On-line Monitoring Method Research Articles

Online monitoring of a high-power CW laser optical mirror based on infrared and visible image features visualization

In high-power CW (continuous wave) laser systems, laser damage to optical mirrors often occurs, and it is urgent to develop timely and effective monitoring technologies to avoid laser damage or emergency stop at the early damage stage. In this paper, an online monitoring method based on the combination of infrared and visible images is proposed. The proposed method is based on the feature recognition of infrared and visible images. After setting an appropriate threshold, a decision model can be employed to enable real-time monitoring and health status evaluation of optical mirrors. Here, the thresholds for abnormal and dangerous temperatures were established at 100°C and 200°C, respectively. The results show that the temperature of the optical mirror would significantly increase after damage, with the surface temperature of the element reaching up to 250°C. At the same time, the damaged area could be captured by a visible camera and the changes in gray values were displayed in the visible image. Online monitoring of the health status of optical elements can be achieved by assessing changes in infrared image temperature, visible image spot position, and area. This monitoring method serves as an early warning method for potential optical elements damage.

Design of Online Monitoring Method for Distribution IoT Devices Based on DBSCAN Optimization Algorithm

Design of Online Monitoring Method for Distribution IoT Devices Based on DBSCAN Optimization Algorithm

An Online Monitoring Method of IGBT Junction Temperature based on Turn off Miller Plateau Current

An Online Monitoring Method of IGBT Junction Temperature based on Turn off Miller Plateau Current

A digital-twin-based online monitoring method for aging of thermal interface material of power converter

A digital-twin-based online monitoring method for aging of thermal interface material of power converter

Enhancing transformer windings monitoring: An approach using longitudinal branch‐circuit conductance analysis

AbstractThe operating environment of transformers is getting more complex with the emergence of new energy sources and power electronic devices. This complexity can cause minor internal faults in transformer windings. Under the cumulative effect, minor faults gradually develop into serious faults, resulting in transformer damage. Conventional differential protection systems may have difficulty detecting these glitches and require avoiding the problem of protection false activation caused by inrush currents. This paper proposes a new online monitoring method for transformer windings based on longitudinal branch‐circuit conductance to address this issue. First, a unified transformer equivalent circuit is proposed to represent transformers under normal conditions, inrush currents, and internal faults. Then, an online transformer monitoring method based on branch conductance is proposed, which is immune to inrush currents. This method aims to prevent delayed detection of faults during inrush currents, improving sensitivity and response speed, especially for minor turn‐to‐turn faults hidden in inrush currents. The proposed method also provides higher sensitivity to minor turn‐to‐turn faults and larger protection margins. Simulation and experimental results validate the effectiveness of this method.

Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.

An Online Monitoring Method for DC Busbar Electrolytic Capacitor Bank Based on Optimized Rogowski Coil Current Sampling

An Online Monitoring Method for DC Busbar Electrolytic Capacitor Bank Based on Optimized Rogowski Coil Current Sampling

An Online Monitoring Method for Capacitor Condition of Cascaded H-Bridge STATCOM Based on Sensorless Capacitor Voltage Detection

An Online Monitoring Method for Capacitor Condition of Cascaded H-Bridge STATCOM Based on Sensorless Capacitor Voltage Detection

Study on On-line Monitoring method for Local Slagging in the Coal-Fired Boiler Furnace

Abstract Slag formation in coal-fired boiler furnaces is an unavoidable issue, which is further exacerbated by the combustion of Zhundong coal. The accumulation of slag in the furnace can reduce the heat exchange capacity of the boiler, leading to a decrease in power generation efficiency and operational economy. However, there is currently a dearth of monitoring techniques to evaluate the local slagging status in the furnace. To address this gap, this study proposes an innovative approach by installing multiple thermocouples on water wall tubes and fins to establish an online monitoring model for local slagging based on temperature differences. The effectiveness of this model has been validated on a 660 MWe utility boiler. The monitoring results reveal that the slagging in the furnace is uneven and asymmetrical in both the circumferential and longitudinal directions. The central region of the water wall in the circumferential direction is particularly prone to slagging, and the main combustion zone exhibits the highest slagging rate, while the burnout zone experiences the lowest. Moreover, the slagging rate in various regions is influenced by the unit load. It is directly proportional to the load in the main combustion zone, inversely proportional in the furnace bottom zone, and relatively unaffected by the load in the burnout zone.

An experimental study on laser cleaning of the soot deposition layer on a white marble surface

A coordinated application of the image binarization, area extrapolation, and laser-induced breakdown spectroscopy (LIBS) detection for monitoring and controlling the laser cleaning soot deposition layer process on a white marble surface was reported. The laser cleaning process used a fiber pulsed laser beam with a fixed wavelength of 1064 nm, a pulse repetition frequency of 100 kHz, and a focal spot diameter of 95 μm. Image binarization was conducted to determine 150 ns as the appropriate fixed pulse width for laser cleaning of the soot deposition layers from four discrete values of 50 ns, 100 ns, 150 ns, and 200 ns. The optimal laser energy density of 2.82 J·cm-2 was obtained through area extrapolation, while the optimal spot overlap rate of 20 % was determined to maximize the effective laser cleaning speed. The optimal number of cleanings for a soot deposition layer with a thickness of 77μm was determined to be 5 using LIBS detection and to monitor the laser cleaning process in real time for each small area of the soot deposition layer. Image binarization was used to evaluate the overall cleaning quality of large marble surfaces. By dynamically adjusting the optimal number of cleanings, the cleaning efficiency of the soot deposition layers on half of the surface area of a 20 cm diameter white marble artifact exceeded 95 %. The results indicate that the synergistic use of various online monitoring methods can optimize multiple laser parameters, enabling precise removal of the soot deposition layer on the white marble surfaces caused by anthropic factors, without damaging its surface.

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

Online monitoring method for inter-turn short circuit fault of dry-type hollow core reactor

Abstract A method for online monitoring of turn-to-turn short circuit faults based on power factor changes is proposed to solve the problem of small overall current changes and delayed overcurrent protection after the occurrence of turn-to-turn short circuit faults in dry-type hollow reactors. A circuit model of inter-turn short-circuit fault reactor was established, and the changes in electrical parameters of the fault reactor were numerically analyzed at different positions. On this basis, the power factor was determined as the monitored variable, and a test platform was built for experimental verification. The research results indicate that after a turn-to-turn short circuit fault, the total current changes very little, the power factor increases significantly, and the changes in various electrical parameters are related to the location of the short circuit. The experimental results are consistent with the theoretical analysis results, verifying the correctness and effectiveness of the monitoring method.

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.

Arbitrary field-of-view wavefront monitoring method based on an improved vision transformer network.

Space-based segmented telescopes are susceptible to mirror misalignments because of temperature and mechanical perturbations in orbit. Monitoring the misalignment status of each mirror is a prerequisite to aligning the telescope promptly and maintaining image quality. In this paper, an online monitoring method based on an improved vision transformer network is proposed. Two new modules have been added to the method: the grid-aligned patch-splitting (GAPS) module and the field linearization eliminator (FLE) module. The former modifies the patch-splitting method based on the characteristics of point spread function (PSF) to improve the accuracy. The latter is used to eliminate the effect of field position for arbitrary field-of-view wavefront monitoring. The method predicts the misalignment state of the telescope through a PSF (stellar image) for an arbitrary field of view, realizing monitoring of the telescope without hindering the observing process and increasing the complexity of the system. Simulations and experiments are performed to verify the effectiveness and practicality of the method.

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. First, the energy features of the preprocessed observation data are extracted via wavelet packet decomposition. Subsequently, the distribution parameters of energy features are obtained through log-logistic distribution fitting. These parameters are then applied to a change-point detection model, enabling the online monitoring of seismic geophones. In addition, we select a long short-term memory 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. 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.