Spreading rate dependence of morphological characteristics in global oceanic transform faults

We quantified the systematic variations in global transform fault morphology,revealing a first-order dependence on the spreading rate.(1)The average age offset of both the full transform and transform sub-segments decrease with increasing spreading rate.(2)The average depth of both the transform valley and adjacent ridges are smaller in the fast compared to the slow systems,reflecting possibly density anomalies associated with warmer mantle at the fast systems and rifting at the slow ridges.However,the average depth difference between the transform valley and adjacent ridges is relatively constant from the fast to slow systems.(3)The nodal basin at a ridge-transform intersection is deeper and dominant at the ultraslow and slow systems,possibly reflecting a lower magma supply and stronger viscous resistance to mantle upwelling near a colder transform wall.In contrast,the nodal high,is most prominent in the fast,intermediate,and hotspot-influenced systems,where robust axial volcanic ridges extend toward the ridge-transform intersection.(4)Statistically,the average transform valley is wider at a transform system of larger age offset,reflecting thicker deforming plates flanking the transform fault.(5)The maximum magnitude of the transform earthquakes increases with age offset owing to an increase in the seismogenic area.Individual transform faults also exhibit significant anomalies owing to the complex local tectonic and magmatic processes.

CENOZOIC ALTYN TRANSFORM FAULT OF THE NORTHERN PART OF THE TIBETAN PLATEAU

The transform fault is essentially a displacement fault whose terminal part is adjusted by other tectonic types, its displacement component is absorbed by other structures intersected with it by high angles or meet at right angles. The main elements of transform fault are the sleep\|dipping displacement faults and the adjusted structures intersected with it at high angles. According to the combination of tectonic features formed by its two ends of displacement fault and the structures intersected with it, the transform fault can be divided into three types, including the adjusted transform fault of extensional normal fault, the adjusted transform fault of compressive fold and thrust fault, and the compound transform fault. The transform fault is different from the displacement fault, its horizontal displacement may be increased or decreased or not be changed at all as the time of fault movement extended, but for parallel displacement the dislocation will be increased. Therefore, the study of transform fault is very important for the recognition of long time disputed displacement components of huge displacement fault. The traditional Altyn fault is the adjusting fault of the compression deformation of the Western Kunlun and Northern Qilian mountains of the northern margin of the Tibetan Plateau since Cenozoic.

Microearthquake reveals the lithospheric structure at midocean ridges and oceanic transform faults

Mid-ocean ridge and oceanic transforms are among the most prominent features on the seafloor surface and are crucial for understanding seafloor spreading and plate tectonic dynamics,but the deep structure of the oceanic lithosphere remains poorly understood.The large number of microearthquakes occurring along ridges and transforms provide valuable information for gaining an indepth view of the underlying detailed seismic structures,contributing to understanding geodynamic processes within the oceanic lithosphere.Previous studies have indicated that the maximum depth of microseismicity is controlled by the 600-℃isotherm.However,this perspective is being challenged due to increasing observations of deep earthquakes that far exceed this suggested isotherm along mid-ocean ridges and oceanic transform faults.Several mechanisms have been proposed to explain these deep events,and we suggest that local geodynamic processes(e.g.,magma supply,mylonite shear zone,longlived faults,hydrothermal vents,etc.)likely play a more important role than previously thought.

基于MTF-Swin Transformer的风机齿轮箱故障诊断

针对风机齿轮箱实际工况复杂多变及含有强噪声,传统故障诊断方法对风机齿轮箱故障诊断识别准确率较低的问题,文章提出了MTF-Swin Transformer风机齿轮箱故障诊断模型。首先,采用马尔科夫变迁场(MTF)图形编码方法将原始一维振动时序信号转化为具有关联时间信息的二维特征图谱;然后,将特征图谱作为Swin Transformer模型的输入,基于自注意力机制进行自动特征提取;最后,实现对不同故障类型的分类。仿真结果表明,该方法对齿轮箱故障诊断准确率达到了99.48%,证明了该方法的有效性和优越性。

宽卷积局部特征扩展的Transformer网络故障诊断模型

视觉Transformer网络的高精度诊断性能依赖于充分的训练数据,利用卷积网络在提取局部特征上的优势,构造能同时描述故障局部和全局特征的提取层,提高诊断模型的抗噪声干扰能力。首先,引入卷积网络模块将原始振动信号转换为Transformer网络可以直接接收的特征向量,提取故障局部特征,并通过增加卷积网络的感受野。然后,结合Transformer网络多头自注意力机制生成的全局信息,构建能同时描述故障局部和全局特征的特征向量。最后,在Transformer网络的预测层,利用高效通道注意力机制对特征向量的贡献度进行自动筛选。在西储大学(CWRU)轴承数据集上的故障诊断结果表明,在信噪比-4 dB的噪声干扰下,改进后的Transformer网络轴承故障诊断模型的准确率达90.21%,与原始Transformer模型相比,准确率提高了13.2%,在噪声环境下表现出优异的诊断性能。

基于DRSN融合Transformer编码器的轴承故障诊断方法研究

针对轴承故障在复杂工况环境中诊断准确率低和泛化性能弱的问题,提出了一种基于深度残差收缩网络(deep residual shrinkage network,DRSN)融合Transformer编码器的轴承故障诊断方法。首先,采用DRSN通过软阈值模块自动去掉振动信号中的噪声信息,并使用注意力机制增强提取到的特征;然后,采用Transformer编码器来进一步解决振动信号中的长期依赖性问题;最后,利用Softmax函数实现多故障模式识别。在凯斯西储大学轴承数据集上通过不同噪声等级对提出的模型进行测试,实验结果表明,该方法实现了对轴承故障分类,强噪声环境下准确率更高,训练时间更快。

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.

Improved BP Neural Network for Transformer Fault Diagnosis

The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method (RTR). Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR.

基于Transformer的多标签工业故障诊断方法研究

工业故障数据的多维性、类不均衡性和并发性为工业故障诊断带来了三大挑战:一是从多维传感器数据中提取故障特征过度依赖于专家知识;二是不同类型故障样本之间的极端类不均衡性严重限制了分类器的性能;三是多个类型的故障可能同时发生增加了故障诊断问题的复杂性。为了应对这些挑战,提出了一种基于多重自注意力机制改进的Transformer多标签故障诊断模型。结合自适应合成采样(adaptive synthetic sampling,ADASYN)和Borderline-SMOTE1组合过采样方法,充分利用Transformer编码器-解码器结构以及注意力机制的优势,可以从多维传感器数据中自动提取特征并充分挖掘出多维传感器数据与多个故障标签之间的复杂映射关系。经PHM2015 Plant数据集验证表明,该方法在极端类不均衡的工业故障数据中仍可以较好地诊断出工厂生产过程中同时发生的多个故障。

Application of extension method to fault diagnosis of transformer

A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions was employed to determine the nature and the grade of the faults in a transformer system. And the proposed method was verified with the experimental data. The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults can be detected at the same time.

Fourier and wavelet transformations application to fault detection of induction motor with stator current

Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.

Rotor broken bar fault diagnosis for induction motors based on double PQ transformation

A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is pre-sented. By distinguishing the different patterns of the PQ components in the PQ plane,the rotor broken bar fault can be detected. The magnitude of power component directly resulted from rotor fault is used as the fault indicator and the distance between the point of no-load condition and the center of the ellipse as its normalization value. Based on these,the fault severity factor which is completely independent of the inertia and load level is defined. Moreover,a method to reliably discriminate between rotor faults and periodic load fluctuation is presented. Experimental results from a 4 kW induction motor demonstrated the validity of the proposed method.

Geochronology Constraints on Transformation Age from Ductile to Brittle Deformation of the Shangma Fault and Its Tectonic Significance,Dabieshan,Central China

By a detailed investigation of geometry and kinematics of the Shangma （商麻） fault in Dabieshan （大别山）, three different crust levels of extension movement have been recognized in sequence from the deep to the shallow：① low-angle ductile detachment shearing with top to the NW; ② low-angle normal fault with top to the NW or NWW in brittle or brittle-ductile transition domain; ③ high-angle brittle normal fault with top to the W or NWW. Two samples were chosen for zircon U-Pb age dating to constrain the activity age of the Shangma fault. A bedding intrusive granitoid pegmatite vein that is parallel to the foliation of the low-angle ductile detachment shear zone of the country rock exhibits a lotus-joint type of boudinage deformation, showing syn-tectonic emplacing at the end of the ductile deformation period and deformation in the brittle-ductile transition domain. The zircon U-Pb dating of this granitoid pegmatite vein gives an age of （125.9±4.2） Ma, which expresses the extension in the brittle-ductile transition domain of the Shangma fault. The other sample, which is collected from a granite pluton cutting the foliation of the low-angle ductile detachment shear zone, gives a zircon U-Pb age of （118.8±4.1） Ma, constraining the end of the ductile detachment shearing. Then the transformation age from ductile to brittle deformation can be constrained between 126-119 Ma. Combined with the previous researches, the formation of the Luotian （罗田） dome, which is locatedto the east of the Shangma fault, can be constrained during 150-126 Ma. This study gives a new time constraint to the evolution of the Dabie orogenic belt.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

基于视觉Transformer的滚动轴承智能故障诊断

在轴承故障智能诊断中,基于卷积神经网络的轴承故障诊断方法,无法建模信号特征之间的依赖关系。基于循环神经网络的轴承故障诊断方法,对于振动信号只能依次进行顺序计算,限制了模型的并行能力;在计算过程中,间隔时间过长的信息会丢失,无法建立上下文的长期依赖。针对以上问题,提出了基于视觉Transformer的滚动轴承智能故障诊断模型。首次使用视觉Transformer网络代替卷积神经网络和循环神经网络进行轴承故障诊断。利用多头注意力机制来捕获振动信号的全局信息,每个头都应用独立的自注意力机制,使诊断模型可以针对不同的任务在不同的表示子空间里学习相关的信息。实验证明,所提方法能够有效提升模型训练效率,减少信息损失,与其他主流诊断方法相比具有更高的诊断精度。

Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Wavelet transform and its applicationto control system fault detection

Wavelet analysis theory is a new theory developed in recent years, it is a new timefrequency localization method. As its analyzing precision can be changed and focused to anydetail of the analyzed signal., it is very useful to study unstationary signals. In this paper wemainly study the wavelet theory a,of its application in control systems. Furthermore, we use it todetect the fault of an underwater vehicle 's direction angle, and attained excellent results from thesimulation.

基于小波时频图与Swin Transformer的柴油机故障诊断方法

针对用传统的故障诊断方法难以对非线性非平稳的柴油机故障信号进行准确高效诊断的问题,提出基于小波时频图与Swin Transformer的柴油机故障诊断方法。该方法可以有效结合小波时频分析在处理非线性非平稳信号方面的优势和Swin Transformer强大的图像分类能力,通过连续小波变换将原始信号表示为小波时频图,将小波时频图作为特征图输入到Swin Transformer进行训练,实现柴油机故障状态识别。实验结果表明,与对比方法相比,所提方法具有较好的故障识别精度及稳定性,在公开数据集和实验室实测数据中的整体故障诊断准确率分别达到100%和98.88%,为柴油机故障诊断提供了一种新的思路。