Influence of complicated faults on the differentiation and accumulation of in-situ stress in deep rock mass

High geostress will become a normality in the deep because in-situ stress rises linearly with depth.The geological structure grows immensely intricate as depth increases.Faults,small fractures,and joint fissures are widely developed.The objective of this paper is to identify geostress anomalies at a variety of locations near faults and to demonstrate their accumulation mechanism.Hydrofracturing tests were conducted in seven deep boreholes.We conducted a test at a drilling depth of over one thousand meters to reveal and quantify the influence of faults on in-situ stresses at the hanging wall,footwall,between faults,end of faults,junction of faults,and far-field of faults.The effect of fault sites and characteristics on the direction and magnitude of stresses has been investigated and compared to test boreholes.The accumulation heterogeneity of stresses near faults was illustrated by a three-dimensional numerical simulation,which is utilized to explain the effect of faults on the accumulation and differentiation of in-situ stress.Due to regional tectonics and faulting,the magnitude,direction,and stress regime are all extremely different.The concentration degree of geostress and direction change will vary with the location of faults near faults,but the magnitude and direction of in-situ stress conform to regional tectonic stress at a distance from the faults.The focal mechanism solution has been verified using historical seismic ground motion vectors.The results demonstrate that the degree of stress differentiation varies according to the fault attribute and its position.Changes in stress differentiation and its ratio from strong to weak occur between faults,intersection,footwall,end of faults,and hanging wall;along with the sequence of orientation is the footwall,between faults,the end of faults,intersection,and hanging wall.This work sheds new light on the fault-induced stress accumulation and orientation shift mechanisms across the entire cycle.

Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data

Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively.

Improved Symbiotic Organism Search with Deep Learning for Industrial Fault Diagnosis

Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems.Both structural and non-structural data of industrial systems are collected,which covers data formats of time-series,text,images,sound,etc.Several researchers discussed above were mostly qualitative,and ceratin techniques need expert guidance to conclude on the condition of gearboxes.But,in this study,an improved symbiotic organism search with deep learning enabled fault diagnosis(ISOSDL-FD)model for gearbox fault detection in industrial systems.The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data.In addition,a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation.Moreover,the deep bidirectional recurrent neural network(DBiRNN)is applied for fault detection and classification.At last,the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised.To illustrate the improvised performance of the ISOSDL-FD algorithm,a comprehensive experimental analysis can be performed.The experimental results stated the betterment of the ISOSDLFD algorithm over current techniques.

Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

Wireless Sensor Networks(WSNs)gather data in physical environments,which is some type.These ubiquitous sensors face several challenges responsible for corrupting them(mostly sensor failure and intrusions in external agents).WSNs were disposed to error,and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach.Machine learning(ML)was extremely utilized for detecting faults in WSNs.Therefore,this study proposes a billiards optimization algorithm with modified deep learning for fault detection(BIOMDL-FD)in WSN.The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency.To do so,the presented BIOMDL-FD technique uses the attention-based bidirectional long short-term memory(ABLSTM)method for fault detection.In the ABLSTM model,the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential features.At the same time,the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTM model,which is stimulated by billiard games,showing the novelty of the work.Experimental analyses are made to affirm the enhanced fault detection outcomes of the BIOMDL-FD technique.Detailed simulation results demonstrate the improvement of the BIOMDL-FD technique over other models with a maximum classification accuracy of 99.37%.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

A Novel Deep Model with Meta-Learning for Rolling Bearing Few-Shot Fault Diagnosis

Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement.This leads to the so-called few-shot learning(FSL)problem,which requires the model rapidly generalize to new tasks that containing only a few labeled samples.In this paper,we proposed a new deep model,called deep convolutional meta-learning networks,to address the low performance of generalization under limited data for bearing fault diagnosis.The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data.The proposed method was compared to several FSL methods,including methods with and without pre-training the embedding mapping,and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain.The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset.The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions.In addition,we found that the pretraining process does not always improve the prediction accuracy.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Hydrocarbon accumulation characteristics in basement reservoirs and exploration targets of deep basement reservoirs in onshore China

Based on the global basement reservoir database and the dissection of basement reservoirs in China,the characteristics of hydrocarbon accumulation in basement reservoirs are analyzed,and the favorable conditions for hydrocarbon accumulation in deep basement reservoirs are investigated to highlight the exploration targets.The discovered basement reservoirs worldwide are mainly buried in the Archean and Precambrian granitic and metamorphic formations with depths less than 4500 m,and the relatively large reservoirs have been found in rift,back-arc and foreland basins in tectonic active zones of the Meso-Cenozoic plates.The hydrocarbon accumulation in basement reservoirs exhibits the characteristics in three aspects.First,the porous-fractured reservoirs with low porosity and ultra-low permeability are dominant,where extensive hydrocarbon accumulation occurred during the weathering denudation and later tectonic reworking of the basin basement.High resistance to compaction allows the physical properties of these highly heterogeneous reservoirs to be independent of the buried depth.Second,the hydrocarbons were sourced from the formations outside the basement.The source-reservoir assemblages are divided into contacted source rock-basement and separated source rock-basement patterns.Third,the abnormal high pressure in the source rock and the normal–low pressure in the basement reservoirs cause a large pressure difference between the source rock and the reservoirs,which is conducive to the pumping effect of hydrocarbons in the deep basement.The deep basement prospects are mainly evaluated by the factors such as tectonic activity of basement,source-reservoir combination,development of large deep faults(especially strike-slip faults),and regional seals.The Precambrian crystalline basements at the margin of the intracontinental rifts in cratonic basins,as well as the Paleozoic folded basements and the Meso-Cenozoic fault-block basements adjacent to the hydrocarbon generation depressions,have favorable conditions for hydrocarbon accumulation,and thus they are considered as the main targets for future exploration of deep basement reservoirs.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Step-over of strike-slip faults and overpressure fluid favor occurrence of foreshocks:Insights from the 1975 Haicheng fore-main-aftershock sequence,China

This study analyzed and summarized in detail the spatial and temporal distributions of earthquakes,tidal responses,focal mechanisms,and stress field characteristics for the M 7.3 Haicheng earthquake sequence in February 1975.The foreshocks are related to the main fault and the conjugate faults surrounding the extension step-over in the middle.The initiation timing of the foreshock clusters and the original time of the mainshock were clearly modulated by the Earth's tidal force and coincided with the peak of dilational volumetric tidal strain.As a plausible and testable hypothesis,we proposed a fluid-driven foreshock model,by which all observed seismicity features can be more reasonably interpreted with respect to the results of existing models.Together with some other known examples,the widely existing step-over along strike-slip faults and associated conjugate faults,especially for extensional ones in the presence of deep fluids,favor the occurrence of short-term foreshocks.Although clustered seismicity with characteristics similar to those of the studied case is not a sufficient and necessary condition for large earthquakes to occur under similar tectonic conditions,it undoubtedly has a warning significance for the criticality of the main fault.Subsequent testing would require quantification of true/false positives/negatives.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks （DBN） is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing work- ing status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine （RBM）, and is trained using layer-by- layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierar- chical representations, which are suitable for fault classi- fication, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

A DEEP SEISMIC REFLECTION PROFILE ACROSS ALTUN FAULT BELT

Altun fault is regarded as a large\|scale sinistral strike\|slip fault, it is composed of several faults with the different character, and there is a special geological structure in the fault belt, and they constitute the northwestern margin fault belt of the Qinghai\|Tibetan plateau. In order to investigate the deep crust structure in the Altun region, layers which Tarim lithosphere subducted beneath the Qinghai\|Tibetan plateau, the forward structure of the subduction plate and the scale of the plate subduction, a deep seismic reflection profile was designed. Data collection work of the deep seismic reflection profile across Altun fault was completed during 24/8/1999 to 25/9/1999. The profile locates in Qiemo county, Xinjiang Uygur Autonomous Region, the southern end of the profile stretches into Altun Mountains, the northern end locates in the Tarim desert margin. The profile is nearly SN trending and crosses the main Altun fault. The profile totally is 145km long, time record is 30 seconds, the smallest explosive amount is 72～100kg, the biggest explosive amount reaches 200～300kg, the explosive distance is 800m, and detectors are laid at a 50m distance.

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.

Fault Systems and their Control of Deep Gas Accumulations in Xujiaweizi Area

A study of faults and their control of deep gas accumulations has been made on the basis of dividing fault systems in the Xujiaweizi area. The study indicates two sets of fault systems are developed vertically in the Xujiaweizi area, including a lower fault system and an upper fault system. Formed in the period of the Huoshiling Formation to Yingcheng Formation, the lower fault system consists of five fault systems including Xuxi strike-slip extensional fault system, NE-trending extensional fault system, near-EW-trending regulating fault system, Xuzhong strike-slip fault system and Xudong strike-slip fault system. Formed in the period of Qingshankou Formation to Yaojia Formation, the upper fault system was affected mainly by the boundary conditions of the lower fault system, and thus plenty of muiti-directionally distributed dense fault zones were formed in the T2 reflection horizon. The Xuxi fault controlled the formation and distribution of Shahezi coal-measure source rocks, and Xuzhong and Xndong faults controlled the formation and distribution of volcanic reservoirs of Y1 Member and Y3 Member, respectively. In the forming period of the upper fault system, the Xuzhong fault was of successive strong activities and directly connected gas source rock reservoirs and volcanic reservoirs, so it is a strongly-charged direct gas source fault. The volcanic reservoir development zones of good physical properties that may be found near the Xuzhong fault are the favorable target zones for the next exploration of deep gas accumulations in Xujiaweizi area.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Extensional Tectonic System of Erlian Fault Basin Groupand Its Deep Background

The Erlian fault basin group, a typical Basin and Range type fault basin group, was formed during Late Jurassic to Early Cretaceous, in which there are rich coal, oil and gas resources. In the present paper the abundant geological and petroleum information accumulated in process of industry oil and gas exploration and development of the Erlian basin group is comprehensively analyzed, the structures related to formation of basin are systematically studied, and the complete extensional tectonic system of this basin under conditions of wide rift setting and low extensional ratio is revealed by contrasting study with Basin and Range Province of the western America. Based on the above studies and achievements of the former workers, the deep background of the basin development is treated.