【正文】 山東交通學(xué)院畢業(yè)設(shè)計 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。 2020 Published by Elsevier Ltd. Selection and/or peerreview under responsibility of [CEIS 2020] Keywords: Condition monitoring。 Fault diagnosis。 Vibration。 Signal processing 山東交通學(xué)院畢業(yè)設(shè)計 3 1. Introduction With the development of modern science and technology, machinery and equipment functions are being more and more perfect, and the machinery structure bees more largescale, integrated, intelligent and plicated. As a result, the ponent number increases significantly and the precision requirement for the part mating is stricter. The possibility and category of the related ponent failures therefore increase greatly. Malignant accidents caused by ponent 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 and the manufacturing procedure to improve the quality of mechanical products, mechanical failures are still difficult to avoid due to the plexity of modern equipments. The condition monitoring and fault diagnosis 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 socalled 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 analyze and extract important characteristics to calibrate the states of the key ponents. By bining the history data, it can recognize the current conditions of the key ponents quantitatively, predicts 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 aspects, including fault mechanism research, signal processing and feature extraction, fault reasoning research 外文翻譯原文及譯文 4 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. 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 topics are described in the point of future generation intelligent fault diagnosis and prognosis system. 山東交通學(xué)院畢業(yè)設(shè)計 5 2. Fault Mechanism Research Fault Mec