1、山东交通学院毕业设计 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 F
2、orce Radar Academy, Wuhan 430019, P. R. China 外文翻译原文及译文 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 mo
3、re 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 aspe
4、cts 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
5、. 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 diagnosis of mechanical equipments. 2011 Published by Elsevier Ltd. Selec
6、tion and/or peer-review under responsibility of CEIS 2011 Keywords: Condition monitoring； Fault diagnosis； Vibration； Signal processing 山东交通学院毕业设计 3 1. Introduction With the development of modern science and technology, machinery and equipment functions are becoming more and more perfect, and the ma
7、chinery 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 related component failures therefore increase greatly. Mal
8、ignant 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 running. Although optimization techniques have been carried o
9、ut 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 diagnosis based on advanced science and technology acts as
10、 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 technology uses the measurements of the monitored machiner
11、y 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 the impending abnormalities and faults, and prognoses th
12、eir 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 aspects, including fault mechanism research, signal processing and feature extraction, fault reasoning research
