Online Monitoring Of Parameters Research Articles

Real-time stress field reconstruction method using online monitoring parameters for thick-walled power plant components based on gappy proper orthogonal decomposition

The three-dimensional stress distribution information of in-service components is of great significance for the intelligent operation and maintenance of power plants. A real-time stress field reconstruction method based on the gappy proper orthogonal decomposition (POD) and prediction function is proposed for condition monitoring of high-temperature pressured thick-walled components. Based on the “snapshots” method, the standard POD orthogonal basis is extracted from the high-fidelity data obtained from finite element analysis under various hot start load conditions to capture the main features of the stress fields. The complete stress field is reconstructed by the linear combination of the standard POD basis, and the modal coefficients are calculated using the discrete node stresses through the gappy POD procedure. Since only the online monitoring parameters of steam are available in practical operations, the prediction function is used to establish the mapping relationship between the monitoring parameters and node stress. The discrete nodes are determined by the trial-and-error method. The small global relative errors between the reconstructed stress fields and numerical solutions indicate the high accuracy of the proposed method. The spatial error of the entire stress field and the temporal error of the maximum stress were also evaluated. The computation time of the reconstruction process is significantly shorter than the sampling time step. The performance of gappy POD on different numbers of snapshots and modes was also presented.

Health assessment for low-voltage electromagnetic coils based on fusion of creep degradation mechanism and transformed MD

Low-voltage electromagnetic coils play ever-increasing roles in safety-critical systems, like electromechanical actuators in more electric aircraft and tractors in zero emission vehicles. Identifying insulation degradation prior to the formation of turn-to-turn short can effectively avoid accidental shutdown and enable predictive maintenance for these systems. High-frequency electrical response analysis is currently a promising technique for electromagnetic coil insulation health assessment. However, the correlation between high-frequency electrical response parameters (HFERP) and actual insulation degradation status (AIDS) is still not clear, which brings challenges in insulation health assessment and remaining useful life prediction. Therefore, in this paper, a fusion method based on creep degradation mechanism and transformed mahalanobis distance (MD) for insulation health assessment is proposed. First, correlation analysis between the coil HFERP and the AIDS is performed from coil insulation coating creep degradation point of view. Second, a coil on-line monitoring parameter (OLMP) for monitoring insulation degradation status on-site is constructed based on transformed MD. A correlation model between OLMP and AIDS is consequently built to assist in determining OLMP failure threshold for coil insulation health assessment. Coil accelerated testing shows that the fusion method can be used to assess insulation status prior to the formation of harmful shorts, enabling condition-based maintenance for components or systems that incorporate electromagnetic coils.

Power plant turbine power trend prediction based on continuous prediction and online oil monitoring data of deep learning

Power output is an important property of steam turbines and more accurate trend prediction is essential for understanding the operation of power plants and anomaly detection in equipment. Equipment power is strongly influenced by human factors and achieving accurate trend prediction is often impossible. In the current research, a continuous prediction model was developed on the basis of deep learning to establish the relationships among oil online monitoring parameters and equipment power. The model used long short term memory (LSTM) method to develop a trend prediction model for oil wear state parameters. Trend prediction results were applied as a test set to establish a deep belief networks (DBN) prediction model for predicting device power. In modeling process, continuous prediction model was optimized via feature selection method on the basis of ridge regression with L2 regularization, recursive feature elimination (RFE) and differential evolution (DE) algorithms for the elimination of subjective factor to decrease cumulative error of forecasting. Comparative experimental results showed that LSTM-RFE-DE-DBN continuous prediction model outperformed LSTM-RFE-DE-BPNN and LSTM-DBN. The developed model realized continuous prediction and applied lubricating oil wear status with objective factors to perform the power prediction of power plant turbines with subjective factors.

Optical Sensor for Monitoring Leakage Current and Weather Conditions in a 500-kV Transmission Line

The leakage current (LC) caused by the surface contamination of insulators, together with environmental variables, is one of the most basic online monitoring parameters for insulator status. However, the impact of weather conditions such as temperature, air humidity, and dew point on the LC has not been deeply studied until now. In this paper, based on meteorological data obtained online and LC obtained with an optical fiber sensor, installed in 500-kV insulator strings of a transmission line, the impact of weather conditions was studied. Results indicate that the LCs follow a specific pattern, according to weather conditions. The system has been continuously monitoring LC, humidity, temperature, and dew point uninterrupted for three years, sending the acquired data to a web page; therefore, it has been demonstrated to be robust, reliable, and repetitive. The sensor features the broadband response and acquisition capabilities of partial discharge pulses in high-voltage insulators, allowing the detection of high-frequency pulses. When comparing the LC measured in this work with those from other works, our measurements are substantially higher; this is due to the type of pollution found in this specific situation, which includes iron oxide powder, producing a conductive layer over the insulator surface that, unlike sea salt, does not depend on humidity to conduct an LC. One of the conclusions reached in this work is that partial discharge surges are caused when the local temperature reaches the dew point and not simply from the presence of high humidity, as stated in many works dealing with LCs. The monitored LC can be used as an indicative parameter of a possible flashover, enabling the proper planning of insulator predictive maintenance, either by jet-washing the surface or even changing the insulators when they are damaged.

SHEAR HEATING PARAMETER OF RUBBER COMPOUNDS USEFUL FOR PROCESS CONTROL IN INJECTION MOLDING MACHINE

ABSTRACT Control of the injection molding process of rubber is made complex by the influence of many parameters. Shear heating, heat generation due to viscous dissipation, is a phenomenon largely exploited in rubber to lower compound viscosity, particularly in the extrusion phase, but it is impossible to predict it from laboratory tests. With the aim of providing useful tools for process control, a parameter for on-line monitoring of shear heating phenomenon is proposed. This parameter is based on direct measurement of rubber surface temperature by infrared thermal camera at the nozzle outlet of the injection molding machine extruder. The measured rubber temperature is a process indicator to give the thermal history of the rubber injection and process safety. Measured temperature increase is then converted into a parameter having dimensions of viscosity, the shear heating parameter, ηSH. Four different rubber compounds are investigated. The ηSH results are compared to minimum torque (ML) from routine rheometric laboratory measurement. The calculated ηSH values increase with increasing ML values. The ML value is a rough indication of the rubber compound viscosity, where its relevant variations affect the rubber processability. Meanwhile ηSH is more sensitive to small processability variations and more accurate because it takes into account the thermal history during the injection stage. The found relationship provides deeper insights into the behavior of rubber, compared to laboratory tests, and how it can be used in the industrial practice to improve the process control by monitoring the shear heating effect during the injection stage.

Design of an online monitoring system forthree-phasebelted HTS cables

Three-phase belted High Temperature Superconductor (HTS) cables have compact dimensions and small inter-phase magnetic interference, and have been applied in the Demonstration Project of Domestic km-Level HTS Cable System of State Grid Corporation of China. The three cores of three-phasebelted HTS cablesare bundled together in the same low-temperature bellows, so a single-phase fault may cause the quench of other two phases, and even lead to an overall failure of the three-phase belted cable. Based on the cable body characteristics, cable termination characteristics and cooling system characteristics of the three-phase belted HTS cables, the related online monitoring parameters and monitoring methods are proposed. The design of an online monitoring system for the three-phase belted HTS cables is introduced.

Application of Data Mining Techniques for Sensor Drift Analysis to Optimize Nuclear Power Plant Performance

The Power Plants are engineered and instrumented to ensure safety in all modes of operation. Hence they should be continuously monitored and maintained with necessary Instrumentation to identify performance degradation and the root causes to avoid calling for frequent maintenance. The degraded performance of Instrumentation & Control systems may also lead to plant outages. Different studies have suggested that a well maintained instrumentation with errors and response times within the permissible limits may increase the availability minimizing outages. The I&C systems are designed for monitoring, control and safety actions in case of an event in a power plant. The sensors used are single, redundant, triplicated or diverse based on the type of application. Where safety is of prime concern, triplicated and 2/3 voting logic is employed for initiating safety actions. Diverse instruments are provided for protecting the plant from any single abnormal event. Redundant sensors are used to improve plant availability. Wherever 2/3 logics are used, the sensors shall uniformly behave and the drifts across the sensor may lead to crossing the threshold, initiating a protective action. Instead of waiting for the regular preventive maintenance schedule for recalibrating the sensors, the drift in the sensors are analyzed by developing a combined overall online monitoring parameter which will give an early warning to the operator the need for recalibration of the redundant sensors. This paper deals with development of one such parameter through data mining techniques for a representative process in a nuclear power plant.

Development and application of in-situ microscopy in on-line monitoring of cell biomass

With the rapid development of modern biotechnology, fermentation process is increasingly important in industrial production. To guarantee the stability of products, fermentation process should be elaborately monitored and controlled. Biomass is an important parameter for on-line monitoring in bioprocesses because biomass can reflect cell growth in a bioreactor directly. In-situ microscope, a non-invasive and image-analysis based technology, can real-time monitor cells in biological process. This review summarizes the development and application of in-situ microscopy in biomass monitoring.

Leakage Current Response Mechanism of Insulator String With Ambient Humidity on Days Without Rain

The leakage current, which is influenced by the operating voltage, the surface contamination, and environmental variables, is one of the most basic online monitoring parameters for the surface contamination status of insulators. However, the impact of temperature and humidity on leakage current have not been expressed concisely nor distinguished according to sunny and rainy weather. In this paper, based on leakage current online monitoring data, the time-domain waveforms of leakage current of an insulator string in Shenzhen, China, were compared with those of the ambient temperature and humidity. Results indicate that the leakage current presents a saddle-shaped curve on days without rain, and that there is a remarkable positive correlation between leakage current and humidity as well as a negative correlation between leakage current and temperature. In a short period (several days) without rain, as long as the relative humidity keeps constant, the leakage current would vary insignificantly, whereas it may vary in a wide range at the same temperature. Moreover, the functional relation mathematical model ${I = {a\ast e}}^{b\ast RH}$ between the first harmonic component of leakage current and the relative humidity is presented, inferring that the insulator string leakage current depends mainly on the ambient relative humidity in a day without rain.

Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process

To implement an automated fluid magnetic abrasive finishing process, an online monitoring scheme is proposed based on characterizing acoustic emission signals in this paper. According to the material removal mechanisms during the fluid magnetic abrasive finishing process, the acoustic emission generation and characteristics are predicted analytically to be dominated by the interactions between the surface asperities and the abrasive particles. Moreover, the interactions and corresponding acoustic emission events will become weaker as the finishing process progresses and the surface becomes smoother. Experimental studies show that the amplitude and the occurrence rate of continuous acoustic emission waves and intermediate bursts reduce gradually with the progression of the finishing process. Based on these features, root mean squared values and burst occurrence rates, being of the lowest computational requirements, are suggested as online monitoring parameters for an automated and intelligent finishing in fluid magnetic abrasive manufacturing. The proposed method is verified experimentally, showing that the root mean squared values are highly consistent with the measured surface roughness values, which confirms the dynamic mechanisms between the fluid magnetic abrasive finishing and acoustic emission generation sources examined.

Stoichiometric evaluation of partial nitritation, anammox and denitrification processes in a sequencing batch reactor and interpretation of online monitoring parameters

A laboratory-scale sequencing batch reactor (SBR) performing partial nitritation – anammox and denitrification was used to treat anaerobic digester effluents. The SBR cycle consisted of a short mixing filling phase followed by oxic and anoxic reaction phases. Working at 25 °C, an ammonium conversion efficiency of 96.5%, a total nitrogen removal efficiency of 88.6%, and an organic carbon removal efficiency of 63.5% were obtained at a nitrogen loading rate of 0.15 kg N m−3 d−1, and a biodegradable organic carbon to nitrogen ratio of 0.37. The potential contribution of each biological process was evaluated by using a stoichiometric model. The nitritation contribution decreased as the temperature decreased, while the contribution from anammox depended on the wastewater type and soluble carbon to nitrogen ratio. Denitrification improved the total nitrogen removal efficiency, and it was influenced by the biodegradable organic carbon to nitrogen ratio. The characteristic patterns of conductivity, oxidation-reduction potential (ORP) and pH in the SBR cycle were well related to biological processes. Conductivity profiles were found to be directly related to the decreasing profiles of ammonium. Positive ORP values at the end of the anoxic phases were detected for total nitrogen removal efficiency of lower than 85%, and the occurrence of bending points on the ORP curves during the anoxic phases was associated with nitrite depletion by the anammox process.

On-line fouling monitoring model of condenser in coal-fired power plants

The fouling of water side surfaces decreases the thermal effectiveness of the condenser and it is always one of operational focuses in coal-fired power plants. This study presents a comprehensive approach to realize the on-line fouling monitoring for the condenser in thermal power plants. Based on the operational mechanism and the coupling property, an all-condition mechanism model (ACMM) of coal-fired power plants is proposed to simulate coupling operating characters of systems. The simulation results of a 600MW/16.7MPa/560°C/560°C supercritical coal-fired power plant indicate that the model is of sufficient accuracy for performance calculations under different off-design conditions. An on-line fouling model of the condenser is presented according to simulation results of ACMM and on-line monitoring parameters from SIS system. For the fouling model, a correction as a function of the non-condensable gas is implemented to improve the accuracy of the condensation heat transfer resistance in the steam side of the condenser. The results of the on-line fouling monitoring in the field reveal that the fouling of the condenser increased with time and the fouling growth rates kept relative stable. Furthermore, this study also provided an alarm for the leakage fault in the condenser according to the abnormal fouling resistance tend.

基于BP神经网络和SVM的电厂粉尘浓度在线监测

基于电厂经济环保运行的要求，需对电厂的污染物排放浓度实时监测。本文以排烟粉尘浓度为例，通过分析电厂DCS系统的在线监测数据，建立了BP神经网络和支持向量机两种粉尘浓度的在线监测模型。对模型进行实例验证(数据来源于某电厂600 MW机组)，结果显示BP模型的预测精度达到96%以上，而SVM模型精度则达到97.5%以上。从总体上看，这两种模型对于粉尘浓度在线监测效果都比较理想，相对而言SVM模型的模拟的精度较高，且具有更高的泛化能力。 For the purpose of achieving online monitoring of dust concentration, the online monitoring pa-rameters in DCS system are adopted to analyze the factors which influence the concentration of smoke dust, and the BP neural network and support vector machine are used to propose an on-line monitoring method for dust concentration proposed. Simulation and prediction are based on the operating data of a power plant 600 MW unit. The simulation results show that the prediction accuracy of the two models is both more than 96%, and the prediction error of BP model is less than 4%, while the error of SVM model is even less than 2.5%. On the whole, these two models are ideal for dust concentration monitoring, but the accuracy of the SVM model is relatively higher, and it has higher generalization ability, and is more stable. Therefore, it can be a kind of effective method for on-line monitoring.

Intercross real-time control strategy in a novel phased isolation tank step feed process for treating low C/N real wastewater under ambient temperature

The paper presents a study correlating the profile of on-line monitoring parameters and off-line measurement of nutrient concentrations (NH4 +-N, TN, NO3 −-N and TP) in a novel technology phased isolation tank step feed (PITSF) system, thereby utilizing the parameters as economical operational tool. A fixed-time control study showed that real control points (RCPs) on pH and oxidation-reduction potential (ORP) profiles accurately coincided with the major biological activities, whereas both DO and pH profiles are applied to control the end of nitrification and phosphate uptake process in aerobic phases, in particular, the “ammonia valley” on the pH profile for nitrification process. The analysis of the first aerobic tanks during the main phases showed that the RCP on DO profile can be used to distinguish the occurrence of simultaneous denitrification and nitrification (SND). Both the RCPs on the ORP profile and “nitrate apex” on the pH profile are applied to control the end of denitrification. The relationship between ORP and phosphorus release is linear and both (dORP/dt) and (dpH/dt) values are applied to control the end of anaerobic phosphorus release. Therefore, a new, reliable and effective real-time control strategy was developed using DO, pH and ORP as control parameters, to improve the performance of the PITSF process. As a final engineering observation, the proposed system is regarded as an effective process because the PITSF system can be operated safely under intercross real time control to treat low C/N domestic wastewater. The total nutrient removal efficiencies of COD, TN, NH4 +-N, and TP with low C/N ratio and normal water temperature of (17–21) °C were 91.7, 83.2, 95.7 and 92.1%, respectively, which was approximately equal to the complete nitrification-denitrification with the addition of external carbon sources under low water temperature of (8–11) °C.

Dynamic Performance Analysis and Simulation of a Full Scale Activated Sludge System Treating an Industrial Wastewater Using Artificial Neural Network

Due to changeable nature of the industrial wastewaters, proper operation of an industrial wastewater treatment plant is of prior importance in order to keep the process stability at the desired conditions. In this mean, simulation of the treatment system behavior using artificial neural network (ANN) can be an effective tool. This paper evaluates long term performance and process stability of a full-scale integrated industrial wastewater treatment system (Faraman’s industrial estate, Kermanshah) in removing organic matter over a 2-year operation. The wastewater treatment system is composed of static screens, an equalization tank, an aerobic biological tower (TF) and an activated sludge (AS) reactor. Multilayer Feed-forward Networks of ANN was used to forecast the process performance of AS system. In this study, mixed liquor suspended solids (MLSS) (mg/l) and organic loading rate (OLR) (kg COD/m 3 .d) were selected as input parameters and TSS removal, COD removal and sludge volume index (SVI) as output parameters. The results showed a very good agreement between the actual and modeled data (R 2 > 0.9). The ANN models provided a robust tool for predicting the performance of wastewater treatment plants and as a result, the online monitoring parameters could be applied for prediction of effluent characteristics.

Vehicle Braking Efficiency On-Line Monitoring and Evaluation with MFDD

According to the vehicle braking efficiency road test rules in GB7258, the braking efficiency on-line monitoring index, parameter, method and threshold are studied. Mean fully developed deceleration (MFDD) is selected as the evaluation index. Pitch angle which is used to monitor the road slope(less than 1%) is collected by gyroscope. Vehicle maximum deceleration is collected at the period of ABS working, which is to make sure that the road adhesion coefficient is more than 0.7. Vehicle maximum braking deceleration is analyzed, and it is used to exclude driving on pit, decelerating band, road step, even crash and so on. The rapid stop is identified with brake pedal travel senor. MFDD is calculated with braking deceleration and wheel speed, and wheel speed is collected from ABS with can bus. MFDD threshold is determined with passenger capacity monitoring with card reader. Finally, the method of vehicle braking efficiency on-line monitoring and evaluation with MFDD is given.

A fuzzy neural network model for monitoring A2/O process using on-line monitoring parameters

An adaptive network based fuzzy inference system (ANFIS) model was employed to predict effluent chemical oxygen demand (CODeff) and ammonia nitrogen (NH4 + eff) from an anaerobic/anoxic/oxic (A2/O) process, and meanwhile a self-adapted fuzzy c-means clustering algorithm was used to identify the model's architecture and optimize fuzzy rules. When constructing the model or predicting, the on-line monitoring parameters, namely hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO) and mixed-liquid return ratio (r), were adopted as the input variables. Compared with the artificial neural network (ANN) model whose weight vector was optimized by a real-code genetic algorithm (GA), the ANFIS presented better estimate performance. When predicting, the mean absolute percentage errors (MAPEs) of 1.8458% and 2.8984% for CODeff and NH4 + eff could be achieved using ANFIS; the root mean square errors (RMSEs) for CODeff and NH4 + eff were 1.6317 and 0.1291, respectively; the correlation coefficient (R) values of 0.9928 and 0.9951 for CODeff and NH4 + eff could also be achieved. The results indicated that reasonable monitoring A2/O process performance, just using on-line monitoring parameters, has been achieved through the ANFIS.

Real-time demonstration of 128-QAM-encoded optical OFDM transmission with a 525bit/s/Hz spectral efficiency in simple IMDD systems utilizing directly modulated DFB lasers

The feasibility of implementing 128-QAM in off-the-shelf component-based real-time optical OFDM (OOFDM) transceivers incorporating advanced channel estimation, on-line performance monitoring and live parameter optimisation, is experimentally investigated, for the first time, in intensity-modulation and direct-detection (IMDD) single-mode fibre (SMF) and multi-mode fibre (MMF) transmission systems involving directly modulated DFB lasers. The highest ever spectral efficiency of 5.25bit/s/Hz is demonstrated successfully in the aforementioned simple systems. Experimental investigations show that, it is feasible to transmit 5.25 Gb/s 128-QAM-encoded OOFDM real-time signals over 25 km MetroCor(TM) SMFs and 500 m 62.5/125 microm OM1 MMFs. The impact of key parameters on the transmission performance of the real-time OOFDM transceivers with 128-QAM encoding are explored, based on which optimum signal clipping ratios are identified.

Complementary parameter for dialysis monitoring based on UV absorbance

An optical on-line monitoring system aimed at the estimation of dialysis dose has been tested clinically. The natural logarithmic slope is used to calculate Kt/V(urea) from ultraviolet (UV)-absorbance measurements. Errors in the calculation of Kt/V(urea) may appear due to changes in blood and dialysate flow or due to disturbances when the slope is used to estimate dialysis dose. This study introduces a new parameter for dialysis monitoring that may be used as a complementary parameter, the area under UV-absorbance curve (AUCa), to reflect a total solute removal during dialysis. The aim was to investigate the relationship between this new dialysis on-line monitoring parameter, AUCa, and the total removal of a few solutes. Fifteen patients were monitored during hemodialysis using UV absorbance at the wavelength of 297 nm. All spent dialysate passed through a flow cuvette in a spectrophotometer and then further to a collection tank where solute concentrations in the entire spent dialysate were determined. The AUCa at 297 nm was compared with the total amount of removed solute in the tank (reference method). The result shows strong correlations between AUCa and the total removal of urea, urate, creatinine, and phosphate during a given treatment and less strong correlation in all 15 patients together. A first indication of a new, possible, complementary parameter in hemodialysis treatment is presented, the AUCa, prospected to estimate solute removal.

Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach

Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SS(eff)), chemical oxygen demand (COD(eff)), and pH(eff) in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% for SS(eff), COD(eff) and pH(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SS(eff), COD(eff), and pH(eff) were 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SS(eff), COD(eff), and pH(eff) could also be achieved.