Software for slip-tendency analysis in 3D: A plug-in for Coulomb

Slip-tendency analysis is a valuable tool in fault reactivation evaluation and seismic hazard assessment as it provides a means of quantifying the slip potential on mapped or suspected faults in a known or inferred stress field. We developed an interactive graphic tool to perform slip-tendency analysis. The application is written in MATLAB in the form of plug-ins for COULOMB, a graphic-rich deformation and stress change open-source software. In addition to identifying the faults most prone to reactivation, we compute and plot synthetic focal mechanisms from the direction and sense of likely slip. This allows compatibility between focal mechanisms and geological structures to be verified. Both individual faults and fault networks can be considered in three dimensions. The potential for slip depends on the prevailing stress field, the fault surface orientation and the coefficient of friction. These parameters are given interactively in a Windows environment. The application thus offers an easy-to-use graphical interface with the possibility of fast parameter modification and 3D visualization of the results.     

Remote sensing data analysis for mapping active faults in the northwestern part of Kangra Valley,NW Himalaya,India

Aerospace data show morphological features indicative of two prominent active faults to the northwest of the Kangra valley, the meizoseismal zone of the 1905 Kangra earthquake (M 7.8). Topographical features indicative of long‐term uplift/deformation during the Holocene and cumulative slip along these faults reflect the manifestation of normal faulting. The south‐side‐up behaviour of these two faults is in contrast to the general north‐side‐up movement along the Main Boundary Thrust (MBT). The newly identified faults suggest that the fault scarps have developed because of large‐magnitude earthquakes in the past, preceding the known 1905 Kangra earthquake. The study also demonstrates that valuable geomorphological and structural inferences associated with active tectonics are possible using high‐resolution satellite data for the tectonically complex terrain of the Himalaya.     

Formal Analysis of Coupling Hypothesis for Logical Faults

Fault-based testing focuses on detecting faults in a software. Test data is typically generated considering the presence of a single fault at a time, under the assumption of coupling hypothesis. Fault-based testing approach, in particular mutation testing, assumes that the coupling hypothesis holds. According to the hypothesis, a test set that can detect presence of single faults in an implementation, is also likely to detect presence of multiple faults. In this paper it is formally shown that the hypothesis is guaranteed to hold for a large number of logical fault classes.     

基于优选几何分组增强的假设检验RAIM方法

针对传统接收机自主完好性监测(RAIM)算法对15~50m级较小故障检测和排除率低,不易满足多测距故障场景应用问题,建立4种典型卫星分布几何结构模型,通过研究各模型对含偏差测距带来的定位误差与检测统计值间耦合性的影响,提出了一种基于几何优选分组增强的RAIM方法。该方法在假设检验方法基础上,利用分组间残差矢量建立新型检测统计量与门限值,通过检验子集一致性来检测和排除含有偏差的测距。仿真表明:该方法能够提升较小故障检测和排除率,增强接收机自主完好性性能水平,且支持联合导航系统单、多测距故障场景假设,有效降低定位估计误差,保证了定位解的精度。     

基于Worldview\|Ⅱ遥感影像的西藏改则地区断裂构造解译研究及应用

提出一种Worldview-Ⅱ遥感影像数据图像处理的方法,并利用高分辨率Worldview-Ⅱ遥感影像数据进行断裂构造解译。结合ETM+遥感影像和已有构造模型,辅助地形地貌特征分析,首次对高海拔、地面工作部署困难的西藏改则附近区域进行了大范围、精细的构造解译工作。结果表明,Worldview-Ⅱ遥感影像数据在构造解译方面具有分辨率高、准确有效的特点。根据解译结果得知,研究区在南北向最大主应力(S1)与东西向最小主应力(S3)的应力模式下,东西向逆冲断层、南北向正断层及北东或北西向走滑断层组合出现,并共同实现研究区内的南北向缩短和东西向伸展。区内的地形地貌总体上都受到构造控制,最为显著的表现包括:①热拉错发育在由南北向正断层所产生的地堑构造之上;②吓嘎错为北西走向右行走滑断裂尾端走向偏转所产生的拉分盆地;③改则洪积扇平原的发育受北东走向张扭性左行走滑断层控制。     

The Al Hoceima (Morocco) earthquake of 24 February 2004, analysis and interpretation of data from ENVISAT ASAR and SPOT5 validated by ground-based observations

The magnitude M_w = 6.3 earthquake in Al Hoceima, Morocco of 24 February, 2004 occurred in the active plate boundary accommodating the oblique convergence between Africa and Eurasia. Three different sets of estimates of its source parameters have already been published. We try to resolve the discrepancies between them by using additional data including two remote sensing satellite systems (ENVISAT and SPOT5). Using a model with a dislocation in an elastic half-space, we constrain the source parameters. The hypothesis of two subevents on distinct faults as inferred from seismological inversions is confirmed here by adopting a cross-fault mechanism. The rupture began on a left-lateral strike-slip fault striking at N10° azimuth with 90 cm of horizontal slip and then transferred to a right-lateral strike-slip fault striking at N312° azimuth with 85 cm of horizontal slip. The first fault is at 500 m depth from the free surface and the second fault is at 3 km depth. This model is consistent with ground-based observations, including GPS, seismology, and mapped surface fissures. The pair of faults activated in 2004 appears to constitute part of a complex seismogenic structure striking NNE-SSW that separates the Rif tectonic blocks.     

Improved resolution of the multiple inverse method by eliminating erroneous solutions

The multiple inverse method is a numerical technique designed to separate stresses from heterogeneous fault–slip data. The method is one of the resampling methods based on the pattern recognition. Plotting solutions determined from k-fault subset into the parameter space, we have clusters representing significant stresses for the dataset. This paper presents a technique to improve the resolution of stress for the method. The regularity/singularity test of the subsets taken from the fault–slip data is the key for this purpose. The resolution and accuracy of the method are improved by eliminating erroneous stresses or artifacts that were yielded by the method. The performances of the method are demonstrated with a variety of artificial datasets.     

Finding fault - fault diagnosis on the wheels of a mobile robot using local model neural networks

As mobile robots are mostly designed to act autonomously, procedures that detect and isolate faults on the various parts of a robot are essential. The most powerful approaches in fault detection and isolation (FDI) are those using a process model, where quantitative and qualitative knowledge-based models, databased models, or combinations thereof are applied. This article suggests a model-free approach to the solution of the fault detection problem. One way to deal with the absence of a mathematical model is to build a model from input-output data. In this article, local model networks (LMNs) are used for plant modeling. The key to fault detection and diagnosis is the creation of residual signals. Although the way these signals are formed varies, in all cases the residuals change their value accordingly with the presence of faults. To avoid false alarms, the residuals must be affected by factors unrelated to faults (like modeling errors) as little as possible. Change-detection algorithms are therefore used for reliable residual generation. These algorithms are designed to detect changes in signals that include noise or other types of disorders. The combination of local model networks for modeling and change-detection algorithms for residual creation provides an efficient method for fault detection and diagnosis. The method is applied on the wheels subsystem of a mobile robot.     

基于改进Apriori算法的地面空调间歇故障预测

针对机坪地面空调间歇故障引起的使用效能低、维修滞后等问题,提出了二次关联累加数组（AS）-Apriori与聚类K-means相结合的间歇故障预测方法,并基于此实现了延误维修预测。其中：AS-Apriori算法解决了Apriori频繁扫描事务库的低效问题,通过实时构造间歇故障数组并对其对应项累加求和;延误维修预测是为了估计出永久故障临界区以安排合理维修,可采用正态分布求出不同间歇故障变量的维修波及延误概率并进行依次累加而实现。验证表明,AS-Apriori提高了运行效率,且二次关联规则支持度提升了20.656个百分点,能更准确预测间歇故障,同时参照数据分析,预测的维修波及延误累加概率呈线性分布,即可预测性高的间歇故障更便于预先维护管理,减少永久故障的形成。     

Fault detection in autonomous robots based on fault injection and learning

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

轻量级分组密码SIMON代数故障攻击

针对SIMON现有故障攻击中存在的故障深度小、手工推导复杂等问题,给出一种代数故障攻击（AFA）方法。首先给出SIMON核心运算‘&’代数表示方法并构建全轮正确加密代数方程组;其次注入故障并将故障信息表示为代数方程,提供故障已知和故障未知两种模型,给出两种模型故障表示方法;最后利用CryptoMinisat-2.9.6解析器求解方程组恢复密钥。实验结果表明：利用单比特故障对SIMON32/64进行攻击,故障位置选取第26轮,故障已知和未知模型仅需5个和6个故障即可恢复全轮密钥;利用n比特宽度故障对SIMON128/128进行攻击,故障位置选取第65轮,两种模型均只需2个故障即可恢复全轮密钥。此外,对比故障已知和未知模型发现,随故障数递增密钥求解时间的决定因素将由故障信息量变为方程组计算量。     

A hybrid fault diagnosis approach using neural networks

A hybrid fault diagnosis method is proposed in this paper which is based on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used to maximise the signal- to- noise ratio of the residual and to isolate different faults. Effectiveness of the method is demonstrated by applying it to fault detection and isolation for a hydraulic test rig. Real data simulation shows that the sensitivity of the residual to the faults is maximised, whilst that to the unknown input is minimised. The simulated faults are successfully isolated by a bank of neural nets.     

Coseismic deformation and source model of the 12 November 2017 MW 7.3 Kermanshah Earthquake (Iran–Iraq border) investigated through DInSAR measurements

A large earthquake with a magnitude of M_W 7.3 struck the border of Iran and Iraq at the province of Kermanshah, Iran. In our study, coseismic deformation and source model of the 12 November 2017 Kermanshah Earthquake are investigated using ALOS-2 ScanSAR and Sentinel-1A/B TOPSAR Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques. Geodetic inversion has been performed to constrain source parameters and invert slip distribution on the fault plane. The optimised source model from joint inversion shows a blind reverse fault with a relatively large right-lateral component, striking 353.5° NNW-SSE and dipping 16.3° NE. The maximum slip is up to 3.8 m at 12–14 km depth and the inferred seismic moment is 1.01 × 10²⁰ Nm, corresponding to M_W 7.3, consistent with seismological solutions. The high-resolution optical images from SuperView-1 satellite suggest that most of the linear surface features mapped by DInSAR measurements are landslides or surface cracks triggered by the earthquake. Coulomb stress changes on the source fault indicating consistency between aftershock distribution and high loaded stress zones. Based on the stress change on neighbouring active faults around this area, the Kermanshah Earthquake has brought two segments of the Zagros Mountain Front Fault (MFF), MFF-1 and MFF-2, 0.5–3.1 MPa and 0.5–1.96 MPa closer to failure, respectively, suggesting the risk of future earthquakes. Recent major aftershocks (M_W≥ 5.0) could probably ease the seismic hazard on MFF-2, but the risk of earthquakes on MFF-2 is still increasing.     

The links between human error diversity and software diversity: Implications for fault diversity seeking

Software diversity is known to improve fault tolerance in N-version software systems by independent development. As the leading cause of software faults, human error is considered an important factor in diversity seeking. However, there is little scientific research focusing on how to seek software fault diversity based on human error mechanisms. A literature review was conducted to extract factors that may differentiate people with respect to human error-proneness. In addition, we constructed a conceptual model of the links between human error diversity and software diversity. An experiment was designed to validate the hypotheses, in the form of a programming contest, accompanied by a survey of cognitive styles and personality traits. One hundred ninety-two programs were submitted for the identical problem, and 70 surveys were collected. Code inspection revealed 23 faults, of which 10 were coincident faults. The results show that personality traits seems not effective predictors for fault diversity as a whole model, whereas cognitive styles and program measurements moderately account for the variation of fault density. The results also show causal relations between performance levels and coincident faults: coincident faults are unlikely to occur at skill-based performance level; the coincident faults introduced in rule-based performances show a high probability of occurrence, and the coincident faults introduced in knowledge-based performances are shaped by the content and formats of the task itself. Based on these results, we have proposed a model to seek software diversity and prevent coincident faults.     

Fault detection,isolation and identification for hybrid systems with unknown mode changes and fault patterns

This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method.     

Adaptive compensation for infinite number of actuator failures or faults

It is both theoretically and practically important to investigate the problem of accommodating infinite number of actuator failures or faults in controlling uncertain systems. However, there is still no result available in developing adaptive controllers to address this problem. In this paper, a new adaptive failure/fault compensation control scheme is proposed for parametric strict feedback nonlinear systems. The techniques of nonlinear damping and parameter projection are employed in the design of controllers and parameter estimators, respectively. It is proved that the boundedness of all closed-loop signals can still be ensured in the case with infinite number of failures or faults, provided that the time interval between two successive changes of failure/fault pattern is bounded below by an arbitrary positive number. The performance of the tracking error in the mean square sense with respect to the frequency of failure/fault pattern changes is also established. Moreover, asymptotic tracking can be achieved when the total number of failures and faults is finite.     

Construction of coherent 3D geological blocks

This paper presents a new approach and efficient methods to build directly coherent geological models honoring the most reliable data set and the assumed local deformation mode. A first set of methods, based on the simple shear model, allows one to build geological structures such as similar folds, rollovers and listric faults. A second set of methods, based on the flexural slip deformation mode, enables one to build structures such as concentric folds and ramp anticlines.The principle of the proposed methods is to construct new horizons and faults coherent with the better known ones. Thanks to these methods, it is straightforward to build quickly coherent 3D blocks based on the most reliable data (well, 2D or 3D seismic image, surface data). Horizons poorly imaged on seismic data can be completed simply. Eroded parts of the upper horizons can be easily extrapolated as well. Finally this provides a useful methodology to check the coherency between the fault and the horizon geometry.     

基于欧姆龙PLC设备的故障诊断解析方法

本文针对PLC控制的电动机在正反转时发生故障,通过观察故障现象和分析其故障原因,提出设定故障检查次序,综合利用假设验证法、替换法、对比法和测量法等故障诊断方法,排除设备的故障,通过实践证明合理设定故障检查次序对设备故障排除的重要性.     

Subspace identification of continuous time models for process fault detection and isolation

This paper proposes a novel subspace approach towards identification of optimal residual models for process fault detection and isolation (PFDI) in a multivariate continuous-time system. We formulate the problem in terms of the state space model of the continuous-time system. The motivation for such a formulation is that the fault gain matrix, which links the process faults to the state variables of the system under consideration, is always available no matter how the faults vary with time. However, in the discrete-time state space model, the fault gain matrix is only available when the faults follow some known function of time within each sampling interval. To isolate faults, the fault gain matrix is essential. We develop subspace algorithms in the continuous-time domain to directly identify the residual models from sampled noisy data without separate identification of the system matrices. Furthermore, the proposed approach can also be extended towards the identification of the system matrices if they are needed. The newly proposed approach is applied to a simulated four-tank system, where a small leak from any tank is successfully detected and isolated. To make a comparison, we also apply the discrete time residual models to the tank system for detection and isolation of leaks. It is demonstrated that the continuous-time PFDI approach is practical and has better performance than the discrete-time PFDI approach.     

基于神经网络的局域网故障诊断系统设计

为了实现准确有效地排除网络故障，根据局域网的特点，本文在分析现有计算机网络故障诊断方法不足的基础上，结合层次分类神经网络原理，提出了基于层次分类BP神经网络算法的局域网智能故障诊断系统，该系统具有层次分类策略、故障诊断策略、通信数据包采集、网络故障知识数据库、故障显示报警等功能，可以实现已知、未知故障的正确识别。通过对故障诊断算法和故障定位的仿真，结果表明故障诊断系统响应性能良好，故障误报率较低，具有一定的实用性。