Preliminary report of coseismic surface rupture(part)of Türkiye's M_(W)7.8 earthquake by remote sensing interpretation

Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface ruptures and secondary disasters surrounding the epicentral area is important for post-earthquake emergency and disaster assessments.High-resolution Maxar and GF-2 satellite data were used after the events to extract the location of the rupture surrounding the first epicentral area.The results show that the length of the interpreted surface rupture zone(part of)is approximately 75 km,with a coseismic sinistral dislocation of 2-3 m near the epicenter;however,this reduced to zero at the tip of the southwest section of the East Anatolia Fault Zone.Moreover,dense soil liquefaction pits were triggered along the rupture trace.These events are in the western region of the Eurasian Seismic Belt and result from the subduction and collision of the Arabian and African Plates toward the Eurasian Plate.The western region of the Chinese mainland and its adjacent areas are in the eastern section of the Eurasian Seismic Belt,where seismic activity is controlled by the collision of the Indian and Eurasian Plates.Both China and Türkiye have independent tectonic histories.

Waveguide Bragg Grating for Fault Localization in PON

Femtosecond laser direct inscription is a technique especially useful for prototyping purposes due to its distinctive advantages such as high fabrication accuracy,true 3D processing flexibility,and no need for mold or photomask.In this paper,we demonstrate the design and fabrication of a planar lightwave circuit(PLC)power splitter encoded with waveguide Bragg gratings(WBG)using a femtosecond laser inscription technique for passive optical network(PON)fault localization application.Both the reflected wavelengths and intervals of WBGs can be conveniently tuned.In the experiment,we succeeded in directly inscribing WBGs in 1×4 PLC splitter chips with a wavelength interval of about 4 nm and an adjustable reflectivity of up to 70% in the C-band.The proposed method is suitable for the prototyping of a PLC splitter encoded with WBG for PON fault localization applications.

Research on Remote Fault Detection System of Ceramic Kiln Based on 5G and IoT Technologies

In order to overcome the defects of the existing technology that the detection of ceramic electric kiln faults takes a long time and costs a lot,an electric kiln control and fault detection device was designed.The working process of the device includes detection module,control module,start⁃stop module and switch module.The detection module detects the resistance circuit and sends a fault signal to the control module.The control module generates stop signal and fault information according to the fault signal,and starts the electric kiln when the fault signal is not received within the preset time.The start⁃stop module can monitor the internal temperature of the electric kiln and control the closing status of the switch module.The switch module is used to control the connection status of AC power and each resistance circuit in the kiln.Based on the 5G DTU or 5G module,the control module could send the information to mobile terminal under the ultra⁃reliable and low⁃latency communication(uRLLC)technical characteristics of 5G communication.

Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.

Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

RESEARCH ON THE LOCAL CORRECTION MODEL OF ATMOSPHERIC DRY DELAY IN GPS REMOTE SENSING WATER VAPOR

The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

Response of vertical deformation on faults to remote strong earthquakes

Vertical coseismic deformation on non-causative fault caused by remote strong earthquakes （epicentral distance ≥1 500 km, Ms≥7.0） are observed by fault-monitoring instruments of new type during recent two years. The monitoring result shows, delay time, maximum amplitude and duration of vertical deformation on the non-causative fault have remarkable close relationship with earthquakes magnitude and epicentral distance. The delay time of vertical coseismic deformation have positive linear relationship with epicentral distance. The velocity of coseismic deformation is 5.5 km/s, close to the velocity of surface wave in granite. The logarithms of maximum amplitude of coseismic deformation and epicentral distance have remarkable linear relationship with magnitude. The greater the magnitude and the closer the epicentral distance are, the bigger the maximum amplitude of coseismic deformation on non-causative fault will be. Relative to the epicentral distance, the magnitude is the most important factor to the duration of coseismic vertical deformation on the non-causative fault. Stronger earthquake causes longer vibration duration of coseismic deformation. The experiential equation of co-seismic deformation faults obtained by this work is significant on the coseismic deformation research.

Influencing Factors of the Power Fluctuation on the Ultra High Voltage Transmission Line Caused by Faults at the Remote Ends of the Interconnected Grid

After the North China grid and the Central China grid get into connection with the UHVAC demonstration, a new phenomenon is discovered according to some simulations. That is, the faults at the remote end of the UHV interconnected grid will result in significant power fluctuation and voltage drop on the UHV transmission line and even system splitting. But the faults near the UHV line only have marginal effects. Further, the simulation results also indicate that the short-circuit current of the buses near the UHV line is larger than that of the buses far away from the UHV line. This phenomenon is divergent from the traditional view. In this paper, the detail will be introduced, and the factors influencing the system stability after faults are presented and analyzed. The results indicate that transmission power of the UHV line and of the lines between the remote end and the major grid influence the fluctuation on UHV line. The load model and the grid structure of the remote end also have effect on it. Finally, corresponding control scheme is presented to improve the operation conditions of the UHV interconnected grid and ensure its security and stability.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing images.We model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)algorithm.We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the image.The various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image.In order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’patterns.The 3D-LTP is calculated from spatial RGB planes of the input image.The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too.Finally,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of features.The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial Image Dataset(AID)and PatternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)graph.The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,local binary pattern(LBP),Fisher vector(FV),vector of locally aggregated descriptors(VLAD)and median robust extended local binary pattern(MRELBP).For WHU-RS19 dataset,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{41.93%,20.87%},{92.30%,32.68%},{86.14%,31.97%},{18.18%,15.22%},{8.96%,19.60%}and{15.60%,13.26%},respectively.For AID,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{152.60%,22.06%},{226.65%,25.08%},{185.03%,23.33%},{80.06%,12.16%},{50.58%,10.49%}and{62.34%,3.24%},respectively.For PatternNet,the NSSTds-3DLTP respectively improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{32.79%,10.34%},{141.30%,24.72%},{17.47%,10.34%},{83.20%,19.07%},{21.56%,3.60%},and{19.30%,0.48%}in terms of{MAP,ANMRR}.The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

THE ANALYSIS OF REMOTE SENSING IMAGES FOR ACTIVE FAULTS AND EARTHQUAKES IN CHINA

The Landsat image information has recently been widely applied to structural geology, especially to the analysis of lineaments, owing to their macroscopic, visual and comprehensive features. The images will be more effective when applied to the interpretation of active faults. Active faults are widely ditributed in China. Much attention has been paid to the study of active faults both in China and abroad. There is certain controversy concerning the implication of the term "active fault". Strictly speaking, the term should refer only to the faults that are still active in the present day. However, the term also usually refers to the faults which have been active continually or intermittently from the Quaternary (or the end of Tertiary) to the present day. We propose that the tones and the configurations of features on Landsat images are the principal keys to the interpretation of active faults. The faults, which display the most prominent

Intra-continental deformation and tectonic evolution of the West Junggar Orogenic Belt,Central Asia:Evidence from remote sensing and structural geological analyses

The West Junggar Orogenic Belt(WJOB)in northwestern Xinjiang,China,is located in the core of the western part of the Central Asian Orogenic Belt(CAOB).It has suffered two stage tectonic evolutions in Phanerozoic,before and after the ocean–continental conversion in Late Paleozoic.The later on intracontinental deformation,characterized by the development of the NE-trending West Junggar sinistral strike-slip fault system(WJFS)since Late Carboniferous and Early Permian,and the NW-trending Chingiz-Junggar dextral strike-slip fault(CJF)in Mesozoic and Cenozoic,has an important significance for the tectonic evolution of the WJOB and the CAOB.In this paper,we conduct geometric and kinematic analyses of the WJOB,based on field geological survey and structural interpretation of remote sensing image data.Using some piercing points such as truncated plutons and anticlines,an average magnitude of^73 km for the left-lateral strike-slip is calculated for the Darabut Fault,a major fault of the WJFS.Some partial of the displacement should be accommodated by strike-slip fault-related folds developed during the strike-slip faulting.Circular and curved faults,asymmetrical folds,and irregular contribution of ultramafic bodies,implies potential opposite vertical rotation of the Miao’ergou and the Akebasitao batholiths,resulted from the sinistral strike-slipping along the Darabut Fault.Due to conjugate shearing set of the sinistral WJFS and the dextral CJF since Early Mesozoic,superimposed folds formed with N–S convergence in southwestern part of the WJOB.

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

Automatic software fault localization based on artificial bee colony

Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony （ABC） algorithm is proposed to be integrated with other related techniques. In this process, the source program is initially instrumented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iteralive process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.

Product Maintenance Oriented Remote Monitoring and Diagnosis System

A research on maintenance oriented remote monitoring and diagnosis modular as well as the data transportation technique is carried out. An opened and modularized data share framework integrated with virtual graphic transportation is presented to realize the data exchange. As a result, it implements a real-time monitoring, diagnosis and maintenance system based on WWW. An effective support technique for the real-time remote fault diagnosis, maintenance and entire life cycle design of products is supplied.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Fault Detection Based on Incremental Locally Linear Embedding for Satellite TX-I

A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.