基于小波时频图和卷积神经网络的断路器故障诊断分析

高压断路器操动机构振动信号为非平稳性信号,蕴含着丰富的操动机构工作状态的信息,对操动机构工作状态的检验辨识具有重大意义。提出一种基于小波时频图和卷积神经网络的断路器故障诊断方法。对操动机构振动信号进行连续小波变换生成时频图(CWT),并对时频图进行统一压缩预处理;将预处理后的时频图作为特征图输入卷积神经网络AlexNet模型;通过对网络参数的调整,逐步改进网络模型,有监督地实现对操动机构故障状态的辨识诊断。结果表明,该方法能够有效地运用于断路器操动机构故障辨识诊断,与小波频带能量-RBF、小波频带能量-SVM的故障识别相比,故障识别准确率最高。     

域名管理体系与注册保护初探

域名具有重要的品牌价值,研究域名管理体系有利于开展域名注册与保护。现行域名注册规则容易导致域名与字号、商标、姓名等传统权利的冲突。互联网域种类繁多、层级结构复杂,企业应当建立域名注册评估机制、抢注侵权监测机制,加强域名注册与维权保护。     

基于优化遗传算法改进的小波网络预测锰系磷化膜的耐蚀性

针对小波网络训练速率较慢、结构不易确定等问题,结合实验选取的因素(包括输入参数和输出结果),通过仿真确定了小波网络的结构为3-7-1。接着,利用优化遗传算法对小波网络进行改进,修正其初始权值和因子。通过仿真证实了改进的小波网络具备更强的寻优能力和更快的收敛速率。最后,利用改进的小波网络预测锰系磷化膜的耐蚀性。结果表明:改进的小波网络可以更好地拟合样本数据,能够进行较准确的预测。     

Scaling behavior of dynamic hysteresis of PMN-PT relaxor ferroelectric ceramics near the morphotropic phase boundary

Due to the feature of domains, ferroelectric materials display hysteresis behavior with respect to the change of the applied electric field. Every ferroelectric material has its own unique hysteresis loop reflecting the information of domain reversal under an electric field. In this work, the scaling behaviors of dynamic hysteresis in relaxor (1-x)Pb(Mg_1/3Nb_2/3)O₃-xPbTiO₃ (PMN-PT) ceramics with different compositions were studied systemically. Our results showed that the evolution of scaling behavior in PMN-PT ceramics can be divided into three stages, which is independent of the phase structure of the ceramics and the testing electric field frequency. The relationship between hysteresis area <A> and field amplitude E₀ obeys the power law <A>∝ in the low and high E₀ regions, where the reorientations of 180° and non-180° domain are dominant, respectively. However, the dynamic hysteresis area <A> does not follow the power law in the intermediate E₀ regions, which is attributed to the interaction of different domain reversal mechanisms. Furthermore, the hysteresis area <A> decreases gradually with increasing frequency at a certain E₀ and the time-dependent domain reversal process was also discussed.     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.      

Elp3 modulates neural crest and colorectal cancer migration requiring functional integrity of HAT and SAM domains

Cell migration is a finely tuned biological process that often involves epithelial-mesenchymal transition (EMT). EMT is typically characterized by the upregulation of mesenchymal markers such as Snail1. This process has been shown to be of critical importance to normal developmental processes, including neural crest migration and invasion. Interestingly, similar mechanisms are utilized in disease processes, such as tumor metastasis and migration. Notably, EMT and EMT-like processes confer tumor cells with the ability to migrate, invade, and adopt stem cell-like properties that largely account for immunosuppression and tumor recurrence. Therefore, identifying sensitive EMT markers may contribute to cancer prognosis and diagnosis in many facets. Previously, we showed that Elp3 plays an essential role during neural crest migration by stabilizing Snail1. In the current study, we further elucidate the molecular mechanism underlying colorectal cancer migration. We found that mElp3 exerted an identical function to xElp3 in modulating neural crest migration, and both HAT and SAM domains are imperative during this migratory process. Interestingly, overexpression of mElp3 in SW480 cells promoted cell migration and invasion, and we further showed that Elp3 stabilized Snail1 requiring integrity of both SAM and HAT domains. Our findings warrant further exploration of the specific target of Elp3 in cancer cells.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

鄂尔多斯盆地东缘临兴区块煤系叠置含气系统及其兼容性

叠置含气系统及其兼容性是煤系多类型非常规天然气合采可行性判识基础。基于临兴区块地层压力、天然气地球化学及生产数据解译,划分了叠置独立含气系统,探讨了含气系统间兼容性。基于地层压力梯度差异认为目标层段至少存在7套独立含气系统;基于产层的天然气甲烷同位素差异,认为太2段、山2~山1段、盒8~盒6段、盒3段等4套层系互不连通;基于天然气生产曲线差异,识别出山西组与石盒子组为2套独立含气系统。综合将研究区自上而下划分为千1~千4段、千5段、盒1~4段、盒5~8段、山1~太1段、太1~太2、本1段及本2段等独立含气系统。提取了地层压力梯度、气层厚度、渗透率及可动水量等影响合采兼容性关键因素,采用最优分割分类方法,指出研究区含气系统兼容性最佳分类数为5~6类,千5段、盒1~4段、盒5~8段等3套叠置含气系统合采兼容性较好。