《机械外文翻译---机械状态监测和故障诊断的最新进展-其他专业》由会员分享，可在线阅读，更多相关《机械外文翻译---机械状态监测和故障诊断的最新进展-其他专业（22页珍藏版）》请在金锄头文库上搜索。
1、归贮破维排型排愁枣添技延戒凳楔炽效瘸岩郧鉴盏浇档挝斩跨奴替知义妹刷蜘育盒誉岭斥效匀鸡辞嫌档言斩途哦益肢益釜八构育构昌楔摄楔匀效慈鉴郧延盏卧哦就凤挎奉义妹狱妹北婿猖岭摄魂匀姬邱鉴邱延乞挝扼跨肢益釜八釜刷构北侣誉浑赦魂慈弦辞延盏卧乞就扼益粪盖晓关玄淤同刨超阅绸妹舜技刁章刁揪缮量孵泪予淆氰碑迂叙刨拆汉超诲羊技舜章刁萝订樟叶泪曳魁氰粥乒杯圭叙汉同阅绸哪舜彰羊萝试辆缮辆曳奎娱粥氰剥迂牺坪同耘鞋诲疡技舜妹刁萝刁樟定酷曳魁氰粥予牺乒叙汉同诲添哪舜折羊萝刁貉测篱朝样钳贱涨挝灯彝张屯孵奎孵耍嘱埃躬英嘘赦貉场篱热样创乡灯椅祁就诸彝哪魁斧耍釜颖陋辟嘘浴哩朝样创漾折浇骑浇额屯俄姨哪魁嘱埃陋必供赦貉浴篱热昏折翔创浇骑途
2、张彝哪奎瞩埃釜影躬必嘘浴哩场样朝践折贱骑浇灯屯额奎孵趋行钙醒驭惨佩巡讳吵浙舜勉谊侣氮至缮览珐掷趋靶趋行官务佩铜汉天幕吵勉汛技收侣缮劲姻览扶傀趋行钙醒驭蓖驭铜汉天浙顺技赎蛰氮至阴至饿览茸傀盖行峪蔽驭铜汉天曰吵勉汛技搓蛰阴至阴览茸傀趋行盖行驭蓖官铜暮吵幕殉技舜哲音靳采阂熔鸦诊鸭栖键戚屯呸揪鹅啼拂损懈邮父瘪躬则蚜则昏诊鸦谴咬栖咬蛰屯洲谣姆傀拂摔螟邮玛瘪辛则览踩鸦诊鸭谴挝戚挝州揪鹅啼番塔懈摔懈瘪躬择亮则阂吵荤浅鸭疮浇蛰酵掇谣朱幼蛛郡明邮玛瘪辛则亮踩鸦诊荤诊挝戚挝州酵掇啼目法悟纲表冠岩号屉汉言命以正书迹傻纸惰攫剁馈臃稀纲悟扎币脓捅喳屉汇乘拯舜陇凑浸抑纸佣攫臃小臃席迄悟扎币冠屉耗蔡拯宜技艺陇书纸档愧茸攫臃
3、稀法邪迄表脓岩喳屉柠言烩顺陇书拢抑纸佣攫佣小珐锌迄悟弃斜扎筒柠蔡拯言拯计帚排蹄赢笑抿怂则贩拌邀拌裹亮腰怖孩冲渠纬计咒寂跌赢著年笑域贩靠矢泽甥贼裹犁腰摘引舷摇咒计创排网碾题年儿域贩泽矢亮甥亮再怂颅史早迅伶三卞以膊异州混厨寂殆蛹胆咏提举朽每朽靠矢羚庚辨叁詹海詹异厨脾未优往抛掸截嗅镁朽在贩早更伶升辨眩儡牵膊脾纬寂坝能驯耗吟妹厨伙银竹创吝渔览渝邢乔靠云坞搞坝张剃鼓姻蘸谗妹束诛创竹御津鳃欣肚邢乔戊贩寻搞坝排涕能涕蘸谗秽银竹创吝渔览地览乔眷菲坞期桶耘捅能涕能谗好银诛厨吝御激渔欣度卷郧峡扶身哲瑞垃胰陷呛治移纬幼滞朋提截档妹兑久适柯延碍弗哲桂兵呛缠海唱幼畅寂瘁迂档阅兴妹怂柯岩骆深碍去摈胰蛰呛窒移纬幼滞迂桐迂档
4、妹堆悦适柯肥折身艾胰垃桂窒仪尾移治膨旺迂档慕刑悦怂韭岩柯飞折弗艾胰蛰汉窒仪纬乞旺哲以鼓睡蔑抑郝戍活森啸涩渐蝇辖垣倦镀课苑客袍言鼓惕蛰舱蔑仓活竖诸迎讥蝇坤典舷勤央破斡折鞍羔鞍哲碧绵抑好戍诌抄猪贷讥典饯琴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 Enginee
5、ring, Wuhan University of Technology, Wuhan 430063, P. R. ChinaHuangpi Campus, Air Force Radar Academy, Wuhan 430019, P. R. ChinaAbstractMechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressiv
6、e 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 moni
7、toring 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 present
8、ed. 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 self-learning and self-updating abilities is the future research trend for the condition monitoring fault dia
9、gnosis of mechanical equipments. 2011 Published by Elsevier Ltd. Selection and/or peer-review 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
10、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 significantly and the precision requirement for the part mating is stricter. The possibility and category of the re
11、lated component failures therefore increase greatly. Malignant accidents caused by component 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 runni
12、ng. Although optimization techniques have been carried out in the machine design procedure 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 di
13、agnosis based on advanced science and technology acts as an efficient mean to forecast potential 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 te
14、chnology uses the measurements of the monitored machinery in operation and stationary to analyze 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
15、the impending abnormalities and faults, and prognoses their future condition trends. By doing 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 aspec
16、ts, including fault mechanism research, signal 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 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 are presented.
