On-line Monitoring Method Research Articles

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

Robotic MAG welding defects and quality assessment with a defect threshold decision model-driven method

The use of machine vision and deep learning methods for online monitoring and evaluation of welding quality during the welding process and timely detection and correction of welding defects is essential for intelligent welding manufacturing. In this paper, with the goal of online monitoring of the welding quality of the robotic arc welding for the excavator boom, we established a multi-source sensing system for robotic arc welding processes. We have developed various feature extraction algorithms for extracting information about welding pools, arc sounds, currents, voltages, and other features. We proposed to extract welding pool image features using Res2-MobileNetV3 to obtain a 12-dimensional welding pool feature, which enabled good inter-class discrimination among different welding defects. We designed a welding defect threshold decision (DD) strategy model using the responsive machine learning and lightweight network Res2-MobileNetV3 method, achieving a welding defect recognition accuracy of 96.59%. Finally, we tested the accuracy and reliability of the welding defect recognition model on an actual welding scene for the robotic welding of the excavator boom. It provided valuable scientific methods and technological approaches for intelligent welding in complex scenes.

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

ABSTRACT Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar and bacterial concentration in real-time, ensuring kombucha quality during production. The feature fingerprints of residual sugar and bacterial concentration were extracted by four variable selection algorithms and then reconstructed using serial and parallel processing methods. Based on these reconstructed features, Partial Least Squares (PLS) and Convolutional Neural Networks (1DCNN and 2DCNN) models were developed and compared. The experimental results showed that the 2DCNN model based on reconstruction features achieved superior performance. The RPDs of the residual sugar and bacterial concentrations models were 4.49 and 6.88, while the MAEs were 0.42 mg/mL and 0.04 (Abs), respectively. These results suggest that the proposed modeling strategy effectively supports quality control during kombucha production and provides a new perspective for spectral analysis.

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.

A method for assessing the operational status of a microseismic monitoring system using energy distribution changes of observed data

Abstract The operational status of geophones plays a pivotal role in ensuring the accuracy and reliability of microseismic monitoring systems. However, conventional techniques used to evaluate the operational status of geophones require human intervention or significant time delays. To address this issue, we propose a method for online monitoring of geophone status using observed data obtained from a microseismic system. Firstly, the energy features of the preprocessed observation data are extracted via wavelet packet decomposition. Subsequently, the distribution parameters of energy features are obtained through loglogistic distribution fitting. These parameters are then applied to a change-point detection model, enabling the online monitoring of seismic geophones. In addition, we selected long short-term memory (LSTM) network to classify the operational status of the geophones, which is trained using the obtained energy distribution data and the time-frequency characteristics of the observed data. The experimental results indicate that the model achieves an accuracy of 98.33%, surpassing the 89.58% accuracy of the support vector machine (SVM). The proposed method not only contributes to online monitoring and precise determination of the operating status of detectors, but also has enormous application potential in other fields that require monitoring and evaluating the operating status of instruments.

Research on Electric Energy Metering Anomaly Detection and Classification Algorithm Under Multi-Source Data Fusion

The renewable energy grid is a distributed power grid, which needs to be parallel to the thermal power grid to ensure the stability of its own power generation. Therefore, renewable energy has the problem of multiple sources of power generation data and complex electric energy metering, which has always limited the development of renewable energy. In order to improve the grid-connected power supply quality of renewable energy and improve the accuracy of electric energy metering, this paper proposes a binary classification algorithm to collect, amplify and standardize multi-source data. Then, the Fourier function is used to normalize the multi-source data to shorten the differences between different devices. In particular, the attribute features are classified and the input data adjustment parameters are set. Finally, renewable energy's electric energy data value is measured, and the abnormal data characteristics are obtained through the second-order derivative to realize data mining and electric energy metering anomaly detection. The simulation results show that the binary classification can complete the metering and anomaly detection of electric energy in the distributed power grid, and the classification processing can simplify the complexity of multi-source data, improve the accuracy of metering anomaly detection, and make its dispersion more reasonable, and the results are between 90~95%, which is better than the online monitoring method. The time for anomaly result detection is 25s, which is shorter than the previous algorithm, and cross-domain data search is realized, with a cross-domain rate of 50~60%. Therefore, the classification algorithm proposed in this paper can meet the requirements of electric energy metering under multi-source data fusion and achieve rapid anomaly detection.

Online welding status monitoring method of T-joint double-sided double arc welding based on multi-source information fusion

In modern manufacturing, double-sided double arc (DSDA) welding brings advantages in properties and efficiency for T-shaped joints. However, the double-side forming makes it difficult to detect the imperfections and ensure weld joint quality, which few studies have covered. Most existing methods deal with single-arc welding with limited sensing sources. To fill the gap, an online welding status monitoring method for DSDA welding is proposed based on the fusion of welding current, arc voltage, arc sound, and weld pool images. In pre-processing, the automatic weld pool region of interest (ROI) detection method based on the lightweight YOLO-L model and the waveform denoising algorithm are designed. In feature engineering, waveform signals are analyzed in both the time and frequency domain. A weld pool feature extractor based on a convolutional neural network (CNN) is proposed with good effectiveness and interpretability. The output feature combination is refined by Fisher-based selection and evaluation. In model building, an ensemble learning model based on three high-fit basic learners is proposed, with an accuracy of 98.538 %. The proposed model shows significant advantages over the single basic classifier and other ensemble methods. Experiments verify high precision and robustness, laying a foundation for accurate real-time monitoring of DSDA welding production.

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.

Thermal transfer analysis of machines used for the treatment of textile materials

The present paper aims to introduce a comprehensive thermal analysis of the sizing-finishing machine (Squeezing Pader Machine) to identify the overheating areas to organise maintenance activities. As there is no universal application for checking the condition of a machine, specific methods must be used depending on the characteristics of the machines being monitored, as machine condition monitoring is very important in terms of productivity, economic benefits and maintenance. The thermal analysis of the equipment in this work paper, has an important role (for planning maintenance activities), because this machine ensures optimal loading with different material treatment solutions, intended for mattresses, according to customer requirements. In the present paper, the authors conducted research on the whole technological flow to plan predictive maintenance activities using online monitoring methods. The mathematical modelling of the data obtained from the thermal analysis of the engines carried out based on the polynomial regression is used to predict the evolution of the temperatures along the analysed lines outside the measurement interval for each engine analysed. In this sense, the authors propose online monitoring of the motors that have the role of stretching and transporting the material, which is not included in the real-time temperature monitoring in the machine software, as the rest of the motors are monitored. Regular monitoring of motors with the help of thermography can help identify potential problems before they become serious, allowing preventive maintenance to improve the equipment’s reliability and efficiency.

Optimization of air distribution and coal blending in pulverized coal boilers for high-temperature corrosion prevention based on POD reduced-order modeling

High-temperature corrosion in coal-fired boilers poses a significant threat to safe operation. However, there is currently a lack of effective online monitoring and optimization methods for high-temperature corrosion. Therefore, this study proposes a novel approach to rapidly predict the distribution of chemical species and evaluate high-temperature corrosion degrees, along with optimizing operating conditions to prevent high-temperature corrosion. Firstly, a total of 564 Computational Fluid Dynamics (CFD) simulations are performed on a 330 MW tangentially fired boiler, covering various operating conditions, including coal blending, air distribution, boiler load, etc., to obtain a database of O2, CO, and H2S distributions within the boiler. Then a method based on Proper Orthogonal Decomposition (POD) and Support Vector Regression (SVR) is used to process the database to realize real-time prediction of boiler chemical species distribution, which is next utilized as inputs of a high-temperature corrosion evaluation to obtain in-situ corrosion degree distribution. Finally, Particle Swarm Optimization (PSO) is used to optimize coal blending and air distribution schemes, effectively reducing severe corrosion ratio from 36.34 % to 10.04 % in a typical case by improving the atmosphere near the water walls. This study thus provides a new perspective for online boiler diagnostics and digital twin construction, particularly by achieving online monitoring of high-temperature corrosion and optimizing operating conditions to prevent corrosion.

On-line error monitoring of capacitive voltage transformer based on substation topology and distribution analysis

Abstract The capacitor voltage transformer (CVT) is widely used in power systems with high voltage levels. The operation state of CVT is related to the accuracy of electric energy metering. Because the internal structure of CVT is complex and vulnerable to the external environment, the error may be out of tolerance in the actual operation process. The error is composed of system error and random error. The change of error is mainly the change of system error. In general, random errors satisfy specific distributions and will not change easily. In this paper, the voltage amplitude and phase effective value output by the CVT acquisition device at the same voltage level are used to compare the amplitude effective value of the in-phase output to obtain the ratio characteristic quantity K, and the phase effective value is subtracted to obtain the difference characteristic quantity θK . K and θK contain the information of two CVT ratio differences and angle differences. The probability density distribution obeyed by K and θK during the normal operation of CVT is taken as the reference probability density distribution and compared with the probability density distribution to be measured during online monitoring. If the probability density distribution changes significantly, it shows that the ratio difference and angle difference have exceeded the difference under the premise of this paper. Finally, the contribution method is used to locate the out-of-tolerance CVT. In this paper, the online monitoring method is verified by simulation data. The test results show that the ±0.1% range ratio difference and the ±5′ range angle difference can be identified, and the online monitoring of the 0.1-stage CVT error is realized.

Research on online monitoring method for time-varying tension in transmission lines based on operational modal response

The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.

European effort to improve highly charged heavy ion beam capabilities with ECR ion sources (invited)

The European Electron Cyclotron Resonance Ion Source (ECRIS) community has more than 20 years of experience working together in various EU-funded projects. In the recent project, called ERIBS (European Research Infrastructure – Beam Services), the community will focus on improving ion beam services for the EURO-LABS (European-Laboratories for Accelerator Based Sciences) research infrastructures. The EURO-LABS is a four-year project funded by the Horizon Europe program of the European Commission for years 2022 - 2026. In the ERIBS collaboration the best expertise, know-how and practices of the ECRIS community will be exploited and transferred between the partners to take full advantage of the European ion source infrastructure. The aim is to extend the beam variety available for the European user community by developing beam production methods and techniques. This development includes further improvement of technologies related to high temperature ovens, axial sputtering and MIVOC method for all the participating laboratories. We will also aim to improve both short- and long-term plasma and beam stability, as well as methods for online monitoring of these conditions. This can be realized, for example, by optical emission spectroscopy, identifying kinetic plasma instabilities by means of hard x-ray detection and using online beam current monitoring systems. An example of the recent developments is the new collaboration proposed by the CNRS-IPHC team to synthesize enriched MIVOC compounds for the other ERIBS partners. For example, the team successfully prepared an enriched chromocene compounds, which were needed to produce intensive 54Cr and 50Cr beams for the JYFL and GANIL nuclear physics programs, respectively.

A novel bilateral stream neural network for melt pool monitoring during laser direct energy deposition

Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Digital twin-driven blade rub-impact diagnosis using blade tip timing

Blade Tip Timing (BTT) has become a promising method for online monitoring of rotating blades due to its non-contact measurement characteristics. However, due to the undersampling and non-uniform characteristics of blade tip timing signals, there are few studies on using them to diagnose rub-impact fault. This paper focuses on this topic and proposes a rub-impact diagnosis process. The contents of this paper are as follows: (1) A review of the existing rub-impact diagnosis technology and signal processing methods, then the advantages of diagnosis rub-impact using BTT are given. (2) The digital twin model framework for rotating blades has been established, including physical entity, digital entity, and the connections between them. (3) Dynamics analysis of both single blades and entire blade disks under rub-impact states has been carried out for the digital twin model, and six signal characteristics of blade tip under rubbing condition are summarized which provide prior knowledge for subsequent diagnosis. (4) Aiming at that traditional Enhanced Sparse Decomposition (ESD) method is prone to falling into local optimal solutions for BTT undersampled data, an envelope spectrum kurtosis enhanced sparse decomposition (ESKESD) method is proposed. (5) A complete rub-impact fault diagnosis process using digital twin is proposed. Finally, the effectiveness of the proposed method and diagnosis process are verified through simulation and experimental data.

Research on Online Condition monitoring for Complex System based on Modified Broad Learning Systems

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. General BLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data.

Defect Identification of XLPE Power Cable Using Harmonic Visualized Characteristics of Grounding Current

This paper proposes an online monitoring and defect identification method for XLPE power cables using harmonic visualization of grounding currents. Four typical defects, including thermal aging, water ingress and dampness, insulation scratch, and excessive bending, were experimentally conducted. The AC grounding currents of the cable specimens with different defects were measured during operation. By using the chaotic synchronization system, the harmonic distortion was transformed into a 2D scatter diagram with distinctive characteristics. The relationship between the defect type and the diagram features was obtained. A YOLOv5 (you only look once v5) target recognition model was then established based on the dynamic harmonics scatter diagrams for cable defect classification and identification. The results indicated that the overall shape, distribution range, density degree, and typical lines formed by scatter aggregation can reflect the defect type effectively. The proposed method greatly reduces the difficulty of data analysis and enables rapid defect identification of XLPE power cables, which is useful for improving the reliability of the power system.

ADMM-1DNet: Online Monitoring Method for Outdoor Mechanical Equipment Part Signals Based on Deep Learning and Compressed Sensing

To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks.