機(jī)械外文翻譯---機(jī)械狀態(tài)監(jiān)測和故障診斷的最新進(jìn)展

1、<p>　　Recent Progress on Mechanical Condition Monitoring and Fault diagnosis</p><p>　　Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei Zhang</p><p>　　Reliability Engineering Institut

2、e, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, P. R. China</p><p>　　Huangpi Campus, Air Force Radar Academy, Wuhan 430019, P. R. China</p><p><b>　

3、　Abstract</b></p><p>　　Mechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressive deterioration

4、 of their state. The mechanical failures account for more than 60% of breakdowns of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper di

5、scusses the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent</p><p>　　© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CE

6、IS 2011] </p><p>　　Keywords: Condition monitoring; Fault diagnosis; Vibration; Signal processing</p><p>　　1. Introduction </p><p>　　With the development of modern science and techno

7、logy, machinery and equipment functions are becoming more and more perfect, and the machinery structure becomes more large-scale, integrated, intelligent and complicated. As a result, the component number increases signi

8、ficantly and the precision requirement for the part mating is stricter. The possibility and category of the related component failures therefore increase greatly. Malignant accidents caused by component faults occur freq

9、uently all </p><p>　　Mechanical equipment fault diagnosis technology uses the measurements of the monitored machinery in operation and stationary to analyze and extract important characteristics to calibrate

10、 the states of the key components. By combining the history data, it can recognize the current conditions of the key components quantitatively, predicts the impending abnormalities and faults, and prognoses their future

11、condition trends. By doing so, the optimized maintenance strategies can be settled, and thus t</p><p>　　The contents of mechanical fault diagnosis contain four aspects, including fault mechanism research, si

12、gnal processing and feature extraction, fault reasoning research and equipment development for condition monitoring and fault diagnosis. In the past decades, there has been considerable work done in this general area by

13、many researchers. A concise review of the research in this area has been presented by [5, 6]. Some landmarks are discussed in this paper. The novel signal processing techniques </p><p>　　2. Fault Mechanism R

14、esearch </p><p>　　Fault Mechanism research is a very difficult and important basic project of fault diagnosis, same as the pathology research of medical. American scholar John Sohre, published a paper on &qu

15、ot;Causes and treatment of high-speed turbo machinery operating problems (failure)", in the United States Institute of Mechanical Engineering at the Petroleum Mechanical Engineering in 1968, and gave a clear and con

16、cise description of the typical symptoms and possible causes of mechanical failure. He suggested that</p><p>　　3. Advanced Signal Processing and Feature Extraction Methods </p><p>　　Advanced sig

17、nal processing technology is used to extract the features which are sensitive to specific fault by using various signal analysis techniques to process the measured signals. Condition information of the plants is containe

18、d in a wide range of signals, such as vibration, noise, temperature, pressure, strain, current, voltage, etc. The feature information of a certain fault can be acquired through signal analysis method, and then fault diag

19、nosis can be done correspondingly. To meet the s</p><p>　　Early research on vibration signal analysis is mainly focused on classical signal analysis which made a lot of research and application progress. Rot

20、ating mechanical vibration is usually of strong harmonic, its fault is also usually registered as changes in some harmonic components. Classical spectrum analysis based on Fourier transform (such as average time-domain t

21、echniques, spectrum analysis, cepstrum analysis and demodulation techniques) can extract the fault characteristic information effec</p><p>　　4. Research on Fault Reasoning</p><p>　　At present, m

22、any methods are adopted in the process of diagnostic reasoning. According to the subject systems which they belong to, the fault diagnosis can be divided into three categories: (1) the fault diagnosis based on control mo

23、del; (2) the fault diagnosis based on pattern recognition; (3) the fault diagnosis based on artificial intelligence. Among them, the fault diagnosis based on control model needs to establish model through theoretic or ex

24、perimental methods. The changes of system param</p><p>　　Pattern recognition conducts cluster description for a series of process or events. It is mainly divided into statistical method and language structur

25、e 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 common recognit

26、ion methods, including bayes category, distance function category, fuzzy diagnosis, fault tree analysis, grey theory diagnosis and</p><p>　　5. Research and Development of Fault Diagnosis Devices </p>

27、<p>　　Fault diagnosis technology ultimately comes 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

28、and diagnosis (including data collector system), and (2) On-line condition monitoring and fault diagnosis system. Portable instrument is mainly adopted single-chip microcomputers to complete data acquisition, which has c

29、ertain ability for signal analysis and fault diagnosis. On-line monitor</p><p>　　Based on the realization of condition monitoring of equipments, network diagnostics center can monitor and diagnose the operat

30、ion of equipments at any time through the network to achieve the long distance information transmission. The remote monitoring system can also achieve the collaborative diagnosis of production equipments, multiple diagno

31、stic systems serve the same piece of equipment, and multiple devices share the same diagnostic system. </p><p>　　6. Conclusions </p><p>　　To achieve a dynamic system condition monitoring and fa

32、ult 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 seriou

33、sly affected. It is therefore very urgent for advanced signal processing technology to eliminate noise to get true signal. No matter classical or advance fault diagnosis techniques, they have achieved great progress in v

34、ariou</p><p>　　Acknowledgements </p><p>　　This project is sponsored by the grants from the National Natural Sciences Foundation of China </p><p>　　(NSFC) (No. 50975213). </p>

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49、ersity press, 2006.</p><p>　　機(jī)械狀態(tài)監(jiān)測和故障診斷的最新進(jìn)展</p><p>　　Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei Zhang</p><p>　　武漢理工大學(xué)，能源與動力工程學(xué)院，可靠性工程研究所，中華人民共和國，武漢，430063</p>

50、<p>　　空軍雷達(dá)學(xué)院，黃陂校區(qū)，中華人民共和國，武漢，430019</p><p><b>　　摘要</b></p><p>　　機(jī)械設(shè)備被廣泛應(yīng)用于各種工業(yè)應(yīng)用。一般在惡劣條件下工作，機(jī)械設(shè)備的狀態(tài)會逐漸惡化。機(jī)械故障占超過60％的系統(tǒng)故障。因此，即將到來的機(jī)械故障的識別系統(tǒng)，是防止系統(tǒng)故障的關(guān)鍵。本文討論了在機(jī)械狀態(tài)監(jiān)測與故障診斷的最新進(jìn)展。從故障機(jī)

51、理研究，信號處理和特征提取，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。概述了一些現(xiàn)有的信號處理和特征提取方法。對這些技術(shù)的優(yōu)點和缺點進(jìn)行了討論。研究結(jié)果表明，基于智能信息融合的機(jī)械故障診斷專家系統(tǒng)與自我學(xué)習(xí)和自我更新能力，是機(jī)械設(shè)備狀態(tài)監(jiān)測和故障診斷未來研究的發(fā)展方向。</p><p>　　©2011年由愛思唯爾公司出版。選擇（和/或）根據(jù)[2011年控制工程與信息科學(xué)會議]責(zé)任同行審查</p

52、><p>　　關(guān)鍵詞：狀態(tài)監(jiān)測，故障診斷，振動，信號處理</p><p><b>　　1.介紹</b></p><p>　　隨著現(xiàn)代科學(xué)技術(shù)的發(fā)展，機(jī)械和設(shè)備的功能變得越來越完善，并且機(jī)械結(jié)構(gòu)變得更大型，集成，智能和復(fù)雜。因此，組件數(shù)量顯著增加，接合部件的精度要求也更加嚴(yán)格。相關(guān)組件故障的可能性和故障的種類因此也大大增加。組件故障所造成的惡性事故頻

53、繁發(fā)生在世界各地，甚至一個小的機(jī)械故障可能會導(dǎo)致嚴(yán)重的后果。因此，有效的早期故障檢測和診斷是機(jī)械正常運轉(zhuǎn)的關(guān)鍵。雖然在機(jī)械設(shè)計過程和制造過程中已經(jīng)采用優(yōu)化技術(shù)來提高機(jī)械產(chǎn)品的質(zhì)量，由于現(xiàn)代設(shè)備的復(fù)雜性，機(jī)械故障仍然難以避免。狀態(tài)監(jiān)測和故障診斷，以先進(jìn)的科學(xué)技術(shù)為根本，作為一種有效的方式來預(yù)測潛在的故障和降低機(jī)器故障的成本。這就是所謂的出現(xiàn)在近三十年的機(jī)械設(shè)備故障診斷技術(shù) [1，2]。</p><p>　　機(jī)械設(shè)備

54、故障診斷技術(shù)使用監(jiān)控機(jī)械運轉(zhuǎn)和固定分析和提取重要特征的測量值來校準(zhǔn)關(guān)鍵部件的狀態(tài)。通過結(jié)合歷史數(shù)據(jù)，它可以定量的識別在目前條件下的關(guān)鍵部件，預(yù)測即將發(fā)生的異常和故障，并且預(yù)測它們未來的發(fā)展趨勢。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。 [3，4]。</p><p>　　機(jī)械故障診斷的內(nèi)容包含四個方面，包括故障機(jī)理研究，信號處理和特征提取，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)

55、。在過去的幾十年里，已經(jīng)有許多研究者在此領(lǐng)域做了大量的工作。在這一領(lǐng)域一個簡明的研究評論已經(jīng)被提出 [5，6]。本文討論了一些里程碑式的觀點。介紹新型的信號處理技術(shù)。這些新的信號處理和特征提取方法的優(yōu)缺點，在這項工作中進(jìn)行了討論。然后，簡要回顧了故障推理研究和診斷設(shè)備。最后，未來的研究課題中所描述的是下一代智能故障診斷和預(yù)測系統(tǒng)。</p><p><b>　　2.故障機(jī)理研究</b><

56、/p><p>　　故障機(jī)理的研究是故障診斷的一個非常艱難和重要的基礎(chǔ)工程，就像病理研究對于醫(yī)療相同。美國學(xué)者John·Sohre，于1968年在美國機(jī)械工程研究所石油機(jī)械工程發(fā)表了“高速渦輪機(jī)械操作問題（失敗）的原因及處理”一文，并對于典型的癥狀和可能引起機(jī)械故障的原因進(jìn)行了一個清晰、簡明的描述。他建議，典型故障可分為9個類型和37種[7]。之后，在上世紀(jì)60年代至70年代期間Shiraki [8]在日本對

57、于故障機(jī)理的研究工作做了很大貢獻(xiàn)，并總結(jié)了豐富的現(xiàn)場故障排除經(jīng)驗，以支持故障機(jī)制的理論。本特利內(nèi)華達(dá)公司也進(jìn)行了一系列實驗研究轉(zhuǎn)子 - 軸承系統(tǒng)的故障機(jī)制 [9]。大量的相關(guān)工作在中國也已經(jīng)完成。Gao等人[10]研究了高速透平機(jī)械振動故障機(jī)理，探討了振動頻率和振動發(fā)電之間的關(guān)系，并擬定了振動故障原因，次同步、同步和超同步振動的機(jī)制和識別功能表。根據(jù)表格他們提出，他們已經(jīng)將典型的故障分為10個類型和58種，并在機(jī)械設(shè)計與制造，安裝和維護(hù)

58、，操作及機(jī)器降解方面提供預(yù)防措施。 Xu等人[11]總結(jié)了旋轉(zhuǎn)機(jī)的常見故障。Chen等人[12]利用非線性動力學(xué)理論來分析了發(fā)電機(jī)軸振動問題的關(guān)鍵。他們建立了發(fā)電機(jī)轉(zhuǎn)子</p><p>　　3.先進(jìn)的信號處理和特征提取方法</p><p>　　先進(jìn)的信號處理技術(shù)被用于提取的原因是靈敏，通過各種信號分析技術(shù)來處理測量信號到具體的故障。植物狀態(tài)信息中包含著廣泛的信號，如振動，噪聲，溫度，壓力，

59、應(yīng)變，電流，電壓等?？梢酝ㄟ^信號分析方法獲得一定的故障特征信息，然后可以做出相應(yīng)的故障診斷。為了滿足故障診斷的特殊需要，故障特征提取和分析技術(shù)正在經(jīng)歷，從時間領(lǐng)域分析到傅里葉頻域分析，從線性平穩(wěn)信號分析到非線性非平穩(wěn)分析，從頻域分析到時頻分析的過程。</p><p>　　振動信號分析的早期研究主要集中在傳統(tǒng)的信號分析，進(jìn)行了大量的研究和應(yīng)用進(jìn)展。旋轉(zhuǎn)機(jī)械振動通常是強(qiáng)烈的諧波，其故障也通常注冊為一些諧波成分的變化。

60、傳統(tǒng)頻譜分析基于傅里葉變換（如平均時域技術(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力機(jī)械，尤其是在旋轉(zhuǎn)機(jī)械振動監(jiān)測和故障診斷。在某種意義上說，傳統(tǒng)的信號分析，仍然是機(jī)械振動信號分析和故障特征提取的主要方法。然而，傳統(tǒng)的頻譜分析也有明顯的劣勢。傅立葉變換反映信號的整體統(tǒng)計特性，適用于平穩(wěn)信號分析。在現(xiàn)實中，從機(jī)械設(shè)備中的信號測量也是千變?nèi)f化的，非平穩(wěn)的，非高斯分布的和非線性隨機(jī)的。尤其是當(dāng)設(shè)備發(fā)生

61、故障，這種情況出現(xiàn)的更加突出。對于非平穩(wěn)信號，一些時頻細(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。因此對于這些非線性的和非平穩(wěn)的信號需要提出新方法。來自于工程實踐的強(qiáng)勁需求，也有助于信號分析的快速發(fā)展。對于非平穩(wěn)信號和非線性信號分析的新方法不斷涌現(xiàn)，他們被很快應(yīng)用于機(jī)械故障診斷領(lǐng)域。信號分析的新方法主要包括時頻分析，小波分析，希爾伯特黃變換，獨立分量分析，先進(jìn)的統(tǒng)計分析，非線性信號分析等。</p>&l

62、t;p><b>　　4.故障推理研究</b></p><p>　　目前，許多方法在診斷推理過程中被采用。根據(jù)他們所屬的主體系統(tǒng)，故障診斷，可分為三類：（1）基于控制模型的故障診斷;（2）基于模式識別的故障診斷;（3）基于人工智能的故障診斷。其中，基于控制模型的故障診斷需要通過理論或?qū)嶒灧椒ń⒛Ｐ?。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)

63、。這項技術(shù)是指模型的建立，參數(shù)估計，狀態(tài)估計，應(yīng)用觀察員等。因為它要求準(zhǔn)確的系統(tǒng)模型，這種方法對于實踐中的復(fù)雜設(shè)備在經(jīng)濟(jì)上是不可行的。</p><p>　　模式識別進(jìn)行集群描述為一系列的過程或事件。它主要分為統(tǒng)計方法和語言結(jié)構(gòu)的方法。設(shè)備的故障診斷，可以作為模式識別的過程被確認(rèn)，也就是說，它承認(rèn)的故障，基于提取的故障特征。有許多共同的識別方法，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。

64、近年來，一些新技術(shù)也已經(jīng)應(yīng)用到旋轉(zhuǎn)機(jī)械故障診斷的領(lǐng)域中，如模糊集與神經(jīng)網(wǎng)絡(luò)組合，基于隱馬爾可夫模型的動態(tài)模式識別等。</p><p>　　5.故障診斷裝置的研究與發(fā)展</p><p>　　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)展有兩個方面：一是便攜式震動檢測和診斷（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)。便攜式儀器主要是可以完成數(shù)據(jù)獲取的單片機(jī)，當(dāng)然儀器本身具有數(shù)

65、據(jù)分析和診斷功能。在線檢測和診斷系統(tǒng)是一個由感應(yīng)器、數(shù)據(jù)采集、警報和互鎖保護(hù)和條件監(jiān)視組成的子系統(tǒng)，具有較強(qiáng)信號分析和診斷軟件。這些軟件主要是美國BENTLY公司開發(fā)的3300, 3500 and DM2000系統(tǒng)，美國西屋公司開發(fā)的PDS系統(tǒng)，ENTECK& IRD公司開發(fā)的5911系統(tǒng)，日本三菱公司開發(fā)的MHM系統(tǒng)，丹麥B&K公司開發(fā)的3450指南針系統(tǒng)。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

66、要設(shè)備。</p><p>　　由于采用了對設(shè)備運行狀況的監(jiān)控手段，網(wǎng)絡(luò)診斷中心可以通過網(wǎng)絡(luò)傳輸信息，隨時完成對設(shè)備運行的遠(yuǎn)程檢測和監(jiān)控，遠(yuǎn)程監(jiān)控系統(tǒng)還可以采集生產(chǎn)設(shè)備運行狀況的診斷信息，多程檢測系統(tǒng)可以用來控制同一條生產(chǎn)線，所有檢測儀器可以共享診斷數(shù)據(jù)。</p><p><b>　　6.結(jié)論</b></p><p>　　要實現(xiàn)動態(tài)系統(tǒng)監(jiān)控和故障

67、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)自身的波動和設(shè)備本身的干擾信號信息經(jīng)常受到干擾，所以很重要的一點是要依靠先進(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。不管是傳統(tǒng)的還是先進(jìn)的故障診斷技術(shù)在各種應(yīng)用中都已經(jīng)取得了很大進(jìn)步，按照信息系統(tǒng)的觀點，每項技術(shù)都是故障診斷的組成部分，所有部分的有效的融合是最好實現(xiàn)條件監(jiān)控和故障診斷的保障。因此故障機(jī)制研究、信號處理和特征采集、故障成因研究和設(shè)備發(fā)展將更加緊密地聯(lián)系在一起，才能

68、在將來實現(xiàn)故障診斷專家系統(tǒng)。實現(xiàn)專家診斷系統(tǒng)的核心是突破知識獲取的瓶頸，用可靠的方式升級數(shù)據(jù)模型，提供專家系統(tǒng)的普及能力。</p><p>　　只有這樣，故障診斷專家系統(tǒng)才能對潛在異常提供準(zhǔn)確的評估，防止事故的發(fā)生，確保機(jī)械設(shè)備的正常運行，將由于設(shè)備故障造成的損失降低到最小程度。</p><p><b>　　致謝    </b>