Energy evolution and structural health monitoring of coal under different failure modes:An experimental study

Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.

Application Research of “3+1” Mode for Birth Defects Monitoring

Objective To explore the ＂3＋1＂ monitoring mode for birth defects and quality control measures based on the population, and to obtain the related information data for birth defects.Methods With the community population as the basis, adopting the unified monitoring scheme dominant by the leadership and administration of government, with districts （counties） as the monitoring sites, the ＂3＋1 ＂ monitoring mode for birth defects was based on a complete monitoring team with the combination of villages/residents＇ committees, townships （towns）, counties （districts） and the municipality. Demonstration research was carried out in the pilot districts/counties in Chongqing City.Results Birth defects population monitoring system based on population and family planning management and service network was established, and during 2005 and 2006.application research was carried out for the monitoring methods among birth defects population in the pilot districts （counties）, obtaining the relevant information in regional birth defects, with a monitoring coverage of over 99%. Conclusion Fully utilizing the birth management functions of Population and Famlty Planning System and the advantages of service networks, long term, dynamic birth defects monitoring system based on community population was established, with the integration of birth defects monitoring and regular reproductive health services, obtaining overall birth defects occurrence information in details, providing scientific basis for the government to formulate scientific, practical, economic and effective birth defects intervention policy, so as to improve the quality of the population.

Finite Element Model Updating for Structural Health Monitoring

This paper provides a model updating approach to detect,locate,and char-acterize damage in structural and mechanical systems by examining changes in mea-sured vibration responses.Research in vibration-based damage identification has been rapidly expanding over the last few decades.The basic idea behind this technology is that modal parameters(notably frequencies,mode shapes,and modal damping)are functions of the physical properties of the structure(mass,damping,and sifies).Therefore,changes in the physical properties will cause changes in the modal proper-ties which could be obtained by structural health monitoring(SHM).Updating is a process fraught with numerical difficulties.These arise from inaccuracy in the model and imprecision and lack of information in the measurements,mainly taken place in joints and critical points.The motivation for the development of this technology is.presented,methods are categorized according to various criteria such as the level of damage detection provided from vibration testing,natural frequency and mode shape readings are then obtained by using modal analysis techniques,which are used for updating structural parameters of the associated finite element model The experi-mental studies for the laboratory tested bridge model show that the proposed model.updating using ME scope technique can provide reasonable model updating results.

Research on the Construction of English Autonomous Learning Monitoring Mode under the Background of Big Data

The era of big data is coming,the combination of big data and traditional teaching can provide more and more accurate services for students'self-learning,and it is a good way to teach students according to their aptitude.In this background,a learning society is coming,which aiming at learning,autonomous learning and lifelong learning.Learning society emphasize the ability of learning autonomy for students unprecedentedly.Learning is no longer limited to the campus.Learning ability will accompany learners'social life and become an active and healthy lifelong activity.Autonomous learning is a learning theory that goes with the requirements of The Times and has a broad development prospect.The study of Autonomous learning not only has a very important guiding significance for the educational and teaching practice in China,but also plays an important role in the life development of every student.The subject of learning is gradually transferred from the classroom,teachers and textbooks to the students themselves.Teachers should not only impart knowledge and answer questions,but also,most importantly,teach students how to exert their autonomy in autonomous learning.After investigating and researching the existing monitoring model of autonomous English learning in colleges and universities,our group found that in practice,there is a lack of corresponding monitoring mechanisms and means,and autonomous learning has gradually become formalized.Therefore,according to the actual situation of autonomous English learning in our country's universities,the monitoring model of autonomous English learning has been reconstructed,and an effective comprehensive evaluation system has been established to effectively improve students'English learning ability.

Real-time State Monitoring During Switching Mode Transitions in High Power Three-level Inverters

大功率多电平逆变器近年来在实际工业生产中得到越来越广泛的应用。多电平逆变器由于结构复杂,采用元器件较多,因此在设计和实验中,实现各个工作状态下运行参数的同步监测和分析较为困难。本文针对大功率三电平逆变器,实现开关动态特性的在线测试,在此基础上,进一步研究三电平逆变器在开关状态变化时理论与实际负载运行工况下电路拓扑的转换变化规律。通过全电路电气参数和元器件状态的实时监测,发现在三电平逆变器非正常运行状态下开关转换时额外电应力,同时,深入研究在实际工况运行条件非正常状态下该额外电应力出现的机理和原因,为三电平逆变器的故障诊断提供了参考,对于设计高可靠性的多电平逆变器系统有一定的理论和现实意义。

Study on Health Monitoring Systems Based on Correction Mode

Current health monitoring systems often do not concern about the needs of the elderly,leading to inaccurate health status monitoring and delayed treatment for emergency health conditions.Similarly,they do not consider the variable factors affecting each patient,resulting in discrepancies between the measured values and real health status.To solve the problems,we propose a new health monitoring system with physiological parameter measurement,correction,and feedback.The study collects clinical samples of the elderly to formulate regression equations and statistical models for analyzing the relationship between gender,age,measurement time,and physical signs.After multiple adjustments to measurements of physical signs,the correction algorithm compares the data with a standard value.The process significantly reduces the risk of misjudgment while matching users’health status more accurately.The application case of this paper proves the validity of the method for measuring and correcting heart rate results in the elderly and presents a specific correction procedure.Additionally,the correction algorithm provides a scientific basis for eliminating or modifying other influencing factors in future health monitoring studies.

Constructing a Monitoring Mode over English Autonomous Learning in the Networking Age

In the Networking age,the monitoring from teachers and classmates over one's English autonomous learning is weakened.The relevance among learning resources can't be measured,and the learning efficiency can't be ensured,either.In order to solve the problems,a monitoring mode over English autonomous learning process is constructed,which involves the application of the internal and external monitoring strategies.

DEBRIS MONITORING AND ANALYZING SYSTEM (DMAS) AND ITS APPLICATION IN AEROENGINE

By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing system (DMAS) is developed from the traditional iron spectrum technology, and has such characteristics as ease for debris separating, forecasting machine failure automatically and accurately in time and so on. The fundamental theory, components and its application in aeroengine health monitoring of DMAS are presented.

Wind-induced instabilities and monitoring of wind turbine

This paper presents real-time monitoring data and analysis results of the non-stationary vibrations of an operational wind turbine. The advanced time-frequency spectrum analysis reveals varied non-stationary vibrations with timevarying frequencies, which are correlated with certain system natural modes characterized by finite element analysis. Under the effects of strong wind load, the wind turbine system exhibits certain resonances due to blade passing excitations. The system also exhibits certain instabilities due to the coupling of the tower bending modes and blade flapwise mode with blade passing excitations under the variation of wind speed. An analytical model is used to elaborate the non-stationary and instability phenomena observed in experimental results. The properties of the nonlinear instabilities are evaluated by using Lyapunov exponent estimation.

Local measurement for structural health monitoring

Localized nature of damage in structures requires local measurements for structural health monitoring. The local measurement means to measure the local, usually higher modes of the vibration in a structure. Three fundamental issues about the local measurement for structural health monitoring including (1) the necessity of making local measurement, (2) the difficulty of making local measurement and (3) how to make local measurement are addressed in this paper. The results from both the analysis and the tests show that the local measurement can successfully monitor the structural health status as long as the local modes are excited. Unfortunately, the results also illustrate that it is difficult to excite local modes in a structure. Therefore, in order to carry structural health monitoring into effect, we must (1) ensure that the local modes are excited, and (2) deploy enough sensors in a structure so that the local modes can be monitored.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Monitoring method of gear teeth failure of hydraulic gear pump based on improved VMD and DBN-DNN of electrical signal

Abundant system operation state information is included in the electrical signal of the hydraulic system motor.How to accurately extract and classify the operation information of electrical signal is the key to realize the condition monitoring of hydraulic system.The early fault characteristics of hydraulic gear pump hidden in the motor current signal are weak and difficult to extract by traditional time-frequency analysis.Based on the correlation coefficient and artificial bee colony algorithm(ABC),the parameter optimization of variational mode decomposition(VMD)is realized in this paper.At the same time,the principle of maximum signal correlation coefficient and kurtosis value is adopted to determine the effective intrinsic mode function(IMF).Moreover,the permutation entropy(PE)and root mean square(RMS)of the effective IMF components are input into the deep belief network(DBN-DNN)as high-dimensional feature vectors.The operation state of gear pump is monitored.The results show that the weak characteristics of current signal of gear pump fault are accurately and stably extracted by this method.The running state of gear pump is monitored and the accuracy of gear fault diagnosis is improved.

EXPERIMENTAL STUDY ON IN-PROCESS MONITORING OF CUTTING CHATTER

The most significant characteristic in frequency domain during cutting chatter occurring process is the steep rise of the vibration energy in certain narrow frequency band containing the chatter frequency. In accordance with the frequency band-energy principle, a reliable criterion for chatter judgement is proposed and the in-process detection of cutting chatter is realized by the use of microcomputer. This method has the advantages of rapidity, high sensitivity, accuracy and high resistance to interference. Some concrete measures taken in practical applications are also discussed.

Real-time parameters monitoring system for underground equipment based on panoramic images

Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.

Dynamic Deformation Monitoring for Long-Span Bridges under Construction and EMD De-noising Method

Construction progress of long-span bridge is complicated and the quality control is strict. Any disadvantage during construction may potentially affect the internal forces and deck alignments after it is open to traffic. To exactly evaluate the periodic alignments, internal forces and safety, geometrical and physical monitoring are needed during construction. This study aims at the requirement of dynamic geometric monitoring during Sutong Bridge construction, and introduces the realization and observing schemes of the self-developed GPS real-time dynamic geometrical deformation monitoring system. Affected by wind load and construction circumstance, GPS （global positioning system） monitoring signal contains a variety of noise. And the useful signal can be extracted from the signal after de-noising the noises. A de-noising method based on EMD （empirical mode decomposition） model is introduced here to process the bridge dynamic monitoring data, and with the wavelet threshold de-noising method are compared. The result shows that the EMD method has good adaptability, is free from the choice of wavelet bases and the number of decomposition layer. The method is an effective de-noising method for dynamic deformation monitoring to large-span bridges.

Develop and Implement of Real-time Network Monitoring Software based on C/S

This article mainly introduces the multi-layer distributed C/S architecture of system design scheme. Its working principle is the client program runs automatically after the computer starts, and establish communication with the application server. The network administrator can monitor and intelligent management of the client computer through the server program, the computer will execute the corresponding operation according to the server to send command instructions. The system realize the main module of the whole system framework, network monitoring data initialization module, network data transmission module, image coding and decoding module, the advantages of system make full use of existing LAN resources, timely delivery and manaRement information.

Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by Empirical Mode Decomposition Method

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element Model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The Empirical Mode Decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

老采空区地表沉降预测合理监测模式分析

为研究老采空区沉降监测数据时间间隔对预测精度的影响,本文利用某老采空区地表沉降监测点实测沉降数据,在等时间间隔、非等时间间隔两种情况下建立两种方案、六种沉降预测模式,采用长短期记忆神经网络(LSTM)预测模型对老采空区地表沉降进行预测,以平均绝对误差(MAE)和平均绝对百分比误差(MAPE)作为评价指标,分析不同时间间隔监测数据对预测精度的影响。结果表明,在总监测时长不变的情况下,预测精度随平均监测间隔时长的增长呈先增高后降低的趋势,即并非监测间隔越短,预测精度越高,而是在相应监测间隔范围内存在预测精度最优值。研究成果可为老采空区监测方案设计及沉降预测模式提供借鉴和指导。