本文選題：頂錘 + 故障診斷　； 參考：《北京郵電大學(xué)》2015年碩士論文

【摘要】：金剛石生產(chǎn)過程中,壓機頂錘承受交變的高溫高壓,容易發(fā)生斷裂,如果裂錘繼續(xù)使用,可能會出現(xiàn)塌錘事故,導(dǎo)致同一壓機的另外五個頂錘報廢,對金剛石生產(chǎn)壓機的安全穩(wěn)定運行有著極大的危害。因此,對壓機頂錘斷裂進(jìn)行及時有效地識別和診斷對于保證金剛石生產(chǎn)的安全有著重大意義。 本文以支持向量數(shù)據(jù)描述為基礎(chǔ),對金剛石頂錘裂紋故障診斷展開以下研究： 1)針對采集到的頂錘裂紋信號背景噪聲大的問題,通過對比分析頂錘故障信號與背景噪聲的能量分布特點,運用高通濾波器去除背景噪聲,并根據(jù)信號的能量利用閾值將故障信號準(zhǔn)確提取。最后通過實驗驗證了該方法的有效性。 2)通過大量實驗發(fā)現(xiàn),傳統(tǒng)參數(shù)分析方法無法準(zhǔn)確地區(qū)分頂錘故障和正常信號。因此本文通過分析,提取了能夠表征故障脈沖的過零率、線性倒譜和功率譜密度三種特征參數(shù),實現(xiàn)了兩者的有效區(qū)分。經(jīng)實驗驗證,該方法可準(zhǔn)確識別頂錘故障脈沖。 3)通過分析故障識別的限制因素,本文引入特征參數(shù)優(yōu)化方法。將連續(xù)特征數(shù)字量化,然后利用信息增益進(jìn)行特征選擇。通過實驗驗證,在建立分類器之前,對特征參數(shù)進(jìn)行優(yōu)選,使得利用特征參數(shù)構(gòu)建的分類模型更加精確。 4)隨著數(shù)據(jù)的不斷采集更新,新增數(shù)據(jù)集將極大豐富訓(xùn)練集,并且新增數(shù)據(jù)蘊含的信息和知識具有很大的潛在價值。因此,本文引入增量學(xué)習(xí)算法,并通過實驗驗證增量學(xué)習(xí)能縮短分類時間,提高分類效率。
[Abstract]:In the process of diamond production, the top hammer of the press is subjected to alternating high temperature and high pressure, so it is easy to break. If the hammer continues to be used, there may be a collapse hammer accident, resulting in the other five top hammers of the same press being scrapped. It has great harm to the safe and stable operation of diamond production press. Therefore, it is of great significance to identify and diagnose the breakage of press head hammer in time and effectively to ensure the safety of diamond production. In this paper, based on the support vector data description, the following research on crack diagnosis of diamond top hammer is carried out: 1) aiming at the problem that the background noise of the crack signal of the top hammer is large, the characteristics of energy distribution between the fault signal and the background noise of the top hammer are compared and analyzed, and the background noise is removed by using the high-pass filter. The fault signal is extracted accurately according to the energy threshold of the signal. Finally, the effectiveness of the method is verified by experiments. 2) through a large number of experiments, it is found that the traditional parameter analysis method can not accurately distinguish the malfunction of the top hammer from the normal signal. Therefore, through analysis, three characteristic parameters, which can represent the zero-crossing rate of fault pulse, linear cepstrum and power spectral density, are extracted, and the effective distinction between the two parameters is realized. The experimental results show that the method can accurately identify the top hammer fault pulse. 3) by analyzing the limiting factors of fault identification, the method of feature parameter optimization is introduced in this paper. The continuous feature number is quantized and the information gain is used for feature selection. The experimental results show that the feature parameters are optimized before the classifier is established, which makes the classification model constructed by the feature parameters more accurate. 4) with the continuous data collection and updating, the new data set will greatly enrich the training set, and the information and knowledge contained in the new data has great potential value. Therefore, the incremental learning algorithm is introduced in this paper, and experimental results show that incremental learning can shorten the classification time and improve the classification efficiency.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ163

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