Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods,making it challenging to ensure the fault diagnosis accuracy and reliability.A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator(ESGMD-CC)and artificial fish swarm algorithm(AFSA)optimized extreme learning machine(ELM)is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis.Firstly,SGMD decomposes the raw vibration signal into multiple Symplectic geometry components(SGCs).Secondly,the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components.Additionally,the calculus operator is performed to strengthen weak fault features and make them easier to extract,and the singular value decomposition(SVD)weighted by power spectrum entropy(PSE)can be utilized as the sample feature representation.Finally,AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification.The superior performance of the proposed method has been validated by various experiments.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

Model-Based Systems Engineering Approach to Design a Human Settlement to Better Serve Displaced People

The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.

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 using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

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.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis

This article explores the ability of multivariate autoregressive model(MAR)and scalar AR model to extract the features from two-lead electrocardiogram signals in order to classify certain cardiac arrhythmias.The classification performance of four different ECG feature sets based on the model coefficients are shown.The data in the analysis including normal sinus rhythm, atria premature contraction,premature ventricular contraction,ventricular tachycardia,ventricular fibrillation and superventricular tachyeardia is obtained from the MIT-BIH database.The classification is performed using a quadratic diacriminant function.The results show the MAR coefficients produce the best results among the four ECG representations and the MAR modeling is a useful classification and diagnosis tool.

Fault diagnosis method for an Aeroengine Based on Independent Component Analysis and the Discrete Hidden Markov Model

The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.

A Theoretical Model of Arc Light Radiation Based on GTA Spectral Diagnosis

An arc light radiation phenomenon in TIG welding process was studied through experiments and theoretical analysis.The arc spectra were acquired under a variety of welding parameters from 200 nm to 1000 nm wavelength rangein TIG. The influence of welding parameters on the arc radiation was discussed. The radiation energy from linesemission mechanisms was calculated and the comparison was made with the one from the continuum emissionmechanisms. The result shows that the radiation energy from the line emission mechanisms is equal to the one fromthe background. Based on the experiments and analysis, a physical model of arc light radiation has been developed.In this model. the arc plasma was presumed to be in local thermal equilibrium (LTE) with light thinness property.The Bolzmann distribution and Saha equation were applied to establish the arc light radiation model. This helps laythe foundation for further mathematical modeling of the arc light radiation.

Fault Diagnosis Technology Based on Model Driven

Fault diagnosis is an important application of the power grids monitoring system. Under the situation of continuous development of smart grid, it brings new challenges to the fault diagnosis technology. A fault diagnosis expert system based on model driven approach is proposed in this paper. And the corresponding fault modeling technology based on Fault Logic Description Language (FLDL) is described step by step. Practices show that this system could meet the requirements of processing fault alarm information rapidly and reliably by operator.

Forward and backward models for fault diagnosis based on parallel genetic algorithms

In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.