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China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one. 相似文献
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Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low. 相似文献
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为提高以空间站为代表的大型载人空间基础设施运行效率和运营效益,本文在研究国际空间站(ISS)在轨运营模式及商业化做法的基础上,针对我国现有空间基础设施的在轨运营特点、国家战略和社会商业化需求,提出了引入社会资本建立商业化运营模式和建立商业成果转化基金等建议,成立专门的商业化运营管理机构,进行统筹管理和商业化运营,探索形成一套职责清晰、高质高效、效益突出的商业化运营管理体系,既保证空间基础设施在轨健康运行,高效开展各类空间应用,又降低建设和运营成本。在孕育商业模式和创新项目促进经济社会发展的同时,也具有重要的现实意义和深远的社会效益。 相似文献
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针对某型超精密机床液体静压导轨温度控制进行深入研究,提出多种不同的温度控制方法,并应用ANSYS仿真软件分别对液体静压导轨热变形进行了仿真,得出了不同温度控制方法下导轨的热变形大小。仿真结果显示,针对不同使用环境,通过不同的温度控制方法,对液体静压导轨进行热变形控制,从而满足超精密机床的使用要求,提高超精密机床的加工精度。。 相似文献
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