Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network

The variations in gas path parameter deviations can fully reflect the healthy state of aero-engine gas path components and units; therefore, airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis. In the past, the airlines could not obtain deviations autonomously. At present, a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations. However, it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks. To obtain monitoring autonomy of each aero-engine model, it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models. This paper adopts the Residual-Back Propagation Neural Network (Res-BPNN) to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy (MK-MMD) adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space (RKHS) for discrepancy measurement. To further reduce the distribution discrepancy of each aero-engine model, the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible, and then the learned features can be confused. Through the above methods, domain-invariant features can be extracted, and the optimal adaptation effect can be achieved. Finally, the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.     

内镀金刚石铰刀制造中开刃技术

内镀金刚石铰刀在脱模和刀杆的加工处理后,还要进行开刃,目的是为了让被金属镀层包裹的金刚石颗粒暴露出合适的高度,使之具有切削能力和容屑能力.本文主要介绍了几种开刃方法,并进行对比,最后对该领域的新技术进行展望.     

ESP与高职高专英语教学

高职高专英语在理论上被界定为专门用途英语(ESP)分支下,介于学术英语(EAP)与职业英语(EOP)之间的业务用途英语(EPP).本文从理论上探讨了ESP与高职高专英语教学之间的关系.     

非工作状态下电液伺服机构运动分析

针对电液伺服机构安装到运载火箭后存在非工作状态下承受外力并产生被动运动的现状,分析伺服机构的受力情况并构建内部液体流动回路,研究非工作状态下伺服机构在外力作用时的运动特性。发现伺服阀零位特性以及伺服机构高压回路密封性能是影响伺服机构非工作状态下运动特性的主要因素。在外力作用下发生运动时油缸某一侧往往处于抽真空状态。使用零位特性良好的伺服阀以及高压回路泄漏小的伺服机构,在外力作用下将处于双向不动或单向可动的状况。利用典型产品开展了物理试验,验证了结论的有效性。     

基于北斗短报文的远程图像通信系统设计

北斗短报文功能在国防、民生和应急救援等领域都具有很强的应用价值。利用北斗短报文通信费用低廉的优点,设计了一款可进行远程图像传输的系统。针对北斗短报文通信的局限性,提出了相应的数据传输机制以保证远程数据传输的可靠性与有效性,并对系统功能进行了验证。测试结果表明,系统能够较快地完成图像数据传输的功能。     

开绕组电机驱动用双逆变器死区效应补偿方法

与传统两电平逆变器的死区效应不同,开绕组电机驱动系统中双逆变器的开关死区将导致零序电压和零序电流,使绕组电流出现低频次谐波,影响系统的性能。提出了一种双逆变器开关死区导致的零序电压的补偿方案,通过扩展占空比最大的一路开关信号的导通时间,来插入一个补偿电压矢量,利用补偿电压矢量产生的零序电压抵消开关死区导致的零序电压,从而抑制系统的零序电压和零序电流。通过在开绕组感应电机驱动系统中的仿真和试验,验证了死区补偿方法的有效性。     

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

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.     

Experimental investigation on dynamic response of flat blades with underplatform dampers

One test rig with three blades and two Under-Platform Dampers (UPDs) is established to better understand the dynamical behavior of blades with UPDs. A pre-loaded spring is used to simulate the centrifugal load acting on the damper, thereby achieving continuous adjustment of the pressing load. UPDs with different forms, sizes and materials are carefully designed as experimental control groups. Noncontact measurement via a laser Doppler velocimeter is employed and contact excitation which is performed by an electromagnetic exciter is adopted to directly obtain the magnitude of the excitation load by a force sensor mounted on the excitation rod. Particular attention is paid to the influence of the contact status of the contact surfaces, e.g. the pressure-sensitive paper is used to measure the effective contact area of the UPDs. The experimental variables are selected as the centrifugal force, the amplitude of the excitation force, the damper mass, the effective contact area, and the damper material. The Frequency Response Function (FRF) of the blade under different experimental parameters is obtained by slow frequency sweep under sinusoidal excitation to study the influence of each parameter on the dynamic characteristics of the blade and the mechanism analysis is carried out combined with the experimental results.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

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.     

融合静电信号和气路参数的发动机性能评估方法

为了研究静电监测技术对航空发动机的监测效果,开展了航空涡喷发动机静电监测台架实验,获得了140个完整试车阶段的静电信号数据。针对发动机传统气路性能评估方法的信息源有限的问题,提出了一种融合静电信号和气路参数的发动机性能评估流程和方法,进一步采用了基于逻辑回归模型的评估算法对发动机的综合性能进行量化评估,通过融合试车实验的静电数据和性能参数,对发动机的健康状态评价方法进行研究。结果表明:当健康值为0.9以上可以认为发动机为健康状态,当健康值小于0.3时可以认为发动机已经发生了较为严重的退化,所提出的融合评估方法要优于传统的性能参数评估方法,能够提前预警气路性能严重退化。