Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Comparative analysis of online monitoring methods for transformer insulation performance of 330-750kV substation

The online-monitoring methods for insulation performance of current transformers of 330-750 kV substation are analyzed and compared.The effectiveness and availability of each method are discussed.Main features,advantages and disadvantages of each method and its corresponding standard are also described.

Research on Online Monitoring Methods on SF_(6) Circuit Breakers

To promote the accuracy and application of arcing time measurement for SF_6 circuit breaker in substation,five measurement methods are investigated by two cases experimentally. First,the test results of the five methods for a circuit breaker in different stages of wear and a circuit breaker with a component failure were presented. Then,the time error is analyzed by simulation.Finally,the advantage and disadvantage of these methods are discussed.

Online Monitoring Volume Deformation of Cement-based Materials in Multiple Environments

Comparing and analyzing some volume deformation measuring means for cement-based materials at home and abroad, a continuous online monitor of cement-based material volume deformation in multiple environments is developed. The device is designed based on the environmental simulation technology, micro-distance measuring technology of laser and eddy current, and transmission agent online monitoring the deformation of multi-group samples. This device can be used widely, such as glass, ceramics, walling material, and so on, with high precision, low testing cost, and intellectualization.

Vapor Online Monitor Model of Vapor Power Station Based on UML

We presents a vapor online monitor system model of vapor power station developed by visual tool rational rose 2000. Use cases such as on line instrument (onlineinstr), control, query, report, real database (realdb) and alarm are generated according to the system requirements. Use case view and class view of the system are formed at the same time. As for all the UML models of the system, this paper focuses the discussion on the class view, the component diagram of the control class and the sequence diagram of the query class. Corresponding C++ codes are produced and finally transferred into the spot running software.

Online Monitoring the Products Quality by Measuring Cavity Pressure during Injection Molding

Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temperature, melt temperature, packing pressure and packing time, affect the final properties of product. The cavity pressure is a significant key factor. Residual stress and injection molding weight are significantly affected by the cavity pressure. This study created an approach to predict weight of injection-molded by real-time online cavity pressure monitoring. This study uses a 6-inch with thickness lmm light guide panel and the largest area beneath the pressure curve of time as well as the maximum pressure as its characteristic. The upper and lower limits of the control are set to ＋2 standard deviations, and GUI （Graphical User Interface）-based LabVIEW software is used to perform calculation and analysis of the pressure curve. The results of the experiment show that the online internal cavity pressure monitoring system can effectively monitor the quality of the molded products. In 500 injection molding cycle tests, its error rate was less than 8%, whereas the deviation in mass of the molded products selected through the system＇s filtering process was successfully controlled to be within ±4%.

In-situ analysis of laser-induced breakdown spectra for online monitoring of femtosecond laser machining of sapphire

Laser-induced breakdown spectroscopy(LIBS)is widely used for elemental analysis.However,its application for monitoring and analyzing a laser machining process by examining the changes in spectral information warrants further investigation.In this study,we investigate the effect of laser parameters on the spectra,variations in the time-resolved plasma emission spectra,and the relationship between the morphology of craters and plasma plume evolution during the femtosecond(fs)laser ablation of sapphire.The Boltzmann plot method and Stark’s broadening model are employed to estimate the temporal temperature and electron density of the plasma plume,revealing the process of plasma evolution.This study aims to demonstrate the feasibility of LIBS for online monitoring of laser processing through experimental data and theoretical explanations.

An ultra-thin bolt tension sensor and online monitoring system:For application in hydropower plant unit

The condition of bolted connections significantly affects the structural safety.However,conventional bolt tension sensors fail to provide precise measurements due to their bulky size or inadequate stability.This study employs the piezoresistive effect of crystalline silicon material to fabricate an ultrathin sensor.The sensor exhibits a linear relationship between pressure and voltage,an exceptional stability at varying temperatures,and a superior resistance to corrosion,making it adaptable and user-friendly for applications of high-strength bolt tension monitoring.A monitoring system,incorporating the proposed sensor,has also been developed.This system provides real-time display of bolt tension and enables the assessment of sensor and structural conditions,including bolt loosening or component failure.The efficacy of the proposed sensor and monitoring system was validated through a project carried out at the Xiluodu Hydropower Plant.According to the results,the sensor and online monitoring system effectively gauged and proficiently conveyed and stored bolt tension data.In addition,correlations were created between bolt tensions and essential unit parameters,such as water head,active power,and pressures at vital points,facilitating anomaly detection and early warning.

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts.Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part.In this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed.Using the proposed method,an average temperature monitoring accuracy of 97%is achieved in various heating patterns.In addition,this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part.This method was experimentally validated during the self-resistance electric heating process,in which the monitoring accuracy reached 93.1%.This method can potentially be applied to automated manufacturing and process control in the composites industry.

Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

Online Monitoring Method Based on Locus-analysis for High-voltage Cable Faults

Online diagnosis methods for high-voltage(HV)cable faults have been extensively studied.Power cable fault monitoring is not accurate,and the degree of data fusion analysis of current methods is insufficient.To address these problems,an online monitoring method based on the locus-analysis for HV cable faults is developed.By simultaneously measuring two circulating currents in a coaxial cable,a two-dimensional locus diagram is drawn.The fault criteria and database are established to detect the fault by analyzing the changes of the locus characteristic parameters.A cross-connected grounding simulation model of a high-voltage cable is developed,and several faults at different locations are simulated.Fault identification is established based on the changes in the long axis,short axis,eccentricity,and tilt angle of the track.The simulation and field experiments show that this method can provide an increased amount of fault-monitoring reference information,which can improve the monitoring accuracy and provide a new approach to cable fault monitoring.

Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

The displacement of transmission tower feet can seriously affect the safe operation of the tower,and the accuracy of structural health monitoring methods is limited at the present stage.The application of deep learning method provides new ideas for structural health monitoring of towers,but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning(DL).In this paper,we propose a DT-DL based tower foot displacement monitoring method,which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method.Then the vibration signal visualization and Data Transfer(DT)are used to add tower fault data samples to solve the problem of insufficient actual data quantity.Subsequently,the dynamic response test is carried out under different tower fault states,and the tower fault monitoring is carried out by the DL method.Finally,the proposed method is compared with the traditional online monitoring method,and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process.The results show that the method can effectively identify the tower foot displacement state,which can greatly reduce the accidents that occurred due to the tower foot displacement.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

An inverse method for online stress monitoring and fatigue life analysis of boiler drums

A method based on solution of the inverse heat conduction problem was presented for online stress monitoring and fatigue life analysis of boiler drums. The mathematical model of the drum temperature distribution is based on the assumptions that the difference of temperature along the longitudinal axis of the boiler drum is negligible with changes only in the radial direction and the circumferential direction, and that the outer surface of drum is thermaUy insulated. Combining this model with the control-volume method provides temperatures at different points on a cross-section of the drum. With the temperature data, the stresses and the life expectancy of the boiler drum are derived according to the ASME code. Applying this method to the cold start-up process of a 300 MW boiler demonstrated the absence of errors caused by the boundary condition assumptions on the inner surface of the drum and testified that the method is an applicable technique for the online stress monitoring and fatigue life analysis of boiler drums.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

UTM:A trajectory privacy evaluating model for online health monitoring

A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.

Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting

Surface roughness is one of the most critical attributes of machined components,especially those used in high-performance systems.Online surface roughness monitoring offers advancements comparable to post-process inspection methods,reducing inspection time and costs and concurrently reducing the likelihood of defects.Currently,online monitoring approaches for surface roughness are constrained by several limitations,including the reliance on handcrafted feature extraction,which necessitates the involvement of human experts and entails time-consuming processes.Moreover,the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental settings.To address these challenges,this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting conditions.Tooltip acceleration signals were acquired during each cutting experiment to develop two datasets,and no handcrafted features were extracted.Five deep learning models were developed and evaluated using standard performance metrics.A convolutional neural network stacked on a long short-term memory network outperformed all other network models,yielding exceptional results,including a mean absolute percentage error as low as 1.51%and an R2value of 96.6%.Furthermore,the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously employed.The performance of the model remained consistent and commendable under varied conditions,asserting its applicability in real-world scenarios.

Analytical Solution and Numerical Simulation of Real-Time Dispersion Monitoring Using Tone Subcarrier

A method for online dispersion monitoring by adding a single in-band subcarrier tone isintroduced.According to the theoretical analysis,the dispersion monitor and measurement range aredetermined by the specific frequency of the subcarrier tone.By using simulation tools,figures aboutrelationship between power of subcarrier tone and transmission distance in ideal condition are shown.