【正文】 de basis for fault diagnosis. This technology refers to model establishment, parameters estimation, status estimation, application of observers, etc. Since it requires accurately system model, this method is not economically feasible for the plicated devices in the practice. Pattern recognition conducts cluster description for a series of process or events. It is mainly divided into statistical method and language structure method. The fault diagnosis of equipments could be recognized as the pattern recognition process, that is to say, it recognizes the fault based on the extraction of fault characteristics. There are many mon recognition methods, including bayes category, distance function category, fuzzy diagnosis, fault tree analysis, grey theory diagnosis and so on. Recent years, some new technologies have been also applied in the field of the fault diagnosis of rotary machines, such as the bination of fuzzy set and neural work, the dynamic pattern recognition based on hidden markov model, etc. 外文翻譯原文及譯文 10 5. Research and Development of Fault Diagnosis Devices Fault diagnosis technology ultimately es down to the actual devices, and at present research and development of fault diagnosis devices is in the following two directions: (1) Portable vibration monitoring and diagnosis (including data collector system), and (2) Online condition monitoring and fault diagnosis system. Portable instrument is mainly adopted singlechip microputers to plete data acquisition, which has certain ability for signal analysis and fault diagnosis. Online monitoring and diagnosis system is usually equipped with sensors, data acquisition, alarm and interlock protection, condition monitoring subsystem, etc. And it is also fitted with rich signal analysis and diagnosis software. These software include America BENTLY Corporation 3300, 3500 and DM2020 systems, America Westinghouse Company PDS system, the 5911 system developed by ENTECK and IRD Company, Japan Mitsubishi MHM system, the Danish Bamp。an Jiaotong University Press, 1998. [12] Chen YS, Tian JY, Jin ZW, Ding Q. Theory of nonlinear dynamics and applied techniques of solving irregular operation of a large scale gas turbine in a prehensive way. China Mechanical Engineering 1999。從故障機(jī)理研究，信號(hào)處理和特征提取，故障推理研究和設(shè)備開發(fā)等方面進(jìn)行了出色的工作。 相關(guān)組件故障的可能性和故障的種類因此也大大增加 。這樣做，最優(yōu)化維修策略可以被制定 ，因此，工業(yè)可以從狀態(tài)監(jiān)測中大大獲益。然后，簡要回顧了故障推理研究和診斷設(shè)備。大量的相關(guān)工作在中國也已經(jīng)完成。然而，大多數(shù)的故障機(jī)理研究是在定性和數(shù)值模擬階段，工程實(shí)踐中是很難實(shí)施的。 振動(dòng)信號(hào)分析的早期研究主要集中在傳統(tǒng)的信號(hào)分析，進(jìn)行了大量的研究和應(yīng)用進(jìn)展。對(duì)于非平穩(wěn)信號(hào)，一些時(shí)頻細(xì)節(jié)不能在頻譜上反應(yīng)，并且它的頻率分辨率使用傅里葉變 換是有限的。（ 3）基于人工智能的故障診斷。有許多共同的識(shí)別方法， 包括貝葉斯分類，距離函數(shù)分類，模糊診斷，故障樹 分析，灰色理論 診斷等等 。中國也成功地開發(fā)出大型在線故障診斷系統(tǒng)，主要用于蒸汽渦輪機(jī)等重要設(shè)備。 。 山東交通學(xué)院畢業(yè)設(shè)計(jì) 21 要實(shí)現(xiàn)動(dòng)態(tài)系統(tǒng)監(jiān)控和故障診斷，最主要的是系統(tǒng)能夠采集到可靠的特征信息，但是由于系統(tǒng)自身的波動(dòng)和設(shè)備本身的干擾信號(hào)信息經(jīng)常受到干擾，所以很重要的一點(diǎn)是要依靠先進(jìn)的數(shù)據(jù)處理技術(shù)排除噪音以保證數(shù)據(jù)的準(zhǔn)確性。 外文翻譯原文及譯文 20 故障診斷技術(shù)最終發(fā)展成為故障診斷儀器，目前其研究和發(fā)展有兩個(gè)方面：一是便攜式震動(dòng)檢測和診斷（包括數(shù)據(jù)采集系統(tǒng)），二是在線環(huán)境監(jiān)控和故障診斷系統(tǒng)。系統(tǒng)參數(shù)或系統(tǒng)狀態(tài)的變化可以直接反映設(shè)備的物理過程的變化，因此，它可以為故障診斷提供依據(jù)。來自于工程實(shí)踐的強(qiáng)勁需求，也有助于信號(hào)分析的快速發(fā)展。傳統(tǒng)頻譜分析基于傅里葉變換（如平均 時(shí)域技術(shù)，頻譜分析，倒頻譜分析和解調(diào)技術(shù)），可以有效提取故障特征信息，因此它被廣泛的用于動(dòng)力機(jī)械，尤其是在旋轉(zhuǎn)機(jī)械振動(dòng)監(jiān)測和故障診斷。故障診斷結(jié)果和應(yīng)用的可能性可能會(huì)影響顯著。根據(jù)表格他們提出，他們已經(jīng)將典型的故障分為 10 個(gè)類型和 58 種，并在機(jī)械設(shè)計(jì)與制造，安裝和維護(hù)，操作及機(jī)器降解方面提供預(yù)防措施。 山東交通學(xué)院畢業(yè)設(shè)計(jì) 17 故障機(jī)理的研究是故障診斷的一個(gè)非常艱難和重要的基礎(chǔ)工程，就像病理研究對(duì)于醫(yī)療相同。 機(jī)械故障診斷的內(nèi)容包含四個(gè)方面，包括故障機(jī)理研究，信號(hào)處理和特征提取， 故障推理研究，以及設(shè)備狀態(tài)監(jiān)測和故障診斷的開發(fā) 。因此，有效的早期故障檢測和診斷是機(jī)械正常運(yùn)轉(zhuǎn)的關(guān)鍵。對(duì)這些技術(shù)的優(yōu)點(diǎn)和缺點(diǎn)進(jìn)行了討論。 31:1046. [15] Liu SY, Song XP, Wen BC. Catastrophe in fault developing process of rotor system. Journal of Northeastern University (Natural Science) 2020。K 3450 COMPASS system, etc. China has also successively developed large online monitoring and fault diagnosis system, which has been put into use on steam turbine and other important equipments. Based on the realization of condition monitoring of equipments, work diagnostics center can monitor and diagnose the operation of equipments at any time through the work to achieve the long distance information transmission. The remote monitoring system can also achieve the collaborative diagnosis of production equipments, multiple diagnostic systems serve the same piece of equipment, and multiple devices share the same diagnostic system. 山東交通學(xué)院畢業(yè)設(shè)計(jì) 11 6. Conclusions To achieve a dynamic system condition monitoring and fault diagnosis, primary task is the need to get enough reliable characteristic information from the system. Due to the fluctuation of the system itself and the environment disturbance, reliable signal collection is seriously affected. It is the