【正文】 ture 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, the signals measured from mechanical equipment are everchanging, nonstationary, nonGaussian distribution and nonlinear random. Especially when the equipment breaks down, this situation appears to be more prominent. For nonstationary signal, some timefrequency details 外文翻譯原文及譯文 8 can not be reflected in the spectrum and its frequency resolution is limited using Fourier transform. New methods need to be proposed for those nonlinearity and nonstationary signals. The strong demand from the engineering practice also contributes to the rapid development of signal analysis. New analytical methods for nonstationary 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 timefrequency analysis, wavelet analysis, HilbertHuang transform, independent ponent analysis, advanced statistical analysis, nonlinear signal analysis and so on. The advantages and disadvantages of these approaches are discussed below. 山東交通學(xué)院畢業(yè)設(shè)計(jì) 9 4. Research on Fault Reasoning At present, many 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 model。山東交通學(xué)院畢業(yè)設(shè)計(jì) 1 Recent Progress on Mechanical Condition Monitoring and Fault diagnosis Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei Zhang Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, P. R. China Huangpi Campus, Air Force Radar Academy, Wuhan 430019, P. R. China 外文翻譯原文及譯文 2 Abstract Mechanical 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 breakdowns 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 mechanism 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 suggests that the intelligent information fusion based mechanical fault diagnosis expert system with selflearning and selfupdating abilities is the future research trend for the condition monitoring fault diagnosis of mechanical equipments. 169。 Vibration。K Company Bamp。 11: 2223. [8] Shiraki T. Mechanical vibration lectures. Zhengzhou: Zhengzhou Mechanical Institute。 10: 106368. 山東交通學(xué)院畢業(yè)設(shè)計(jì) 13 [13] Yang JG, Zhou YC. Internal bustion engine vibration monitoring and fault diagnosis. Dalian: Dalian Maritime University Press, 1994. [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 Technology 1999。機(jī)械故障占超過 60％的系統(tǒng)故障。概述了一些現(xiàn)有的信號處理和特征提取方法。2020 年由愛思唯爾公司出版。組件故障所造成的惡性事故頻繁發(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。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]。 [3，4]。本文討論了一些里程碑式的觀點(diǎn)。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)。他建議，典型故障可分為 9 個類型和 37 種 [7]。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。他們建立了發(fā)電機(jī)轉(zhuǎn)子的非線性動力學(xué)模型，全面調(diào)查在不同影響下轉(zhuǎn)子的動態(tài)反應(yīng)，并提出有效的解決方案，以防止轉(zhuǎn)子故障。此外，故障信息往往呈現(xiàn)較強(qiáng)的非線性，非平穩(wěn)和非高斯特性，模擬測試不能非常準(zhǔn)確的反映這些特點(diǎn)。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，應(yīng)變，電流，電壓等。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。傅立葉變換反映信號的整體統(tǒng)計(jì)特性，適用于平穩(wěn)信號分析。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。 山東交通學(xué)院畢業(yè)設(shè)計(jì) 19 目前，許多方法在診斷推理過程中被采用。其中，基 于控制模型的故障診斷需要通過理論或?qū)嶒?yàn)方法建立模型。 模式識別進(jìn)行集群描述為一系列的過程或事件。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。這些 軟件主要是美國 BENTLY 公司開發(fā)的 3300, 3500 and DM2020 系統(tǒng)，美國西屋公司開發(fā)的 PDS系統(tǒng)， ENTECKamp。 由于采用了對設(shè)備運(yùn)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(yùn)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(yùn)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)。實(shí)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