On-line Monitoring Applications Research Articles

Insights into Tetravalent Np Speciation in HNO3 through Spectroelectrochemistry and Multivariate Analysis.

In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO3 concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO3 and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO3 concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO3 concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO3 concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO3 concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.

Measurement Techniques for Low-Concentration Tritium Radiation in Water: Review and Prospects.

Tritium (3H) is one of the most critical nuclides for environmental monitoring, yet it is challenging to measure. Its high natural mobility and its potential to enter the human body through the food chain underscore the importance of not overlooking the radiation safety risks associated with tritium. The need for the online measurement of tritium at low concentrations is becoming increasingly apparent. This review examines the two principal stages of current measurement methodologies: sample preparation and radiation signal detection. It provides a summary of the tritium sample preparation and detection techniques, highlighting advances in the research with potential applications in online monitoring. The review concludes with an analysis of the issues inherent in the current techniques and offers perspectives on possible technological enhancements and future trajectories for the development of online monitoring systems for trace tritium levels.

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Design of On-line Monitoring and Fault Diagnosis Platform for Oil-immersed Shunt Reactor

Abstract With the development of society and technology, the demand for power supply and reliability is increasing. In the power system, the reactor plays an important role as a key equipment. However, due to long-term operation, the reactor is affected by factors such as loss, aging and hidden faults, and its reliability gradually decreases. The operation state of the reactor is directly related to the operation effect of the power grid and the quality of the user’s electricity. Therefore, the application of reactor online monitoring and fault diagnosis in real life is very important. This paper considers the actual operation of the coastal power grid. Based on Qt software development platform, C + + language and MySQL database, an online monitoring and fault diagnosis platform for oil-immersed shunt reactors is developed. The application of the platform takes the online monitoring and fault diagnosis method of dissolved gas in oil as the core. The platform realizes the basic information management, online monitoring, fault diagnosis and suggestions for operation and maintenance of the reactor.

Real-Time Monitoring of Cable Sag and Overhead Power Line Parameters Based on a Distributed Sensor Network and Implementation in a Web Server and IoT.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly, and in return, the calculation by the alternative method, which uses another group of sensors, generates both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Also, the global accuracy of the entire OPL cable sag calculus system is estimated at 0.1%. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application using Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching.

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

Online monitoring of the respiration activity in 96-deep-well microtiter plate Chinese hamster ovary cultures streamlines kill curve experiments.

Cell line generation of mammalian cells is a time-consuming and labor-intensive process, especially because of challenges in clone selection after transfection. Antibiotics are common selection agents for mammalian cells due to their simplicity of use. However, the optimal antibiotic concentration must be determined with a kill curve experiment before clone selection starts. The traditional kill curve experiments are resource-intensive and time-consuming due to necessary sampling and offline analysis effort. This study, thus, explores the potential of online monitoring the oxygen transfer rate (OTR), as a non-invasive and efficient alternative for kill curve experiments. The OTR is monitored using the Transfer-rate Online Measurement (TOM) system and the micro(μ)-scale Transfer-rate Online Measurement (μTOM) device, which was used for mammalian cells first. It could be shown that the OTR curves for both devices align perfectly, affirming consistent cultivation conditions. The μTOM device proves effective in performing kill curve experiments in 96-deep-well plates without the need for sampling and offline analysis. The streamlined approach reduces medium consumption by 95%, offering a cost-effective and time-efficient solution for kill curve experiments. The study validates the generalizability of the method by applying it to two different CHO cell lines (CHO-K1 and sciCHO) with two antibiotics (puromycin and hygromycin B) each. In conclusion, the broad application of OTR online monitoring for CHO cell cultures in 96-deep-well plates is highlighted. The μTOM device proves as a valuable tool for high-throughput experiments, paving the way for diverse applications, such as media and clone screening, cytotoxicity tests, and scale-up experiments.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

The Attitudes of Patients with Cardiovascular Diseases towards Online Exercise with the Mobile Monitoring of Their Health-Related Vital Signs.

The health care cost of cardiovascular diseases (CVD) in the EU is estimated to be today over 282 billion euros. It is well documented today that exercise training is one of the main strategies for secondary disease prevention and the follow-up integration of these patients. This study aimed to examine patients' attitudes towards online exercise with mobile monitoring of their vital signs. More specifically, the research objectives were as follows: (a) to examine patients' attitudes and expectations of online exercise, (b) cluster patients in high- and low-attitude groups and examine their intention to participate in online exercise, and (c) to examine age and gender differences in terms of their intention to exercise online. The final goal of this project was to develop a real application that could be of use to patients and professionals. Data were collected from fifty patients in the city of Thessaloniki, Greece. The results revealed that most patients were positive about exercising online if the programs were perceived as fun and, especially, safe. The use of an online monitoring application with the distant supervision of health professionals could both motivate them and strengthen their feeling of safety.

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Study on ecological environment impact and soil and water conservation technology of power transmission and transformation projects in typical ecologically fragile areas in western China

In the steady development of social and economic, the field of power transmission and transformation engineering construction in our country gradually forms a new electric power development pattern, providing a solid foundation for the innovative development of electric power enterprises in a new era. Since power transmission and transformation projects will cause serious soil and water loss, which will have a direct impact on the stability of ecological environment in various regions, which is not conducive to the construction and operation of power transmission and transformation projects and the long-term development of social economy, how to construct a safe and standardized construction environment, scientifically deal with the ecological environment problems of power transmission and transformation projects, and rationally use soil and water conservation technology? It is the main problem that Chinese scholars study and discuss at present. On the basis of understanding the environmental status of typical ecologically fragile areas in western China, and according to the development of soil and water conservation technology in power transmission and transformation projects, this paper mainly studies the online monitoring system and application technology of soil and water conservation for power transmission and transformation projects in a certain area, so as to provide effective basis for the construction management of power transmission and transformation projects in the new era.

A physics-informed CNN-TSE hybrid network for micro-EDM process monitoring and control

Despite being a widespread non-conventional processing technique, electrical discharge machining (EDM) has been complicated due to its removal mechanisms and weak process stability for decades. Process monitoring is an enabling technology to offer an indirect insight into the discharge phenomena, understanding the probabilistic anomaly events such as arcs and short circuits. However, the transient high-frequency pulse train poses a great challenge for statistical monitoring and control. Traditional approaches are limited to single pulse analysis, which hinders a deep understanding of the process. To address the challenge, this paper proposes a novel physics-informed deep learning architecture for real-time micro-EDM process monitoring. To prepare condition labels, a series of signal-processing techniques including sensory fusion, pulse-shape filtering and pulse segmentation are employed, followed by observation diagrams on two critical indicators, i.e. discharge energy and discharge time interval. A decision logic that allows embedding of domain knowledge is formulated, yielding physics-informed labels on each pulse train. For online monitoring applications, a hybrid hierarchical structure that combines a 1D convolutional neural network (1D-CNN) and a Transformer encoder (TSE) is designed to simultaneously extract local pulse features and temporal dependencies between pulse sequences. This deep learning model achieves a classification accuracy of 96% on the validation dataset and reaches precision and recall scores of more than 92% on the test dataset with varying processing parameters. Two model interpretation approaches, i.e. gradient-weighted class activation mapping (Grad-CAM) and attention visualization, are adopted. Consequently, it is found that the CNN module assigns high feature importance to the breakdown and maintaining stages of one pulse and the TSE module improves the feature salience and reliance on the condition of distinct events. This monitoring network is further demonstrated on a control application for adaptive regulation of the micro-EDM drilling process. The results of deep-hole drilling suggest that the CNN-TSE model can highly improve the process stability and hence the machining quality.

Flexible Sensors with a Sandwich Stack Structure of Few-Layer Ti3C2Fx/Polypyrrole Nanowires for Human Motion Detection and Healthcare Monitoring

Flexible wearable sensors with high sensitivity, broad sensing range, and low detection accuracy have great potential applications in human motion online monitoring, human healthcare detection, and human–machine interface. However, high sensitivity and broad sensing range conflict, as the former requires conspicuous structural changes under microstrain while the latter requires complete morphology under large deformation. Herein, we report few-layer 2D Ti3C2Fx and 1D polypyrrole (PPy) nanowires with a sandwich stack structure transfer on polyacrylic acid (PAA) tape based on layer-by-layer (LBL) assemble technology. The 1D PPy nanowires enhance the strain sensor’s interfacial adhesion and electron transmission channel, resulting in high sensitivity and a permissible detection range (0–50%). Under various applied force stimuli, the synergetic effect of Ti3C2Fx nanosheets (4.72 nm thickness) and PPy nanowires (diameter 20–50 nm) endows the stack structure with good electrical–mechanical performance, which is reflected by the sensor’s high sensitivity (GF = 2950, ε = 50%; GF = 475, ε = 10%; GF = 22, ε = 0.1%). Such a flexible sensor with a permissible detection range (up to 50%), a low detection limit (0.1%), and reliable repeatability (>1500 cycles) has potential applications for human motion detection, clinical diagnosis, and healthcare monitoring.

Acoustic emission identification of wheel wear states in engineering ceramic grinding based on parameter-adaptive VMD

In engineering ceramic grinding, the wheel wear states have an important influence on the processing efficiency and grinding quality. To address the difficulty of online monitoring of wheel wear states, a signal reconstruction method based on parameter-adaptive variational modal decomposition was proposed. Experiments on the entire life cycle of the grinding wheel were performed, and the wear states were classified based on the observed surface morphologies of the wheel and workpiece. Subsequently, acoustic emission signals were collected by nodes, and high correlation frequency bands were chosen and reconstructed. The signal features were extracted, and the wear states were identified based on a random forest optimization feature combination, finally. In this study, taking alumina ceramic as an example, eight groups of single-factor experiments were designed based on cumulative grinding depth to determine the optimal linear speed of the grinding wheel, worktable moving rate, and other process parameters. A total of 160 groups of AE signal samples were obtained. The results show that feature extraction by frequency bands can better reflect the changes in the grinding wheel wear states than the overall signal. In addition, optimization feature combinations based on the bands after decomposition, filtering, and reconstruction can divide and identify the various wear states more effectively. The comprehensive identification accuracy reached 90.6%. The accuracy of identifying the severe wear state reached 100%. Thus, the proposed method provides theoretical guidance for the practical industrial applications of online monitoring of wheel wear states.

Improvement Possibilities for Nuclear Power Plants Inspections by Adding Deep Learning-based Assistance Algorithms Into a Classic Ultrasound NDE Acquisition and Analysis Software

The safety of nuclear power plants has always been one of the most important security issues in the industry in general. Numerous standards, techniques, and tools have been developed to deal specifically with the safety of nuclear power plants – one has specialised probes, robotized systems, electronics, and software. Although seen as a mature (or slowly evolving) industry, this notion about nuclear safety is a bit misleading – the area is developing in many promising new directions. Some recent global events will speed up this development even more. On the other hand, the industry is currently going through digital transformation, which brings networking of devices, equipment, computers, and humans. This fourth industrial revolution promises speed, reliability, and efficiencies not possible up until now. In the NDE sector, new production techniques and traditional manufacturing lines are getting to be lights-out operations (near-total automation). The same is most probably going to happen with the safety inspections and quality insurance. Robotics and automation are improving worker safety and reducing human error. The well-being of inspectors working in a hazardous environment is being taken care of. Most experts agree that the digitalization of NDE offers unprecedented opportunities to the world of inspection for infrastructure safety, inspector well-being, and even product design improvements. While the community tends to agree on the value proposition of digital transformation of NDE, it also recognizes the challenges associated with such a major shift in a well-established and regulated sector. The work presented in this paper shows a part of the project that aims to develop a modular ultrasound diagnostic NDE system (consisting of exchangeable transducers, electronics, and acquisition/analysis software algorithms), for applications in hazardous environments within nuclear power plants. The paper will show how the software part of this system can reach near-total automation by implementing various deep learning algorithms as its features and, then, testing those algorithms on laboratory samples, showing encouraging results and promises of online monitoring applications. Furthermore, future general prospects of this technology are discussed, and how this technology can affect the well-being of nuclear power plant inspectors and contribute to overall plant safety.

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

• A closed-loop control approach is successfully developed for online detection of defects and adjustment of process parameters by deep learning, which is never achievable in any conventional methods of composite fabrication. • Deep learning is implemented to detect the CFRP defects for AM in real-time with high accuracy and low latency. The proposed method is able to identify different types of CFRP defects (i.e., misalignment and abrasion). • The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.

Failure forecast method applied on burst test data to valuate its practicability for acoustic emission testing and monitoring purposes

This paper describes the performance of a laboratory burst test on a test object with a severe real-world crack, the interpretation and evaluation of the acoustic emission (AE) data in general and in particular the results of further AE data trend analysis utilising the failure forecast method at different sections in testing time. The objective of this exercise is to gain experience in application of this method and to check whether or not it could be employed for testing and/or monitoring purposes. The burst pressure prediction as well as the ratio between the actual test pressure and the burst pressure prediction, the failure convergence, are consistent with the usual AE data evaluation throughout the test. Test sequences with a strong progressive increase of activity trend in AE data are highlighted by a gradually increasing failure convergence. This method has the potential to complement state-of-the-art evaluation techniques for testing applications, in particular with activity trend analysis. Due to the good results obtained already with a rather simple approach, remaining useful life prediction for on-line monitoring applications seems to be within reach.

Pressure vessel leakage detection method based on online acoustic emission signals

Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired. To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.

Tagging and tracking oil-gas mixtures in multiphase pipelines

Pipeline transportation of multiphase products, such as gas and oil mixtures, exhibits complex and varying flow regimes: as a result, analytical approaches or conventional methods cannot accurately describe the composition or the propagation characteristics of the fluid mix inside the transportation system itself. We address such an issue by presenting a methodology, driven by the data and applied to a real case history, where basic pressure transients are used to tag and track, along a pipeline, different batches of a multiphase medium. Several statistical indicators, computed from the pressure data and on different window lengths, are employed to train a machine learning model, which learns to distinguish the characteristic behavior of two different oil-gas slugs: in practice, each different combination of fluid phases (in terms of gas/oil ratio in a given batch of product) and each different sequence of slugs (in terms of gas/oil ratio variability between successive batches) behaves like a coded tag linked to the flowing fluid. The key innovation consists in the possibility of tracking such multiphase slugs along the flowline and at each monitoring station: this allows one to determine in real-time the fluid composition entering/exiting the line, its position, and its movement along the pipe. As such, we obtain also a virtual metering system, able to provide estimates of the flow rate and phases ratio. Moreover, by having several recording stations accurately synchronized, one can also leverage real-time transmission and multichannel processing of the data, enabling the opportunity for online monitoring applications. The results on the test cases and the accuracy scores obtained for the metrics considered validate the tagging and tracking approach.

A 60 GHz pulsed coherent radar for online monitoring of the withering condition of leaves

This paper introduces a novel sensor application for online monitoring of the withering condition of leaves. The proposed system is based on the millimeter-wave radar sensitivity and its signal variations that correlate with the water content in a leaf. To validate the experimental prototype, a 60 GHz pulsed coherent radar (PCR) coupled with a dielectric lens was configured. The data obtained by means of the radar sensor is correlated with the data of the conventional gravimetric using a modelling approach based on Beer-Lambert’s law. To the best of our knowledge this is the first proof-of-concept demonstrating the relation between the received signal amplitude from the millimeter-wave radar sensor and plant leaf mass loss.