Structural features in the mid-southern section of the Kyushu–Palau Ridge based on satellite altimetry gravity anomaly

The Kyushu–Palau Ridge(KPR),an anti-S-shaped submarine highland at the center of the Philippine Sea Plate(PSP),is considered the residual arc of the Izu–Bonin–Mariana Island Arc,which retains key information about the cessation of the Western Philippine Basin(WPB)expansion and the Parece Vela Basin(PVB)breakup.Herein,using the new generation of satellite altimetry gravity data,high-precision seafloor topography data,and newly acquired ship-borne gravity data,the topographic and gravity characteristics of the KPR mid-southern section and adjacent region are depicted.The distribution characteristics of the faults were delineated using the normalized vertical derivative–total horizontal derivative method(NVDR-THDR)and the minimum curvature potential field separation method.The Moho depth and crustal thickness were inverted using the rapid inversion method for a double-interface model with depth constraints.Based on these results,the crust structure features in the KPR mid-southern section,and the“triangular”structure geological significance where the KPR and Central Basin Rift(CBR)of the WPB intersect are interpreted.The KPR crustal thickness is approximately 6–16 km,with a distinct discontinuity that is slightly thicker than the normal oceanic crust.The KPR mid-southern section crust structure was divided into four segments(S1–S4)from north to south,formed by the CBR eastward extension joint action and clockwise rotation of the PVB expansion axis and the Mindanao fault zone blocking effect.

Bearing Fault Diagnosis with DDCNN Based on Intelligent Feature Fusion Strategy in Strong Noise

Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology.However,conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data.Therefore,this paper proposes a dual-channel convolutional neural network(DDCNN)model that leverages dual data inputs.The DDCNN model introduces two key improvements.Firstly,one of the channels substitutes its convolution with a larger kernel,simplifying the structure while addressing the lack of global information and shallow features.Secondly,the feature layer combines data from different sensors based on their primary and secondary importance,extracting details through small kernel convolution for primary data and obtaining global information through large kernel convolution for secondary data.Extensive experiments conducted on two-bearing fault datasets demonstrate the superiority of the two-channel convolution model,exhibiting high accuracy and robustness even in strong noise environments.Notably,it achieved an impressive 98.84%accuracy at a Signal to Noise Ratio(SNR)of−4 dB,outperforming other advanced convolutional models.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory（EA） model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.

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.

Development features of volcanic rocks of the Yingcheng Formation and their relationship with fault structure in the Xujiaweizi Fault Depression,Songliao Basin,China

The Xujiaweizi Fault Depression is located in the northern Songliao Basin,Northeast China.The exploration results show that the most favorable natural gas reservoirs are in the volcanic rocks of the Yingcheng Formation（K 1 yc）.Based on seismic interpretation,drill cores and the results of previous research,we analyzed the distribution of faults and the thickness of volcanic rocks in different periods of K 1 yc,and studied the relationship of volcanic activities and main faults.Volcanic rocks were formed in the Yingcheng period when the magma erupted along pre-existing fault zones.The volcanic activities strongly eroded the faults during the eruption process,which resulted in the structural traces in the seismic section being diffuse and unclear.The tectonic activities weakened in the study area in the depression stage.The analysis of seismic interpretation,thin section microscopy and drill cores revealed that a large number of fractures generated in the volcanic rocks were affected by later continued weak tectonic activities,which greatly improved the physical properties of volcanic reservoirs,and made the volcanic rocks of K 1 yc be favorable natural gas reservoirs.The above conclusions provide the basis to better understand the relationship of the volcanic rock distribution and faults,the mechanism of volcanic eruption and the formation of natural gas reservoirs in volcanic rocks.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring.Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction.The effectiveness of the proposed method is verified through simulation signal and experiment data.

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

Sentinel-1 In SAR observations and time-series analysis of co-and postseismic deformation mechanisms of the 2021 Mw 5.8 Bandar Ganaveh Earthquake,Southern Iran

In the past two decades,because of the significant increase in the availability of differential interferometry from synthetic aperture radar and GPS data,spaceborne geodesy has been widely employed to determine the co-seismic displacement field of earthquakes.On April 18,2021,a moderate earthquake(Mw 5.8)occurred east of Bandar Ganaveh,southern Iran,followed by intensive seismic activity and aftershocks of various magnitudes.We use two-pass D-InSAR and Small Baseline Inversion techniques via the LiCSBAS suite to study the coseismic displacement and monitor the four-month post-seismic deformation of the Bandar Ganaveh earthquake,as well as constrain the fault geometry of the co-seismic faulting mechanism during the seismic sequence.Analyses show that the co-and postseismic deformation are distributed in relatively shallow depths along with an NW-SE striking and NE dipping complex reverse/thrust fault branches of the Zagros Mountain Front Fault,complying with the main trend of the Zagros structures.The average cumulative displacements were obtained from-137.5 to+113.3 mm/yr in the SW and NE blocks of the Mountain Front Fault,respectively.The received maximum uplift amount is approximately consistent with the overall orogen-normal shortening component of the Arabian-Eurasian convergence in the Zagros region.No surface ruptures were associated with the seismic source;therefore,we propose a shallow blind thrust/reverse fault(depth~10 km)connected to the deeper basal decollement fault within a complex tectonic zone,emphasizing the thin-skinned tectonics.

Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch

Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network(ANN) classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore,according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current.Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied.Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results.

Feature Extraction Technique for Fault Prognosis Based on Fault Trend Analysis

Fault prognosis is one of the key techniques for prognosis and health management,and an effective fault feature can improve prediction accuracy and performance. A novel approach of feature extraction for fault prognosis based on fault trend analysis was proposed in this paper. In order to describe the ability of tracking fault growth process,definitions and calculations of fault trackability was developed, and the feature which had the maximum fault trackability was selected for fault prognosis. The vibration data in bearing life tests were used to verify the effectiveness of the method was proposed. The results showed that the trackability of energy entropy for bearing fault growth was the maximum,and it was the best fault feature among selected features root mean square( RMS),kurtosis,new moment and energy entropy. The proposed approach can provide a better strategy for fault feature extraction of bearings in order to improve prediction accuracy.

Geophysical Features of the Ore-Controlling Fault in the Chang'an Gold Deposit, Southern Yunnan Province

The Ailao Mountain is one of the most important metallogenic belts ofpolymetallic deposits in the Sanjiang region, southwestern China. Located in the southern segment of this metallogenic belt, the newly-discovered Chang＇an gold deposit is large in scale （Fig. 1A）, and has attracted much attention among geologists. The ore-hosted rocks in the district include the Late Ordovician Xiangyang Fm. sandstone and clastic rocks and the Early Silurian Kanglang Fm. dolomite. Affected by the multistage tectonic activities, stocks and dykes of lamprophyre, dolerite, syenite porphyry and orthoclasite are widely exposed, and the orebodies are in symbiosis with or crosscut the dyke rocks.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

Deformation features and tectonic transfer of the Gumubiezi Fault in the northwestern margin of Tarim Basin, NW China

Through field geologic survey,fine interpretation of seismic reflection data and analysis of well drilling data,the differential deformation,tectonic transfer and controlling factors of the differential deformation of the Gumubiezi Fault(GF)from east to west have been studied systematically.The study shows that GF started to move southward as a compressive decollement along the Miocene gypsum-bearing mudstone layer in the Jidike Formation at the Early Quaternary and thrust out of the ground surface at the northern margin of the Wensu Uplift,and the Gumubiezi anticline formed on the hanging wall of the GF.The displacement of the GF decreases gradually from 1.21 km in the east AA′transect to 0.39 km in the west CC′transect,and completely disappears in the west of the Gumubiezi anticline.One part of the displacement of the GF is converted into the forward thrust,and another part is absorbed by Gumubiezi anticline.The formation of the GF is related to the gypsum-bearing mudstone layer in the Jidike Formation and barrier of the Wensu Uplift.The differential deformation of the GF from east to west is controlled by the development difference of gypsum-bearing mudstone layer in the Jidike Formation.In the east part,gypsum-bearing mudstone layer in the Jidike Formation is thicker,the deformation of the duplex structure in the north of the profile transferred to the basin along gypsum-bearing mudstone layer;to the west of the Gumubiezi structural belt(GSB),the gypsum-bearing mudstone layer in Jidike Formation decreases in thickness,and the transfer quantity of deformation of the duplex structure along the gypsum-bearing mudstone layer to the basin gradually reduces.In contrast,on the west DD′profile,the gypsum-bearing mudstone is not developed,the deformation of the deep duplex structure cannot be transferred along the Jidike Formation into the basin,the deep thrust fault broke to the surface and the GF disappeared completely.The displacement of the GF to the west eventually disappeared,because the lateral ramp acts as the transitional fault between east and west part of GSB.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

Geomorphological responses of rivers to active tectonics along the Karahay?t Fault, Western Türkiye

Understanding the topography in active tectonic areas and assessing the rates and models of active deformation in the upper crust are primary objectives in tectonic geomorphology studies. The drainage pattern of river systems is highly sensitive to tectonically induced changes, and it often preserves the records of the formation and progression of most tectono-geomorphic processes within its boundaries. Therefore, the evolution of landforms is a consequence of the evolution of individual drainage basins in which they are formed. Assessing the rates of tectonic deformation using geomorphic data is a traditionally adopted method to characterize the nature of active faults. Globally, the Digital Elevation Model(DEM) is widely used as a crucial tool to analyze the morphotectonic features of drainage basins. In this study, some geomorphic indices were applied to investigate the impact of tectonism on landscape along the Karahay?t Fault and its associated drainage areas. These geomorphic indices are mountain front sinuosity(Smf values between 1.17-1.52), valley floor width-to-height ratio(Vf values between 0.25-1.46), basin asymmetry factor(AF values between 15-72), drainage basin shape(Bs values between 3.18-6.01), hypsometric integral and curve(HI values between 0.32-047), channel sinuosity(S values between 1-1.6), normalized steepness index(Ksn values between 1-390) and Chi integral(χ values between 200-4400). The development of drainage areas on the hanging wall and footwall block of the Karahayit Fault differs depending on the uplift. The drainage areas developed on the hanging wall present different patterns depending on the regional uplift caused by the fault. This reveals that the fault contributed significantly to the development of drainage areas and regional uplift in the region. In addition, the maximum earthquake magnitude that may occur in the future on the Karahayit Fault, whose activity is supported by geomorphic indices, is calculated as 6.23. Since an earthquake of this magnitude may cause loss of life and property in the region, precautions should be taken.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.