機械外文翻譯---機械狀態(tài)監(jiān)測和故障診斷的最新進展

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>

35、<p>　　References </p><p>　　[1] Wu XK. The fault diagnosis based on information fusion theory and its application in internal combustion engine. Ph.D. thesis, Wuhan University of Technology, 1998. <

36、;/p><p>　　[2] Chen YR. Modern signal processing technology in the application of vibration diagnosis of internal combustion engine.Ph.D. thesis, Wuhan University of Technology, 1998. </p><p>　　[3]

37、Qu LS, He ZJ. Mechanical fault diagnostics. Shanghai: Shanghai Science and Technology Press, 1986. </p><p>　　[4] Huang WH, Xia SB, Liu RY. Equipment fault diagnosis principle, technology and application. Bei

38、jing: Science Press, 1996. </p><p>　　[5] Jayaswalt P, Wadhwani AK. Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review. Aus

39、tralian Journal of Mechanical Engineering 2009; 7: 157-172. </p><p>　　[6] Daneshi-Far Z, Capolino GA, Henao H. Review of failures and condition monitoring in wind turbine generators. 19th International Conf

40、erence on Electrical Machines. Rome, Italy; 2010. </p><p>　　[7] Sohre JS. Trouble-shooting to stop vibration of centrifugal. Petrop Chem. Engineer 1968; 11: 22-23. </p><p>　　[8] Shiraki T. Mecha

41、nical vibration lectures. Zhengzhou: Zhengzhou Mechanical Institute; 1984. </p><p>　　[9] Bently DW. Forced subrotative speed dynamic action of rotating machinery. USA: ASME Publication, 74-pet-16. </p>

42、<p>　　[10] Gao JJ. Research on high speed turbine machinery vibration fault mechanism and diagnostic method. Ph.D. thesis, Xi'an Jiaotong University, 1993. </p><p>　　[11] Xu M, Zhang RL. Equipment

43、 fault diagnosis manual. Xi’an: Xi'an Jiaotong University Press, 1998. </p><p>　　[12] Chen YS, Tian JY, Jin ZW, Ding Q. Theory of nonlinear dynamics and applied techniques of solving irregular operation

44、of a large scale gas turbine in a comprehensive way. China Mechanical Engineering 1999; 10: 1063-68. </p><p>　　[13] Yang JG, Zhou YC. Internal combustion engine vibration monitoring and fault diagnosis. Dali

45、an: Dalian Maritime University Press, 1994. </p><p>　　[14] Wang Y, Gao JJ, Xia SB. The study of causes and features of faults in supporting system for rotary machinery. Journal of Harbin Institute of Techno

46、logy 1999; 31:104-6. </p><p>　　[15] Liu SY, Song XP, Wen BC. Catastrophe in fault developing process of rotor system. Journal of Northeastern University (Natural Science) 2004; 17:159-162. </p><p

47、>　　[16] Han J, Zhang RL. Rotating machinery fault mechanism and diagnostic technique. Beijing: China Machine Press, 1997. </p><p>　　[17] Chen AH. Research on some nonlinear fault phenomenon of rotating ma

48、chinery. Ph.D. thesis, Central South University of Technology, 1997. </p><p>　　[18]Zhang W, Zhang YX. Missile power system fault mechanism analysis and diagnosis technology. Xi’an: Northwest Industrial Univ

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

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

51、理研究，信號處理和特征提取，故障推理研究和設備開發(fā)等方面進行了出色的工作。概述了一些現(xiàn)有的信號處理和特征提取方法。對這些技術的優(yōu)點和缺點進行了討論。研究結果表明，基于智能信息融合的機械故障診斷專家系統(tǒng)與自我學習和自我更新能力，是機械設備狀態(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會議]責任同行審查</p

52、><p>　　關鍵詞：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)代科學技術的發(fā)展，機械和設備的功能變得越來越完善，并且機械結構變得更大型，集成，智能和復雜。因此，組件數(shù)量顯著增加，接合部件的精度要求也更加嚴格。相關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，甚至一個小的機械故障可能會導致嚴重的后果。因此，有效的早期故障檢測和診斷是機械正常運轉的關鍵。雖然在機械設計過程和制造過程中已經采用優(yōu)化技術來提高機械產品的質量，由于現(xiàn)代設備的復雜性，機械故障仍然難以避免。狀態(tài)監(jiān)測和故障診斷，以先進的科學技術為根本，作為一種有效的方式來預測潛在的故障和降低機器故障的成本。這就是所謂的出現(xiàn)在近三十年的機械設備故障診斷技術 [1，2]。</p><p>　　機械設備

54、故障診斷技術使用監(jiān)控機械運轉和固定分析和提取重要特征的測量值來校準關鍵部件的狀態(tài)。通過結合歷史數(shù)據(jù)，它可以定量的識別在目前條件下的關鍵部件，預測即將發(fā)生的異常和故障，并且預測它們未來的發(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>　　機械故障診斷的內容包含四個方面，包括故障機理研究，信號處理和特征提取，故障推理研究，以及設備狀態(tài)監(jiān)測和故障診斷的開發(fā)

55、。在過去的幾十年里，已經有許多研究者在此領域做了大量的工作。在這一領域一個簡明的研究評論已經被提出 [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。這些新的信號處理和特征提取方法的優(yōu)缺點，在這項工作中進行了討論。然后，簡要回顧了故障推理研究和診斷設備。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測系統(tǒng)。</p><p><b>　　2.故障機理研究</b><

56、/p><p>　　故障機理的研究是故障診斷的一個非常艱難和重要的基礎工程，就像病理研究對于醫(yī)療相同。美國學者John·Sohre，于1968年在美國機械工程研究所石油機械工程發(fā)表了“高速渦輪機械操作問題（失?。┑脑蚣疤幚怼币晃?，并對于典型的癥狀和可能引起機械故障的原因進行了一個清晰、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60年代至70年代期間Shiraki [8]在日本對

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

58、，操作及機器降解方面提供預防措施。 Xu等人[11]總結了旋轉機的常見故障。Chen等人[12]利用非線性動力學理論來分析了發(fā)電機軸振動問題的關鍵。他們建立了發(fā)電機轉子</p><p>　　3.先進的信號處理和特征提取方法</p><p>　　先進的信號處理技術被用于提取的原因是靈敏，通過各種信號分析技術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正在經歷，從時間領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，進行了大量的研究和應用進展。旋轉機械振動通常是強烈的諧波，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，頻譜分析，倒頻譜分析和解調技術），可以有效提取故障特征信息，因此它被廣泛的用于動力機械，尤其是在旋轉機械振動監(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，從機械設備中的信號測量也是千變萬化的，非平穩(wěn)的，非高斯分布的和非線性隨機的。尤其是當設備發(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，一些時頻細節(jié)不能在頻譜上反應，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)，他們被很快應用于機械故障診斷領域。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實驗方法建立模型。系統(tǒng)參數(shù)或系統(tǒng)狀態(tài)的變化可以直接反映設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、。這項技術是指模型的建立，參數(shù)估計，狀態(tài)估計，應用觀察員等。因為它要求準確的系統(tǒng)模型，這種方法對于實踐中的復雜設備在經濟上是不可行的。</p><p>　　模式識別進行集群描述為一系列的過程或事件。它主要分為統(tǒng)計方法和語言結構的方法。設備的故障診斷，可以作為模式識別的過程被確認，也就是說，它承認的故障，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，一些新技術也已經應用到旋轉機械故障診斷的領域中，如模糊集與神經網絡組合，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，當然儀器本身具有數(shù)

65、據(jù)分析和診斷功能。在線檢測和診斷系統(tǒng)是一個由感應器、數(shù)據(jù)采集、警報和互鎖保護和條件監(jiān)視組成的子系統(tǒng)，具有較強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等重

66、要設備。</p><p>　　由于采用了對設備運行狀況的監(jiān)控手段，網絡診斷中心可以通過網絡傳輸信息，隨時完成對設備運行的遠程檢測和監(jiān)控，遠程監(jiān)控系統(tǒng)還可以采集生產設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線，所有檢測儀器可以共享診斷數(shù)據(jù)。</p><p><b>　　6.結論</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)自身的波動和設備本身的干擾信號信息經常受到干擾，所以很重要的一點是要依靠先進的數(shù)據(jù)處理技術排除噪音以保證數(shù)據(jù)的準確性。不管是傳統(tǒng)的還是先進的故障診斷技術在各種應用中都已經取得了很大進步，按照信息系統(tǒng)的觀點，每項技術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。因此故障機制研究、信號處理和特征采集、故障成因研究和設備發(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確的評估，防止事故的發(fā)生，確保機械設備的正常運行，將由于設備故障造成的損失降低到最小程度。</p><p><b>　　致謝    </b>