Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

Development and performance evaluation of tropospheric scintillation model on Ku-band satellite link over Akure,Nigeria

Existing amplitude scintillation prediction models often perform less satisfactorily when deployed outside the regions where they were formulated. This necessitates the need to evaluate the performance of scintillation models developed in one region using data data from other regions while documenting their relative errors. Due to its variation with elevation angle, frequency, other link parameters and meteorological factors, we employed three years (January 2016 to December 2018) of concurrently measured satellite radio beacons and tropospheric weather parameters to develop a location-based amplitude scintillation prediction model over the Earth-space path of Akure (7.17^oN, 5.18^oE), South-western Nigeria. The satellite beacon measurement used Tektronix Y400 NetTek Analyzer at 1 s integration time while meteorological parameters, namely; temperature, pressure and relative humidity were measured using Davis Vantage Vue weather station at 1 min integration time. Comparative study of the model’s performance with nine (9) existing scintillation prediction models indicates that the best and worst performing models, in terms of root mean square error (RMSE), are the Statistical Temperature and Refractivity (STN) and direct physical and statistical prediction (DPSP) models with values 11.48 and 51.03 respectively. Also, worst month analysis indicates that April, with respective enhancement and fade values of 0.88 and 0.90 dB for 0.01% exceedance, is the overall worst calendar month for amplitude scintillation.     

一种基于虚拟页地址映射的NAND Flash管理算法

本文提出了一种基于虚拟页地址映射的NAND Flash管理算法.该算法通过定义坏块表、对应表等结构，以及设计的坏块替换策略和虚拟页地址到实际物理页地址的转换算法，实现上层软件采用虚拟地址对NAND Flash的无坏块连续页地址访问.该算法是一种高效的地址映射算法，能高效地对数据进行索引，占用SRAM空间较少，使系统达到高性能，并使得闪存使用的更加稳定持久.     

一种卫星间跟瞄方向的控制策略与实现

进行自由空间的光通信或空间对抗，需要在卫星间实现精确的跟踪与瞄准。因为地面测控站的设置非常有限，指令与数据注入的范围受到很多限制，所以要求这种跟瞄在很多时候是无主控制状态下完成的。提出了一种利用在地面测控站的范围内向星上数管中心注入目标卫星、本体卫星的轨道参数，其它时间依靠自主导航飞行和GPS修正的方法，结合本体星的姿态测量、振动测量实现卫星瞄准指向的方位角与俯仰角的精确控制的方法，提高了在地面站无法注入指令与数据时卫星的捕获概率。通过仿真，将计算结果与卫星仿真工具软件STK的数据进行比较，结果一致。最后在嵌入式DSP平台上实现了这一策略。     

基于关联分析的机载电子设备SRU级故障诊断方法

针对机载设备二类检测设备将故障隔离到SRU的要求,对LRU的内部结构进行了分析,提出了点、底项、组底项的概念,用关联值表示点与点之间、点与SRU之间及组底项之间的关联关系;针对多SRU结构UUT,提出逐级排除法故障诊断方法.     

专科英语、本科英语及成人专科英语的定位和相互关系

专科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)。而本科(大学)英语在理论体系上属于普通英语(GE)。本文从ESP理论的角度探讨了专科英语,本科英语及成人专科英语的定位和相互关系。     

一种用于人体早期病变诊断的仪器

根据探针在人体穴位电流量的变化情况 ,具体分析了该仪器电路设计的工作原理、电路结构及具体的使用方法。     

用亚稳态正电子素研究络合的瞬态反应（Ⅲ）

本文用自制的装有 BaF_2探头的高探测效率的正电子湮没寿命谱仪测量了在室温下正电子素 o-Ps 与 p-RPhG 及 m-RPhG(R=NO_2,Cl,H,CH_3,cH_3O)在20%(V/V)二氧六环水溶液中的络合形成速度常数。实验结果表明,这些o-Ps-M 络合形成速度常数 K 值均与作为配位体的 p-RPhG、m-RPhG 的碱性解离常数 K 的负对数 pK_2 呈线性函数。这表明上述瞬态络合反应满足线性自由能关系,由此可推断出上述络合反应的一些重要性质。