A fault recognition method based on clustering linear regression

Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is proposed.Firstly,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube algorithm.Secondly,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between them.Finally,a fault is generated from each category of key points based on least squares curve fitting method to realize fault identification.To verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.

Research on fault recognition method combining 3D Res-UNet and knowledge distillation

Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are usually not able to accurately identify complex faults.In this study,using the advantage of deep residual networks to capture strong learning features,we introduce residual blocks to replace all convolutional layers of the three-dimensional(3D)UNet to build a new 3D Res-UNet and select appropriate parameters through experiments to train a large amount of synthesized seismic data.After the training is completed,we introduce the mechanism of knowledge distillation.First,we treat the 3D Res-UNet as a teacher network and then train the 3D Res-UNet as a student network;in this process,the teacher network is in evaluation mode.Finally,we calculate the mixed loss function by combining the teacher model and student network to learn more fault information,improve the performance of the network,and optimize the fault recognition eff ect.The quantitative evaluation result of the synthetic model test proves that the 3D Res-UNet can considerably improve the accuracy of fault recognition from 0.956 to 0.993 after knowledge distillation,and the eff ectiveness and feasibility of our method can be verifi ed based on the application of actual seismic data.

Effects of Spark Energy on Spark Plug Fault Recognition in a Spark Ignition Engine

The increasing demands for fuel economy and emission reduction have led to the development of lean/diluted combustion strategies for modern Spark Ignition(SI)engines.The new generation of SI engines requires higher spark energy and a longer discharge duration to improve efficiency and reduce the backpressure.However,the increased spark energy gives negative impacts on the ignition system which results in deterioration of the spark plug.Therefore,a numerical model was used to estimate the spark energy of the ignition system based on the breakdown voltage.The trend of spark energy is then recognized by implementing the classification method.Significant features were identified from the Information Gain(IG)scoring of the statistical analysis.k-Nearest Neighbor(KNN),Artificial Neural Network(ANN),and SupportVector Machine(SVM)models were studied to identify the best classifier for the classification stage.For all classifiers,the entire featured dataset was randomly divided into standardized parameter values of training and testing data sets with the ratio of 70-30 for each class.It was shown in the study that the KNN classifier acquired the highest Classification Accuracy(CA)of 94.1%compared to ANN and SVM that score 77.3%and 87.9%on the test data,respectively.

Fault Tolerant Optical Mark Recognition

OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.

Fault Pattern Recognition Based on Hidden Markov Model

Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.

New Recognition about Faulted Structures in Tarim Basin

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Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network

The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment,and the identification of faults is a prerequisite.Therefore,the fault identification of internal combustion engines is one of the important directions of current research.In order to further improve the accuracy of the fault recognition of internal combustion engines,this paper takes a certain type of internal combustion engine as the research object,and constructs a support vector machine and a fuzzy neural network fault recognition model.The binary tree multi⁃class classification algorithm is used to determine the priority,and then the fuzzy neural network is verified.The feasibility of the model is proved through experiments,which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency.

An early warning method for water inrush in Dongjiahe coal mine based on microseismic moment tensor

Dongjiahe Coal Mine belongs to the Carboniferous Permian coal field which has a high degree of karst and fissure development.This paper takes the working face of Dongjiahe Coal Mine as an example;through the microseismic(MS)monitoring system arranged on the working face,the moment tensor theory was used to invert the focal mechanism solution of the anomalous area of the floor MS event;combining the numerical simulation and field data,the underlying floor faults were identified by the stress inversion method.The results show that:1)Moment tensors were decomposed into three components and the main type of rupture in this area is mixed failure according to the relative criterion;2)The hidden fault belongs to the reversed fault,its dip angle is approximately 70°,and the rupture length is 21 m determined by the inversion method of the initial dynamic polarity and stress in the focal mechanism;3)The failure process of the fault is divided into three stages by numerical simulation method combined with the temporal and spatial distribution of MS events.The results can provide a reference for early warning and evaluation of similar coal mine water inrush risks.

Research on Mineral Resource Prediction by GA-ANN

Fault recognition and coal seam thickness forecast are important problems in mineral resource prediction. Knowledge of multiple disciplines, which include mining engineering, mine geology, seismic prospecting etc, was used synthetically. Artificial neural network was combined with genetic algorithm to found integrated AI method of genetic algorithm artificial neural network(GA ANN). Fault recognition and coal seam thickness forecast were carried to completion by case studies. And the research results are satisfactory.

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis field.However,existing studies suffer from two weaknesses.First,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types.Second,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable.This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition results.Second,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore determined.The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump.Results show the proposed method’s validity in diagnosing fault types and sources.

An Algorithm for the Protection of Distribution Feeders Using the Stockwell and Hilbert Transforms Supported Features

Faults’recognition in the distribution feeders(DFs)is extremely important for improving the reliability of the distribution system.Therefore,this paper proposes a technique to identify the faults on the DF using the Stockwell Transform(ST)dependent variance feature and Hilbert transform(HT)by utilizing current signals.By element to element multiplication of the H-index,we compute using HT aided decompositions of current waveforms and VS-index,and calculate through ST aided decomposition of current waveforms.By utilizing the decision rules,various faults are classified.Different faults studied in this work are line to ground,double line,double line to ground and 3-Φto ground.For high fault impedance,this technique is effectively utilized.Furthermore,variations in the fault incidence angles are also utilized to test the performance of the proposed technique.To perform the proposed algorithm,a IEEE-13 bus system is developed in MATLAB/Simulink software.The algorithm effectively classified the faults with accuracy greater than 98%.The algorithm is also successfully validated on the IEEE-34 bus test system.Furthermore,the algorithm was successfully validated on the practical power system network.It is recognized that the developed method performed better than the discrete Wavelet transform(DWT)and ruled decision tree based protection scheme reported in various literature.