【正文】 致謝 這個(gè)項(xiàng)目是由來自中國國家自然科學(xué)基金的資助（國家自然科學(xué)基金）（編號(hào)： 50975213）。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(diǎn)，每項(xiàng)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(shí)現(xiàn)條件監(jiān)控和故障診斷的保障。K 公司開發(fā)的 3450 指南針系統(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)據(jù)分析和診斷功能。設(shè)備的故障診斷，可以作為模式識(shí)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。這項(xiàng)技術(shù)是指模型的建立，參數(shù)估計(jì)，狀態(tài)估計(jì)，應(yīng)用觀察員等。（ 2）基于模式識(shí)別的故障診斷 。對于非平穩(wěn)信號(hào)和非線性信號(hào)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。尤其是當(dāng)設(shè)備發(fā)生故障，這種情況出現(xiàn)的更加突出。在某種意義上說，傳統(tǒng)的信號(hào)分析，仍然是機(jī)械振動(dòng)信號(hào)分析和故障特征提取的主要方法。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(shí)間領(lǐng)域分析到傅里葉頻域分析，從線性平穩(wěn)信號(hào)分析到非線性非平穩(wěn)分析，從頻域分析到時(shí)頻分析的過程。因此，故障診斷技術(shù)的發(fā)展仍然面臨著很大的困難。 20 世紀(jì) 80年代以來，其他研究人員在機(jī)械的故障機(jī)制中已經(jīng)做了很多，并出版 了許多有價(jià)值的論文，在故障診斷系統(tǒng)中的應(yīng)用中提供了理論和技術(shù)支持 [1418]。 Xu 等人 [11]總結(jié)了旋轉(zhuǎn)機(jī)的常見故障。本特利內(nèi)華達(dá)公司也進(jìn)行了一系列實(shí)驗(yàn)研究轉(zhuǎn)子 軸承系統(tǒng)的故障機(jī)制 [9]。美國學(xué)者 John這些新的信號(hào)處理和特征提取方法的優(yōu)缺點(diǎn)，在這項(xiàng)工作中進(jìn)行了討論。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 。通過結(jié)合歷史數(shù)據(jù)，它可以定量的識(shí)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。雖然在機(jī)械設(shè)計(jì)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。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。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。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。 17:159162. [16] Han J, Zhang RL. Rotating machinery fault mechanism and diagnostic technique. Beijing: China Machine Press, 1997. [17] Chen AH. Research on some nonlinear fault phenomenon of rotating machinery. . thesis, Central South University of Technology, 1997. [18]Zhang W, Zhang YX. Missile power system fault mechanism analysis and diagnosis technology. Xi’an: Northwest Industrial University press, 2020. 外文翻譯原文及譯文 14 機(jī)械狀態(tài)監(jiān)測和故障診斷的最新進(jìn)展 Chenxing Sheng, Zhixiong Li, Li Qin, Zhiwei Guo, Yuelei Zhang 武漢理工大學(xué)，能源與動(dòng)力工程學(xué)院，可靠性工程研究所，中華人民共和國，武漢， 430063 空軍雷達(dá)學(xué)院，黃陂校區(qū)，中華人民共和國，武漢， 430019 山東交通學(xué)院畢業(yè)設(shè)計(jì) 15 摘要 機(jī)械設(shè)備被廣泛應(yīng)用于各種工業(yè)應(yīng)用。an Jiaotong University, 1993. [11] Xu M, Zhang RL. Equipment fault diagnosis manual. Xi’an: Xi39。 7: 157172. [6] DaneshiFar Z, Capolino GA, Henao H. Review of failures and condition monitoring in wind turbine generators. 19th International Conference on Electrical Machines. Rome, Italy。 (2) the fault diagnosis based on pattern recognition。 2020 Published by Elsevier Ltd. Selection and/or peerreview under responsibility of [CEIS 2020] Keywords: Condition monitoring。 Fault diagnosis。 (3) the fault diagnosis based on artificial intelligence. Among them, the fault diagnosis based on control model needs to establish model through theoretic 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, 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 Mits