Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam,so determining the deformation monitoring control value can effectively monitor the safety of dam operation.The traditional dam deformation monitoring control value only considers the single measuring point.In order to overcome the limitation,this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points.A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of eachmeasuring point.The projection pursuit method(PPM)combined with the grey wolf optimization(GWO)algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation.The peaks over threshold(POT)model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement.In addition,the POTmodel is improved with the automatic threshold determinationmethod based on the 3σcriterion in probability theory and the GWO algorithm,which can avoid subjectivity and randomness in determining the threshold.The results of the engineering application show the feasibility and applicability of the proposed method.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging,given the significant risk it poses for both maternal and foetal health outcomes.While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests,the advent of continuous glucose monitoring(CGM)systems has revolutionized the approach.These devices offer a safe and reliable means of tracking glucose levels in real-time,benefiting both women with diabetes during pregnancy and the healthcare providers.Moreover,CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes,especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device.Such a combined approach has been demonstrated to improve overall blood sugar control,lessen the occurrence of preeclampsia and neonatal hypoglycaemia,and minimize the duration of neonatal intensive care unit stays.This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Underground ground control monitoring and interpretation,and numerical modeling, and shield capacity design

Mine or longwall panel layout is a 3D structure with highly non-uniform stress distribution. Recognition of such fact will facilitate underground problem identification/investigation and solving by numerical modeling through proper model construction. Due to its versatility, numerical modeling is the most popular method for ground control design and problem solving. However numerical modeling results require highly experienced professionals to interpret its validity/applicability to actual mining operations due to complicated mining and geological conditions. Underground ground control monitoring is routinely performed to predict roof behavior such as weighting and weighting interval without matching observation of face mining condition while the mining pressures are being monitored, resulting in unrealistic interpretation of the obtained data on mining pressure. The importance of ground control pressure monitoring and simultaneous observation of mining and geological conditions is illustrated by an example of shield leg pressure monitoring and interpretation in an U.S. longwall coal mine: it was found that the roof strata act like a plate, not an individual block of the size of a shield dimension, as commonly assumed by all researchers and shield capacity is not a fixed property for a longwall panel or a mine or a coal seam. A new mechanism on the interaction between shield's hydraulic leg pressure and roof strata for shield loading is proposed.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

Nonparametric Control Scheme for Monitoring Phase Ⅱ Nonlinear Profiles with Varied Argument Values

Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply.

A wireless solution for greenhouse monitoring and control system based on ZigBee technology

With the rapid development of wireless technologies, it is possible for Chinese greenhouses to be equipped with wireless sensor networks due to their low-cost, simplicity and mobility. In the current study, we compared the advantages of ZigBee with other two similar wireless networking protocols, Wi-Fi and Bluetooth, and proposed a wireless solution for green- house monitoring and control system based on ZigBee technology. As an explorative application of ZigBee technology in Chinese greenhouse, it may promote Chinese protected agriculture.

Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

A hierarchical architecture of centralized monitoring and controlling system and its high-performance and interoperability protocol

This paper describes a hierarchical architecture and a high-performance and interoperability protocol for centralized monitoring and controlling systems (CMCS) . The protocol we proposed can interoperate different monitoring and controlling systems constructed by different companies, each with different functions and communication protocols. The protocol reduces the amount of traffic and has real-time and high-perfor-mance advantages. The protocol was implemented in CMCS for telecommunication power supply and air-condi-tioner used by the Telecommunication Bureau of Zhejiang Province. This paper deals with the hierarchical architecture and function of CMCS and packet format, command ID, and SDL description of its protocol. We also discuss the properties of the interoperability and performance of the protocol in this paper.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Microseismic monitoring and forecasting of dynamic disasters in underground hydropower projects in southwest China:A review

The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.

Oscillatory Failure Detection for Flight Control System Using Voting and Comparing Monitors

Oscillatory failure cases(OFC)detection in the fly-by-wire(FBW)flight control system for civil aircraft is addressed in this paper.First,OFC is ranked four levels:Handling quality,static load,global structure fatigue and local fatigue,according to their respect impact on aircraft.Second,we present voting and comparing monitors based on un-similarity redundancy commands to detect OFC.Third,the associated performances,the thresholds and the counters of the monitors are calculated by the high fidelity nonlinear aircraft models.Finally,the monitors of OFC are verified by the Iron Bird Platform with real parameters of the flight control system.The results show that our approach can detect OFC rapidly.

Monitoring and control technology of solar photovoltaic power generation system

In view of characteristics of solar photovoltaic (PV) power station such as the decentralized layout and massive monitoring and control information, a solar PV power generation monitoring and control system has been designed. The system is designed into three layers namely the sensor and actuator layer, the PLC field monitoring and control layer and the remote network monitoring and control layer. Through ZigBee wireless network, PROFIBUS and GPRS wireless network, the system makes the three layers exchange information rapidly, and the system supervises not only various operational parameters of the power generating system but also weather changes as a way to change the solar tracking strategy of the PV power generating system and reduce the operating energy consumption of the system. Through the hardware redundant design of PLC central controller and the upper computer, the solar PV power station can be more secure and reliable when running.

CONTROLLING AND MONITORING THERMOTE NICAL PARAMETERS OF GLASS MELTING FURNACE

According to the characteristics of the ther-motechnical parameters such as temperature pressure and glass level for glass melting furnace, the design method for monitoring and controlling these parameters is introduced in this paper based on MACROMAX-2 concentrated and dis-tributedcontrol system. The configuration of management information and control loop is described, and research tests were performed to check the functions of system.

Development and experience in the monitoring,assessment and control of environmentalpollutants in Hong Kong

The late start of environmental protection in Hong Kong was discussed in the light of problems encountered during the development of environmental protection legislation in Hong Kong for the past 20 years. The collaboration in monitoring and assessment of environmental pollutants between the University of Hong Kong and various governments were descrbed in parallel with the progress in environmental protection in Hong Kong. The developments of new analytical techniques for environmental monitoring and analysis is given and their application in environmental control described. The joint projects in assessment and control of environmental pollutants carried out in collaboration with local industries and other organizations within and without the university are given and discussed. The problems and possible solution facing Hong Kong in development control equipment for small scale industries are discussed and areas of development identified. The development and experience in the monitoring assessment and control of environmental pollutants in Hong Kong are summarized and areas of difficulties are illustrated.

Application of ArtificialNeural Network to Real-Time Condition Monitoring Control and Usual Trouble Diagnosis during Driling

ＡｐｐｌｉｃａｔｉｏｎｏｆＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋｔｏＲｅａｌ┐ＴｉｍｅＣｏｎｄｉｔｉｏｎＭｏｎｉｔｏｒｉｎｇＣｏｎｔｒｏｌａｎｄＵｓｕａｌＴｒｏｕｂｌｅＤｉａｇｎｏｓｉｓｄｕｒｉｎｇＤｒｉｌｉｎｇ＊ＳｈｉＹｕｓｈｅｎｇＤｅｐａ...

Integration Between Enterprise Process Monitoring and Controlling System and Enterprise Application

The relationships and the features of integration between Enterprise ProcessMonitoring and Controlling System (EPMCS) and Enterprise Process Related Applications (EPRA) wereanalyzed. An integration architecture centered on EPMCS was presented, in which there were fourlayers to connect from EPMCS to EPRA: EPMCS, application integration layer, transport layer andEPRA, and there were four layers used to etstablish integration: presentation layer, function layer,data layer and system layer. The frameworks to connect EPMCS and EPRA were designed, thatEnterprise-Independent Model (EIM), Enterprise-Specific Model (ESM) and meta-model to describe thesetwo models were defined. The method to integrate data based on XML was designed to exchange datafrom EPMCS to EPRA according to the mapping between EIM and ESM. The approches are suitable forintegrating EPMCS and systems in Product Data Management (PDM), project management and enterprisebusiness management.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.