外文翻译---机械状态监测和故障诊断的进展

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1、山东交通学院毕业设计Recent Progress on Mechanical Condition Monitoring and Fault diagnosisChenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei ZhangReliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, P. R. ChinaHuangpi Campus, Air Force R

2、adar Academy, Wuhan 430019, P. R. ChinaAbstractMechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressive deterioration of their state. The mechanical failures account for more than 60% of break

3、downs of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper discusses the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the fault mec

4、hanism research, signal processing and feature extraction, fault reasoning research and equipment development. An overview of some of the existing methods for signal processing and feature extraction is presented. The advantages and disadvantages of these techniques are discussed. The review result

5、suggests that the intelligent information fusion based mechanical fault diagnosis expert system with self-learning and self-updating abilities is the future research trend for the condition monitoring fault diagnosis of mechanical equipments. 2011 Published by Elsevier Ltd. Selection and/or peer-rev

6、iew under responsibility of CEIS 2011 Keywords: Condition monitoring; Fault diagnosis; Vibration; Signal processing1. Introduction With the development of modern science and technology, machinery and equipment functions are becoming more and more perfect, and the machinery structure becomes more lar

7、ge-scale, integrated, intelligent and complicated. As a result, the component number increases significantly 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 compone

8、nt faults occur frequently all over the world, and even a small mechanical fault may lead to serious consequences. Hence, efficient incipient fault detection and diagnosis are critical to machinery normal running. Although optimization techniques have been carried out in the machine design procedure

9、 and the manufacturing procedure to improve the quality of mechanical products, mechanical failures are still difficult to avoid due to the complexity of modern equipments. The condition monitoring and fault diagnosis based on advanced science and technology acts as an efficient mean to forecast pot

10、ential faults and reduce the cost of machine malfunctions. This is the so-called mechanical equipment fault diagnosis technology emerged in the nearly three decades 1, 2. Mechanical equipment fault diagnosis technology uses the measurements of the monitored machinery in operation and stationary to a

11、nalyze and extract important characteristics to calibrate 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 condition trends. By do

12、ing so, the optimized maintenance strategies can be settled, and thus the industrials can benefit from the condition maintenance significantly 3, 4. The contents of mechanical fault diagnosis contain four aspects, including fault mechanism research, signal processing and feature extraction, fault re

13、asoning 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 many researchers. A concise review of the research in this area has been presented by 5, 6. Some landmarks are discussed in this

14、 paper. The novel signal processing techniques are presented. The advantages and disadvantages of these new signal processing and feature extraction methods are discussed in this work. Then the fault reasoning research and the diagnostic equipments are briefly reviewed. Finally, the future research

15、topics are described in the point of future generation intelligent fault diagnosis and prognosis system. 2. Fault Mechanism Research 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, pu

16、blished a paper on 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 concise description of the typical symptoms and possible causes of mechan

17、ical failure. He suggested that typical failures could be classified into 9 types and 37 kinds 7. Following, Shiraki 8 conduced considerable work on the fault mechanism research in Japan during 60s-70s last century, and concluded abundant on-site troubleshooting experience to support the fault mecha

18、nism theory. BENTLY NEVADA Corporation has also carried out a series experiments to study the fault mechanism of the rotor-bearing system 9. A large amount of related work has been done in China as well. Gao et al. 10 researched the vibration fault mechanism of the high-speed turbo machinery, invest

19、igated the relationship between the vibration frequency and vibration generation, and drew up the table of the vibration fault reasons, mechanism and recognition features for subsynchronous, synchronous and super-synchronous vibrations. Based on the table they proposed, they have classified the typi

20、cal failures into 10 types and 58 kinds, and provided preventive treatments during the machine design and manufacture, Installation and maintenance, operation, and machine degradation. Xu et al. 11 concluded the common faults of the rotational machines. Chen et al. 12 used the nonlinear dynamics the

21、ory to analyze the key vibration problems of the generator shaft. They established a rotor nonlinear dynamic model for the generator to comprehensively investigate the rotor dynamic behavior under various influences, and proposed an effective solution to prevent rotor failures. Yang et al. 13 adopte

22、d vibration analysis to study the fault mechanism of a series of diesel engines. Other researchers have done a lot in the fault mechanism of mechanics since 1980s, and have published many valuable papers to provide theory and technology supports in the application of fault diagnosis systems 14-18. H

23、owever, most of the fault mechanism research is on the qualitative and numerical simulation stage, the engineering practice is difficult to implement. In addition, the fault information often presents strong nonlinear, non stationary and non Gaussian characteristics, the simulation tests can not ref

24、lect these characteristics very accurately. The fault diagnosis results and the application possibility may be influenced significantly. As a result, the development of the fault diagnosis technique still faces great difficulties. 3. Advanced Signal Processing and Feature Extraction Methods Advanced

25、 signal 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 contained in a wide range of signals, such as vibration, noise, temperature, pressur

26、e, strain, current, voltage, etc. The feature information of a certain fault can be acquired through signal analysis method, and then fault diagnosis can be done correspondingly. To meet the specific needs of fault diagnosis, fault feature extraction and analysis technology is undergoing the process

27、 from time domain analysis to Fourier analysis-based frequency-domain analysis, from linear stationary signal analysis to nonlinear and nonstationary analysis, from frequency-domain analysis to time-frequency analysis. Early research on vibration signal analysis is mainly focused on classical signal

28、 analysis which made a lot of research and application progress. Rotating 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 techniques, s

29、pectrum analysis, cepstrum analysis and demodulation techniques) can extract the fault characteristic information effectively, thus it is widely used in motive power machine, especially in rotating machinery vibration monitoring and fault diagnosis. In a manner of speaking, classical signal analysis

30、 is still the main method for mechanical vibration signal analysis and fault feature extraction. However, classical spectrum analysis also has obvious disadvantages. Fourier transform reflects the overall statistical properties of a signal, and is suitable for stationary signal analysis. In reality,

31、 the signals measured from mechanical equipment are ever-changing, non-stationary, non-Gaussian distribution and nonlinear random. Especially when the equipment breaks down, this situation appears to be more prominent. For non-stationary signal, some time-frequency details can not be reflected in th

32、e spectrum and its frequency resolution is limited using Fourier transform. New methods need to be proposed for those nonlinearity and non-stationary signals. The strong demand from the engineering practice also contributes to the rapid development of signal analysis. New analytical methods for non-

33、stationary signal and nonlinear signal are emerging constantly, which are soon applied in the field of machinery fault diagnosis. New methods of signal analysis are main including time-frequency analysis, wavelet analysis, Hilbert-Huang transform, independent component analysis, advanced statistical

34、 analysis, nonlinear signal analysis and so on. The advantages and disadvantages of these approaches are discussed below. 4. Research on Fault ReasoningAt present, many methods are adopted in the process of diagnostic reasoning. According to the subject systems which they belong to, the fault diagno

35、sis can be divided into three categories: (1) the fault diagnosis based on control model; (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

36、or experimental methods. The changes of system parameters or system status could directly reflect the changes of equipments physical process, and hence it is able to provide basis for fault diagnosis. This technology refers to model establishment, parameters estimation, status estimation, applicatio

37、n of observers, etc. Since it requires accurately system model, this method is not economically feasible for the complicated 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 structur

38、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 recognition methods, including bayes category, distance function category, fuzzy diagnos

39、is, 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 combination of fuzzy set and neural network, the dynamic pattern recognition based on hidden markov model, etc. 5.

40、Research and Development of Fault Diagnosis Devices 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 and diagnosis (including data collector

41、 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 certain ability for signal analysis and fault diagnosis. On-line monitoring and diagnosis system is usually equipped wit

42、h 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 DM2000 systems, America Westinghouse Company PDS system, the 5911

43、 system developed by ENTECK and IRD Company, Japan Mitsubishi MHM system, the Danish B&K Company B&K 3450 COMPASS system, etc. China has also successively developed large on-line monitoring and fault diagnosis system, which has been put into use on steam turbine and other important equipments. Based

44、 on the realization of condition monitoring of equipments, network diagnostics center can monitor and diagnose the operation 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

45、of production equipments, multiple diagnostic systems serve the same piece of equipment, and multiple devices share the same diagnostic system. 6. Conclusions To achieve a dynamic system condition monitoring and fault diagnosis, primary task is the need to get enough reliable characteristic informat

46、ion from the system. Due to the fluctuation of the system itself and the environment disturbance, reliable signal collection is seriously affected. It is therefore very urgent for advanced signal processing technology to eliminate noise to get true signal. No matter classical or advance fault diagno

47、sis techniques, they have achieved great progress in various applications. In the point of systematic view, every technology is a part of the whole diagnostic system, and the efficient fusion of these parts will provide best performance for the condition monitoring and fault diagnosis. Thus, the fau

48、lt mechanism research, signal processing and feature extraction, fault reasoning research and equipment development will connect even tighter to form an effective fault diagnostic expert system in the future. To realize the expert system, the core issue is to break through the bottleneck of knowledg

49、e acquisition, update the data model in a reliable manner and provide good generalization ability of the expert system. By doing so, the fault diagnostic expert system can offer accurate estimation of the potential abnormalities, and prevent them before breaking out to ensure the normal operation of

50、 the machines. Hence, the loss caused by the machine breakdowns can be minimized significantly. Acknowledgements This project is sponsored by the grants from the National Natural Sciences Foundation of China (NSFC) (No. 50975213). References 1 Wu XK. The fault diagnosis based on information fusion t

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59、 1997. 18Zhang W, Zhang YX. Missile power system fault mechanism analysis and diagnosis technology. Xian: Northwest Industrial University press, 2006.机械状态监测和故障诊断的最新进展Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei Zhang武汉理工大学，能源与动力工程学院，可靠性工程研究所，中华人民共和国，武汉，430063空军雷达学院，黄陂校区，中华人民共和国，武汉，430019摘

60、要机械设备被广泛应用于各种工业应用。一般在恶劣条件下工作，机械设备的状态会逐渐恶化。机械故障占超过60的系统故障。因此，即将到来的机械故障的识别系统，是防止系统故障的关键。本文讨论了在机械状态监测与故障诊断的最新进展。从故障机理研究，信号处理和特征提取，故障推理研究和设备开发等方面进行了出色的工作。概述了一些现有的信号处理和特征提取方法。对这些技术的优点和缺点进行了讨论。研究结果表明，基于智能信息融合的机械故障诊断专家系统与自我学习和自我更新能力，是机械设备状态监测和故障诊断未来研究的发展方向。2011年由爱思唯尔公司出版。选择（和/或）根据2011年控制工程与信息科学会议责任同行审查关键词：

61、状态监测，故障诊断，振动，信号处理1.介绍随着现代科学技术的发展，机械和设备的功能变得越来越完善，并且机械结构变得更大型，集成，智能和复杂。因此，组件数量显着增加，接合部件的精度要求也更加严格。相关组件故障的可能性和故障的种类因此也大大增加。组件故障所造成的恶性事故频繁发生在世界各地，甚至一个小的机械故障可能会导致严重的后果。因此，有效的早期故障检测和诊断是机械正常运转的关键。虽然在机械设计过程和制造过程中已经采用优化技术来提高机械产品的质量，由于现代设备的复杂性，机械故障仍然难以避免。状态监测和故障诊断，以先进的科学技术为根本，作为一种有效的方式来预测潜在的故障和降低机器故障的成本。这就是所

62、谓的出现在近三十年的机械设备故障诊断技术 1，2。机械设备故障诊断技术使用监控机械运转和固定分析和提取重要特征的测量值来校准关键部件的状态。通过结合历史数据，它可以定量的识别在目前条件下的关键部件，预测即将发生的异常和故障，并且预测它们未来的发展趋势。这样做，最优化维修策略可以被制定，因此，工业可以从状态监测中大大获益。 3，4。机械故障诊断的内容包含四个方面，包括故障机理研究，信号处理和特征提取，故障推理研究，以及设备状态监测和故障诊断的开发。在过去的几十年里，已经有许多研究者在此领域做了大量的工作。在这一领域一个简明的研究评论已经被提出 5，6。本文讨论了一些里程碑式的观点。介绍新型的信号

63、处理技术。这些新的信号处理和特征提取方法的优缺点，在这项工作中进行了讨论。然后，简要回顾了故障推理研究和诊断设备。最后，未来的研究课题中所描述的是下一代智能故障诊断和预测系统。2.故障机理研究故障机理的研究是故障诊断的一个非常艰难和重要的基础工程，就像病理研究对于医疗相同。美国学者JohnSohre，于1968年在美国机械工程研究所石油机械工程发表了“高速涡轮机械操作问题（失败）的原因及处理”一文，并对于典型的症状和可能引起机械故障的原因进行了一个清晰、简明的描述。他建议，典型故障可分为9个类型和37种7。之后，在上世纪60年代至70年代期间Shiraki 8在日本对于故障机理的研究工作做了很

64、大贡献，并总结了丰富的现场故障排除经验，以支持故障机制的理论。本特利内华达公司也进行了一系列实验研究转子 - 轴承系统的故障机制 9。大量的相关工作在中国也已经完成。Gao等人10研究了高速透平机械振动故障机理，探讨了振动频率和振动发电之间的关系，并拟定了振动故障原因，次同步、同步和超同步振动的机制和识别功能表。根据表格他们提出，他们已经将典型的故障分为10个类型和58种，并在机械设计与制造，安装和维护，操作及机器降解方面提供预防措施。 Xu等人11总结了旋转机的常见故障。Chen等人12利用非线性动力学理论来分析了发电机轴振动问题的关键。他们建立了发电机转子的非线性动力学模型，全面调查在不同

65、影响下转子的动态反应，并提出有效的解决方案，以防止转子故障。Yang等人13通过振动分析，研究了一系列的柴油发动机的故障机制。 20世纪80年代以来，其他研究人员在机械的故障机制中已经做了很多，并出版了许多有价值的论文，在故障诊断系统中的应用中提供了理论和技术支持14-18。然而，大多数的故障机理研究是在定性和数值模拟阶段，工程实践中是很难实施的。此外，故障信息往往呈现较强的非线性，非平稳和非高斯特性，模拟测试不能非常准确的反映这些特点。故障诊断结果和应用的可能性可能会影响显着。因此，故障诊断技术的发展仍然面临着很大的困难。3.先进的信号处理和特征提取方法先进的信号处理技术被用于提取的原因是灵敏，通过各种信号分析技术来处理测量信号到具体的故障。植物状态信息中包含着广泛的信号，如振动，噪声，温度，压力，应变，电流，电压等。可以通过信号分析方法获得一定的故障特征信息，然后可以做出相应的故障诊断。为了满足故障诊断的特殊需要，故障特征提取和分析技术正在经历，从时间领域分析到傅里叶频域分析，从线性平稳信号分析到非线性非平稳分析，从频域分析到时频分析的过程。振动信号分析的早期研究主要集中在传统的信号分析，进行了大量的研究和应用进展