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and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution.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。

Design of a d_(33)-mode piezocomposite electricity generating element and its application to bridge monitoring

The piezoelectric effect is used in sensing applications such as in force and displacement sensors.However,the brittleness and low performance of piezoceramic lead zirconate titanate(PZT) often impede its applicability in civil structures which are subjected to large loads.The concept of a piezocomposite electricity generating element(PCGE) has been proposed for improving the electricity generation performance and overcoming the brittleness of piezoceramic wafers.The post-curing residual stress in the PZT layer constitutes a main reason for the PCGE's enhanced performance,and the outer epoxy-based composites protect the brittle PZT layer.A d33-mode PCGE designed for bridge monitoring application was inserted in a bridge bearing to provide a permanent and simple weigh-in-motion system.The designed PCGEs were tested through a series of tests including fatigue and dynamic tests to verify their applicability for monitoring purposes in a bridge structure.A simple beam example was presented to show the applicability of the proposed bridge bearing equipped with the PCGE for adequately measuring the traffic loads.

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.

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.

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.

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。

基于动响应数据特征的桥梁结构损伤识别

介绍了本征正交分解(Proper Orthogonal Decomposition,POD)的基本原理,探讨了POD在桥梁结构损伤识别中的应用。提出了基于动响应数据特征的桥梁结构损伤识别方法,该识别方法基于POD技术对桥梁结构在不同位置、不同时刻收集到的位移快照矩阵(Snapshot Matrix)进行本征正交分解,得到结构的本征正交模态(POMs),进而构造出损伤指标来识别结构的损伤位置及程度,实现了对桥梁结构损伤的多工况识别。并以保定黄花沟桥为例,通过数值模拟试验,验证了该方法的有效性,结果表明POD能够从空心板桥结构的振动响应数据中提取出结构的本质特征,并且提取过程简单、快捷,可为桥梁结构提供一种有效的损伤识别方法。

基于数字孪生和深度学习的结构损伤识别

土木工程实际结构损伤状态的时间跨度通常只占总生命周期的一小部分。为解决传统基于数据驱动的结构损伤识别方法缺乏足够多的损伤训练数据的问题,提出结合数字孪生和深度学习的结构损伤识别方法,并应用于实际工程。该方法利用数值仿真模型和在线监测数据构建结构的数字孪生,以获得不同损伤工况下结构动力响应的“大数据”;为了摆脱对外激励信息的依赖,应用经验模态分解法和传递率函数对得到的数据进行预处理;将预处理后的固有模态传递率函数数据作为深度学习的输入进行训练,实现结构的损伤识别。为验证方法的有效性,对实际结构未经训练的监测数据进行分析,结果表明,该方法泛化能力良好,能够有效识别结构损伤状况。通过数字孪生技术解决了传统方法数据匮乏的问题,不需要任何地震信息,利用固有模态传递率函数数据训练的深度神经网络仍能保持较高的损伤识别准确率,二者结合可以使工程结构健康监测更为主动、可靠、高效。