本文關(guān)鍵詞：基于多信息和多模型融合的刀具磨損預(yù)測性評估的方法研究　出處：《中國科學(xué)院大學(xué)(中國科學(xué)院沈陽計算技術(shù)研究所)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： PHM 刀具磨損 數(shù)據(jù)驅(qū)動法 多信息融合 集成方法 多模型融合

【摘要】：本文在工業(yè)大數(shù)據(jù)和PHM的應(yīng)用背景下,利用數(shù)據(jù)驅(qū)動式的分析流程對數(shù)控銑刀磨損的預(yù)測性評估方法進行了深入的研究和分析。在研究過程中,本文首先對銑削過程中采集的信號進行了充分地探索和分析,主要包括信號無效值的截斷處理、異常值的過濾處理,信號周期性、平穩(wěn)性以及功率和能量特性等方面的分析。接著,對預(yù)處理后的信號進行了特征提取,分別用統(tǒng)計方法從時域中提取統(tǒng)計特征、用FFT變換從頻域中提取頻譜和能量特征、用WT變換從時頻聯(lián)合域中提取小波系數(shù)和能量分布比特征。此外,按照特征所屬的信號類型和所屬的軸向?qū)μ崛〕鰜淼奶卣鬟M行了劃分和融合,用于多信息特征融合的實驗研究。本文中通過基于F-test檢驗的評估值和基于互信息(MI)的評估值對特征進行選擇,以提高模型擬合的速度和準(zhǔn)確性。在本文中,分別用決策回歸樹(DTR)和支持向量回歸(SVR)模型對多信息融合的特點和影響刀具磨損的主要因素進行了實驗驗證和分析。結(jié)果表明,一般情況下多信息融合的效果要優(yōu)于單信息,并且得出銑削力信號特征和X軸上特征是影響刀具磨損的主要因素。最后,本文引入了機器學(xué)習(xí)領(lǐng)域的集成方法作為多模型融合的策略,并用回歸樹作為集成方法的基礎(chǔ)學(xué)習(xí)器,對刀具磨損進行了評估和預(yù)測。在本文基于多模型融合的刀具磨損評估過程中,從模型的準(zhǔn)確度、穩(wěn)定性和適用性三個方面上,通過實驗驗證和對比分析方式分析了多模型融合方法和單模型方法在刀具磨損預(yù)測上的性能。結(jié)果表明,基于集成方法的多模型融合策略在上述三個指標(biāo)中取得的效果明顯優(yōu)于單模型,從而說明了基于集成方法的多模型融合策略能夠有效地用于刀具磨損的評估和預(yù)測。
[Abstract]:The industrial application background in big data and PHM, using data driven analysis process of assessment method of NC cutter wear is studied and analyzed. In the process of research, this paper gives a full exploration and analysis of the signal acquisition in milling process, including the truncation signal. The processing value, filtering the abnormal value, periodic signal, analysis of stability and power and energy characteristic. Then, the preprocessed signals were extracted respectively, using statistical methods from time domain to extract the statistical feature extraction, spectrum and energy characteristics from the frequency domain using FFT transform to extract and the energy distribution of wavelet coefficients than the features from the joint time-frequency domain using WT transform. In addition, in accordance with the characteristics of axial signal type characteristics belong and belongs to extract was divided and fusion for multi Experimental study on the characteristics of information fusion. This paper evaluates the F-test test value and based on mutual information (MI) based on the evaluation value for the feature selection, in order to improve the speed and accuracy of model fitting. In this paper, respectively using decision regression tree (DTR) and support vector regression (SVR) model is verified by experiments and analysis of the multi information fusion characteristics and main influencing factors of tool wear. The results show that the multi information fusion is generally better than single information, and that the milling force signal characteristics and X axis characteristics are the main factors influencing the tool wear. Finally, this paper introduces the integration method in the field of machine learning as much model integration strategy, and regression tree as the basis of integrated method of learning, the evaluation and prediction of tool wear. In this paper based on the multi model fusion tool wear assessment process, from the mold The three aspects of accuracy, stability and applicability, through experimental verification and comparative analysis method to analyze the multi model fusion method and single model method in prediction of tool wear performance. The results show that the multi model integration method fusion strategy in the above three indicators have better effect than single model based on thus, the model integration method of fusion strategy can be effectively used for evaluation and prediction of tool wear based on.

【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院沈陽計算技術(shù)研究所)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG71;O212.1

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本文編號：1356957