本文選題：概念驅(qū)動(dòng)　切入點(diǎn)：規(guī)則　出處：《吉林大學(xué)》2011年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：本文在總結(jié)前人研究的基礎(chǔ)上,研究了基于概念驅(qū)動(dòng)的變速箱故障診斷機(jī)理。概念驅(qū)動(dòng)診斷是一種根據(jù)過(guò)去知識(shí)和經(jīng)驗(yàn)進(jìn)行的診斷。研究了如何模擬人利用感知過(guò)程中的信息處理方式來(lái)建立診斷行為模型,發(fā)掘各種可能的經(jīng)驗(yàn)信息介入方式、概念驅(qū)動(dòng)診斷算法的邏輯框架和算法的實(shí)現(xiàn)方法。給出一個(gè)新的智能診斷理論算法框架,滿足在生產(chǎn)節(jié)拍要求下對(duì)變速箱生產(chǎn)線終端快速異常噪聲問(wèn)題的檢測(cè)。 在復(fù)雜的工作環(huán)境下,裝配線上工人所定義的敲響、異響、叉響、噪音等感知性故障狀態(tài)是固定的診斷結(jié)論。所以,將人的認(rèn)知能力辨別的故障類(lèi)型作為診斷概念來(lái)指導(dǎo)變速箱故障診斷,就可解決故障結(jié)論界定不統(tǒng)一、特征參數(shù)提取缺乏概念分類(lèi)依據(jù)等不一致問(wèn)題。本文基于認(rèn)知心理學(xué)關(guān)于人類(lèi)認(rèn)知過(guò)程的信息加工原理,探索了基于概念驅(qū)動(dòng)滿足生產(chǎn)節(jié)拍需要的變速箱異常噪聲問(wèn)題。 研究中首先利用成熟的信號(hào)采集分析技術(shù)收集變速箱敲響、異響、叉響、噪音等典型故障的振動(dòng)信號(hào),并計(jì)算其振動(dòng)特征參數(shù)值。研究各種故障與相應(yīng)故障狀態(tài)下齒輪、軸承、軸以及箱體等質(zhì)量問(wèn)題相關(guān)聯(lián)的振動(dòng)特征值,區(qū)分哪些特征對(duì)哪些問(wèn)題敏感,以及敏感條件是什么。變速箱故障的振動(dòng)與噪聲表現(xiàn)有時(shí)域特征和頻域特征,需要研究這些特征之間的關(guān)系,以及這些關(guān)系變化與變速箱故障狀態(tài)問(wèn)題的關(guān)聯(lián)性,并研究這些關(guān)系變化與變速箱制造質(zhì)量問(wèn)題的關(guān)聯(lián)性。其次,建立一個(gè)可供后續(xù)變速箱異常噪聲問(wèn)題診斷系統(tǒng)開(kāi)發(fā)研究的知識(shí)庫(kù),這個(gè)知識(shí)庫(kù)將記錄變速箱典型故障振動(dòng)特征與變速箱零部件制造質(zhì)量問(wèn)題相關(guān)聯(lián)的基本知識(shí)。 其次,模擬人的認(rèn)知加工過(guò)程,依據(jù)振動(dòng)特征之間的關(guān)系對(duì)變速箱異常噪聲進(jìn)行了診斷識(shí)別。為此,本文提出了采用變速箱故障特征維分?jǐn)?shù)維分析方法、支持向量機(jī)與基于規(guī)則的推理相結(jié)合的故障診斷方法來(lái)建立變速箱故障診斷模型。 本文首先研究了具有概念驅(qū)動(dòng)意識(shí)的推理規(guī)則；研究了各種模式的觸發(fā)規(guī)則,以及使用這些規(guī)則進(jìn)行診斷的診斷規(guī)則,建立一個(gè)可供后續(xù)變速箱典型質(zhì)量問(wèn)題研究的知識(shí)庫(kù)。這個(gè)知識(shí)庫(kù)將記錄變速箱典型故障振動(dòng)特征與變速箱零部件制造質(zhì)量問(wèn)題相關(guān)聯(lián)的基本知識(shí)。知識(shí)庫(kù)的建設(shè)將為后續(xù)實(shí)施基于概念驅(qū)動(dòng)的診斷算法研究提供基礎(chǔ)數(shù)據(jù)支撐,并在此基礎(chǔ)上,對(duì)基于概念驅(qū)動(dòng)的變速箱故障診斷的機(jī)理進(jìn)行了研究。 其次,運(yùn)用人類(lèi)知覺(jué)過(guò)程的信息加工原理、數(shù)據(jù)挖掘技術(shù)、人工智能原理以及信息論等理論方法研究了變速箱敲響、異響、噪音、叉響等質(zhì)量問(wèn)題的“概念”描述方法。提取了診斷過(guò)程中的概念驅(qū)動(dòng)方法規(guī)則,闡述了故障診斷過(guò)程中的信息加工與概念驅(qū)動(dòng)機(jī)制,構(gòu)建了具有概念驅(qū)動(dòng)意識(shí)的變速箱質(zhì)量問(wèn)題智能診斷算法框架。
[Abstract]:On the basis of summing up the previous studies, In this paper, the mechanism of gearbox fault diagnosis based on concept driving is studied. Conceptual driven diagnosis is a kind of diagnosis based on past knowledge and experience. This paper explores various possible methods of empirical information intervention, the logical framework of concept-driven diagnosis algorithm and the implementation method of the algorithm. A new framework of intelligent diagnosis theory algorithm is presented. To meet the requirements of the production beat to the transmission production line terminal fast abnormal noise detection. In a complex work environment, the perceived fault states defined by workers on the assembly line, such as ringing, abnormal, fork, and noise, are fixed diagnostic conclusions. Taking the fault type identified by human cognitive ability as the diagnosis concept to guide the gearbox fault diagnosis can solve the problem that the fault conclusion is not uniform. Based on the information processing principle of cognitive psychology on human cognitive process, this paper explores the abnormal noise problem of gearbox based on concept driven to meet the needs of production rhythm. In the research, the vibration signals of typical faults such as sound, abnormal noise, fork noise, noise and so on are collected by using the mature signal collection and analysis technology, and the characteristic parameters of vibration are calculated, and the gears under various faults and corresponding faults are studied. Vibration eigenvalues associated with mass problems such as bearings, shafts and boxes, distinguishing which features are sensitive to which problems and what are the sensitive conditions. The vibration and noise of gearbox faults exhibit time-domain and frequency-domain characteristics. It is necessary to study the relationship between these characteristics and the relationship between the change of these relations and the problem of the fault state of the gearbox, and the relationship between the change of these relations and the problem of the manufacturing quality of the gearbox. Secondly, A knowledge base can be established for the development and research of the subsequent gearbox abnormal noise diagnosis system. This knowledge base will record the basic knowledge of the vibration characteristics of the transmission typical fault and the manufacturing quality problem of the gearbox parts and components. Secondly, according to the relationship between vibration characteristics, the abnormal noise of gearbox is diagnosed and identified by simulating human cognitive processing process. In this paper, a fractal dimension analysis method of gearbox fault feature is proposed. The fault diagnosis model of gearbox is established by combining support vector machine and rule-based reasoning. In this paper, we first study the reasoning rules with concept driven consciousness, the trigger rules of various patterns, and the diagnostic rules using these rules for diagnosis. To establish a knowledge base which can be used to study the typical quality problem of gearbox. This knowledge base will record the basic knowledge of the vibration characteristics of the gearbox typical fault and the manufacturing quality problem of the gearbox parts. It will provide the basic data support for the further research on the concept driven diagnosis algorithm. On this basis, the mechanism of gearbox fault diagnosis based on concept drive is studied. Secondly, using the principles of information processing, data mining, artificial intelligence and information theory of human perceptual process, the paper studies the sound, noise and noise of gearbox. In this paper, the concept driving method rules in the diagnosis process are extracted, and the information processing and concept driving mechanism in the fault diagnosis process are expounded. An intelligent diagnosis algorithm framework of gearbox quality problem with concept driven consciousness is constructed.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類(lèi)號(hào)】：TH165.3

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