序,包含聲卡信號采集、圖形界面顯示及多線程信號分析框架等。設(shè)計(jì)基于短時能量和短時平均過零率雙門限的端點(diǎn)檢測算法,截取出疑似信號和裂錘信號,完成信號預(yù)處理。信號識別算法利用Matlab語言編寫,編譯成COM組件,在信號分析線程調(diào)用完成裂錘信號識別。本文設(shè)計(jì)的檢測系統(tǒng)目前繼續(xù)進(jìn)行現(xiàn)場信號采集,同時利用現(xiàn)有樣本特征,設(shè)計(jì)信號識別算法,在現(xiàn)場進(jìn)行測試,當(dāng)出現(xiàn)誤報(bào)警或破裂未報(bào)警時,提取信號進(jìn)行分析,改進(jìn)信號識別算法。
[Abstract]:The breakage of the top hammer of a six-sided press is a common production accident in the synthetic process of synthetic diamond. If it is not discovered in time, it is easy to cause greater economic losses and even casualties. However, there are no related products for on-line detection of top hammer rupture at home and abroad. At present, the main means of detection are manual stroke hammer and experienced workers in the production process. Therefore, the on-line detection of hammer rupture is of great practical significance and is also a key problem in the development of industrial automation of synthetic diamond synthesis. Based on the analysis of the causes of top hammer rupture and combined with the working environment of top hammer, an on-line detection scheme of top hammer rupture based on acoustic emission is proposed in this paper. Combined with the current method of artificial audio recognition, the acoustic emission detection signal is determined in the audio section, and its rationality is discussed theoretically, and the signal acquisition is completed by using the sound card of the industrial control computer. Complete the design of acoustic emission detection module, including the selection of piezoelectric energy transfer elements, shell design, power module design and signal preamplifier and filter circuit signal conditioning hardware circuit design. The sensor module is used to collect the working signals of the top hammer, which can be divided into three categories: low energy noise, low energy noise, Suspected signal (top hammer pretightening ring due to heating temperature fluctuation caused by thermal expansion and contraction and synthetic pressure fluctuation caused by vibration signal, crack hammer signal. The characteristics of the breaking signal of the top hammer are studied in the early stage. By using spectrum analysis and EMD empirical mode decomposition method, it is found that the characteristic of suspected signal is obvious in the frequency range of 200Hz / 400Hz / 2kHz / 4kHz. The marginal spectrum of HHT obtained by EMD empirical mode decomposition clearly shows that the fault frequency of crack hammer exists at 13kHz / 16kHz. The system software is designed by C and Matlab mixed programming method. C is used to write software program, including sound card signal acquisition, graphical interface display and multi-thread signal analysis framework. An endpoint detection algorithm based on the double threshold of short time energy and short time average zero crossing rate is designed to intercept the suspected signal and crack hammer signal and complete the signal preprocessing. The signal recognition algorithm is compiled by Matlab language and compiled into a COM component, which is called by the signal analysis thread to complete the signal recognition of the hammer. The detection system designed in this paper continues to collect the field signals at present. At the same time, using the existing sample features, the signal recognition algorithm is designed and tested on the spot. When there is false alarm or failure to alarm, the signal is extracted for analysis. Improved signal recognition algorithm.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ163;TG115.28

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