Real-time Online Monitoring Research Articles

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 2.8 % in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

A new method for the rapid identification of external water types in rainwater pipeline networks using UV–Vis absorption spectroscopy

Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Change Characteristics of Carbonaceous Aerosol in Chengdu from 2018 to 2021

Carbonaceous aerosol is an important component of atmospheric fine particulates （PM2.5） that has an important effect on global climate change, atmospheric visibility, regional air quality, and human health. In order to investigate the long-term change characteristics of carbonaceous aerosols under the background of emission reduction, the concentrations of organic carbon （OC）, elemental carbon （EC） in PM2.5 samples, and volatile organic compounds （VOCs） in Chengdu from 2018 to 2021 and the corresponding meteorological factors were obtained through real-time online monitoring. The results showed that the average ρ（OC） and ρ（EC） during the monitoring period were （10.9 ±5.7） μg·m-3 and （2.6 ±1.9） μg·m-3, accounting for 25.2% and 6.0% of PM2.5, respectively, and the average ρ（SOC） was （5.7 ±3.3） μg·m-3, accounting for 52.9% of OC. The concentrations of OC, EC, and PM2.5 showed a downward trend from 2018 to 2020 [PM2.5： The concentration of average annual decrease was -7.1 μg·（m3·a） -1, with an average annual decrease of -14.6 %·a-1； OC： -1.7 μg·（m3·a）-1, -14.2 %·a-1； EC： -0.1 μg·（m3·a）-1, -4.4 %·a-1], and the concentrations of each pollutant in 2021 rebounded in different ranges compared with those in 2020. The concentrations of PM2.5 and OC were as follows： winter > spring > autumn > summer, and the concentrations of EC were as follows： winter > autumn > spring > summer. The proportions of OC and EC were higher in summer and autumn than in other seasons, with the average proportions of 26.8% and 6.9%, respectively. With the aggravation of the pollution level, OC, EC, and SOC concentrations gradually increased, but the proportions in PM2.5 showed a gradual downtrend, indicating that the control factor of PM2.5 pollution in Chengdu was not the carbon component. Source apportionment results showed that carbonaceous aerosols in Chengdu were mainly affected by motor vehicles, industrial sources, biomass combustion sources, and VOCs secondary reaction. From 2019 to 2021, EC was affected by the characteristic components of motor vehicles and decreased yearly. OC and EC were affected by VOCs more in spring and autumn than in other seasons. VOCs emission management should be increased in spring and autumn to reduce the impact of secondary reaction.

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing.

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3-4-1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

Directional Moisture-Wicking Triboelectric Materials Enabled by Laplace Pressure Differences.

Wearable sensors are experiencing vibrant growth in the fields of health monitoring systems and human motion detection, with comfort becoming a significant research direction for wearable sensing devices. However, the weak moisture-wicking capability of sensor materials leads to liquid retention, severely restricting the comfort of the wearable sensors. This study employs a pattern-guided alignment strategy to construct microhill arrays, endowing triboelectric materials with directional moisture-wicking capability. Within 2.25 s, triboelectric materials can quickly and directionally remove the droplets, driven by the Laplace pressure differences and the wettability gradient. The directional moisture-wicking triboelectric materials exhibit excellent pressure sensing performance, enabling rapid response/recovery (29.1/37.0 ms), thereby achieving real-time online monitoring of human respiration and movement states. This work addresses the long-standing challenge of insufficient moisture-wicking driving force in flexible electronic sensing materials, holding significant implications for enhancing the comfort and application potential of electronic skin and wearable electronic devices.

Review of Recent Patents on Smart Bearing

Background: Smart bearing is based on the traditional bearing, which is integrated by sensing devices and control devices, formating a unique bearing structure unit. It uses information processing, automatic control, and other technologies to achieve real-time online monitoring of bearing operating conditions, fault detection, and state control. Compared with ordinary bearings, smart bearings have the characteristics of self-awareness, self-decision, and self-regulation. By analyzing the composition of various types of smart bearings and their working principles, it is beneficial to prospect the future development direction of bearings. Objective: We analyze and organize the patents on smart bearings in recent years, summarize the problems encountered in the development of smart bearings, and propose the development direction of smart bearings in the future to provide a reference for future scientific research. Methods: This paper reviews the patents related to smart bearings and analyzes the composition and structure of smart bearings. The main smart bearings include rolling bearing, sliding bearing, magnetic bearing, and rubber bearing. The principle and structure of these bearings are analyzed. Their advantages and shortcomings are discussed. Results: Through the analysis of relevant patents and papers on smart bearings, the current development status and problems of smart bearings are summarized, the future design of smart bearings is proposed, and the research ideas and directions are proposed. Conclusion: The development of smart bearings is conducive to the timely detection of bearing conditions in the operation process. Except for detection, smart bearings have the functions of judgment, feedback, processing, etc. There are many factors that interfere with the accuracy of the smart bearing judgment and information transfer, so it is necessary to improve the structure and working principle of smart bearings through a variety of measures. In the future, more smart bearings will be invented.

An Online Digital Imaging Excitation Sensor for Wind Turbine Gearbox Wear Condition Monitoring Based on Adaptive Deep Learning Method.

This paper designed and developed an online digital imaging excitation sensor for wind power gearbox wear condition monitoring based on an adaptive deep learning method. A digital imaging excitation sensing image information collection architecture for magnetic particles in lubricating oil was established to characterize the wear condition of mechanical equipment, achieving the real-time online collection of wear particles in lubricating oil. On this basis, a mechanical equipment wear condition diagnosis method based on online wear particle images is proposed, obtaining data from an engineering test platform based on a wind power gearbox. Firstly, a foreground segmentation preprocessing method based on the U-Net network can effectively eliminate the interference of bubbles and dark fields in online wear particle images, providing high-quality segmentation results for subsequent image processing, A total of 1960 wear particle images were collected in the experiment, the average intersection union ratio of the validation set is 0.9299, and the accuracy of the validation set is 0.9799. Secondly, based on the foreground segmentation preprocessing of wear particle images, by using the watered algorithm to obtain the number of particles in each size segment, we obtained the number of magnetic particle grades in three different ranges: 4-38 µm, 39-70 µm, and >70 µm. Thirdly, we proposed a method named multidimensional transformer (MTF) network. Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to obtain the error, and the maintenance strategy is formulated according to the predicted trend. The experimental results show that the predictive performance of our proposed model is better than that of LSTM and TCN. Finally, the online real-time monitoring system triggered three alarms, and at the same time, our offline sampling data analysis was conducted, the accuracy of online real-time monitoring alarms was verified, and the gearbox of the wind turbine was shut down for maintenance and repair.

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.

Real-time in-situ process monitoring method based on the self-conductivity of carbon fiber prepreg for automated fiber placement (AFP)

Real-time online monitoring is of great significance for the manufacturing of carbon fiber reinforced composites, contributing to improving and ensuring the quality of the final manufactured part. An in-situ real-time process monitoring method based on the conductivity of continuous carbon fiber reinforced thermoplastic matrix prepreg was proposed for automated fiber placement (AFP). The detection capability of several different defects was investigated, including foreign object debris, tape breakage, weak edge bonding, gaps/overlaps, and even debonding. In addition, the influence of placement speed and laser power on the detection capacity was also discussed. The number, position, length, and width of various interlayer defects were detected through the resistance change between the current placement layer and the laid layers. Furthermore, accurate detection of defects in different layers was also achieved, indicating the detection capability of existing defects. This method of timely defect detection during the manufacturing process allows for an early manual intervention to improve the quality of manufactured structures and the intelligence of AFP technology.

Triboelectric probes integrated with deep learning for real-time online monitoring of suspensions in liquid transport

The stability of suspensions is critical to the smooth operation of the entire production system, especially during material storage, transportation and production. Nevertheless, the challenge of achieving cost-effective and real-time online monitoring remains, owing to the variability in the particles size and concentration. In this study, a triboelectric probe integrated with deep learning technology were employed to achieve self-powered, real-time, online monitoring of the particles size and concentration, whose variations would influence the distribution of surface charges, thereby inducing changes in the macroscopic dielectric constant of the suspension. Such triboelectric probes successfully captures output signals that reflect changes in the properties of the suspension, based on the coupled effects of contact electrification and electrostatic induction at the liquid-solid interface. Furthermore, a convolutional neural network model within deep learning technologies were established for handling the relevant signals, and performed an average recognition accuracy exceeding 98% for both the particles size and concentration. The integration of the triboelectric probe and deep learning technology presents a novel approach for real-time online monitoring of particles, holds significant implications for the production security monitoring in industries such as pharmaceuticals, chemical engineering, and food production.

Research and Thinking on the Application of Big Data in Budget Performance Management

At present, the budget performance reform has become a breakthrough in the profound reform of government governance, and is an inevitable requirement for further promoting the modernization of the national governance system and governance capacity. Big data technology can improve the quality of budget performance management from four ways: the scientific review of budget performance objectives, the accuracy of performance evaluation, the online real-time monitoring of performance operation and the visualization of performance information. Some local financial departments in China have explored the introduction of big data in the aspects of pre-performance review, database construction, performance operation and target monitoring to carry out budget performance management. Improving the legislation on "budget performance management + big data", promoting the interconnection of cross-departmental data, comprehensively applying multiple data analysis methods, and establishing a composite talent training mechanism are the directions for further improving the quality of budget performance management with big data technology.

Analysis and experimental research of transient temperature rise characteristics of high-speed cylindrical roller bearing

In this paper, on one hand, the time-varying characteristics of the heat source and thermal boundary conditions of the high-speed spindle system were analyzed considering the thermal-structural coupling effect. And a transient bearing temperature field prediction method combining the thermal network method and finite element method was proposed. Furthermore, the relationships between time step, calculation efficiency and calculation results were analyzed. On the other hand, a online real-time monitoring system of the transient temperature of the cylindrical roller bearing inner ring for maximum speed of 13,000 r/min was designed and implemented using fibre optic sensing technology. Comparing with the conventional static thermal analysis results, it is verified that the simulation method proposed in this paper has higher accuracy. This paper provides a new approach for analysing and testing the thermal characteristics of high-speed spindle system.

The development of a generic IIOT framework for an industrial pneumatic system

The development of digital technologies, such as Cyber Physical Systems (CPS) and Industrial Internet of Things (IIoT), under the umbrella of Industry 4.0, has increased rapidly within the manufacturing industry. Monitoring system behaviour in real time is becoming prevalent with manufacturing stakeholders to minimise downtime, maintain or improve equipment efficiency, and enhance production output. This paper proposes a generic IIoT framework for the online real time monitoring of system parameters. Such an approach has been implemented on the demand-side of a pneumatic setup, and a visualisation technique was developed for evaluation purposes. The developed framework paves the way towards further data analytics with the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques.

A Microwave Sensor for Real-Time Online Monitoring and Measuring the Blade Tip Clearance of Aero-Engine

A Microwave Sensor for Real-Time Online Monitoring and Measuring the Blade Tip Clearance of Aero-Engine

Method of measuring atmospheric CO<sub>2</sub> based on Fabry-Perot interferometer

CO<sub>2</sub> is one of the main greenhouse gases. Its emission and accumulation lead to the strengthening of the greenhouse effect, which in turn causes global climate change. Therefore, it is of great significance to obtain the change of CO<sub>2</sub> concentration in the atmospheric environment for the study of climate change. In order to meet the requirements of low cost, fast, on-line and accurate measurement of CO<sub>2</sub> in atmospheric environment, a CO<sub>2</sub> gas concentration measurement system based on Fabry-Perot interferometer is built in this work. The thermal radiation source based on micro-electro-mechanical system (MEMS) technology is used as a light source of the Fabry-Perot interferometer system, and the transmission optical path is designed to replace the common refractive optical path. By electrostatically controlling the distance between the two lenses and changing the interference spectrum, the interference peak adjustment of the center wavelength of the 10 nm step is realized, and the absorption spectrum is obtained by scanning. Based on the principle of differential optical absorption spectroscopy, the concentration of CO<sub>2</sub> gas is obtained, and the real-time on-line monitoring of CO<sub>2</sub> concentration is realized. Using the sample gas calibration system and the commercial photoacoustic spectroscopy multi-gas analyzer to verify the system, the results show that the detection limit of the system is 1.09×10<sup>–6</sup>, the detection accuracy is ±1.13×10<sup>–6</sup>, and the measurement error is less than 1%. Real-time online monitoring of atmospheric CO<sub>2</sub> has been conducted in Huaibei, a coal city. A comparative observational experiment is performed between this system and a commercial photoacoustic spectroscopy multi-gas analyzer. The two systems show consistent trends in measuring CO<sub>2</sub> variations, with a correlation coefficient of <i>R</i>=0.92. It shows that the Fabry-Perot interferometer system can meet the requirement of rapid, convenient and high precision measurement of CO<sub>2</sub> concentration in the environment.

Improving long-term operation performance of hazardous waste rotary kiln incineration facilities: An evaluation with DEA model

Hazardous waste rotary kiln incineration, as the most effective and comprehensive technology to reduce and detoxify waste, generally faces problems such as low load rate and short continuous operating periods. However, there are few studies on the actual operation of such facilities and evaluation of their technical efficiency. Based on the 77-week time-series data of the case company, this study introduces in-depth key operating parameters and evaluates long-term technical efficiency through the data envelopment analysis (DEA) method. The results show that the continuous operating period of the rotary kiln incineration facility can reach more than half a year, with an average load rate of 91.7%. In the analysis of 9 input indicators, the amount of injected activated carbon could not be effectively evaluated due to the lack of relevant standards and online real-time monitoring of dioxins, which might become a weak link in the control of flue gas pollution. The average comprehensive technical efficiency of rotary kiln incineration facilities was 0.939, of which the average pure technical efficiency was 0.949 while the average scale efficiency was 0.989. With 33 of the 77 decision-making units being invalid, there is scope for improvement. The amount of incineration could be increased by 5.34%, and among the input variables, dosage of urea, calcium hydroxide and lye with a relatively high improvement ratio. Based on the results, targeted suggestions were proposed to advance the scientific and precise compatibility of hazardous waste, strengthen the control of dioxin emissions, and promote the intelligent control of the entire process.

Online real-time learning of dynamical systems from noisy streaming data

Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

Shrimp Pond Monitoring System using Cooperative Wireless Sensor Network Multi-Hop Technique based on Internet of Things

Water quality is a crucial factor in maintaining the survival and growth of shrimp. Manual water quality monitoring in shrimp ponds is no longer effective due to the need for periodic monitoring to maintain stable water quality. Therefore, online monitoring using various sensors installed in each pond is necessary. However, there are several challenges to overcome, such as the large expanse of the shrimp ponds, which may lead to data loss due to signal disruptions, and limited energy to power the sensors. To address these issues, this paper proposes the cooperative Wireless Sensor Network (WSN) technique with a multi-hop method for communication in the monitoring process. The system consists of five sensor nodes: temperature sensor, pH sensor, water level sensor, intake water flow sensor, and drain water flow sensor. The cooperative WSN multi-hop technique helps reduce energy consumption in the sensor nodes during measurement and data transmission, while also preventing data packet loss. This is achieved through the use of relay nodes that strengthen signals and forward data to the sink node. As a result, the battery life is extended, and energy usage in the monitoring process can be optimized. The system enables real-time online monitoring and can be accessed through a smartphone application. The results of this study show that the total energy consumption for data transmission in the sensor nodes is 9.64 J, while the total energy consumption for data forwarding in the relay nodes is 9.15 J. The total energy consumption in the transmit and receive processes is 18.79 J or 5.2 mWh. Therefore, it can be concluded that the energy savings of the proposed system is 4.3 mWh or approximately 45%, and is more efficient than the previous system.

CFRP drive shaft damage identification and localization based on FBG sensing network and GWO-BP neural networks

Carbon Fiber Reinforced Plastic (CFRP) has the advantages of light weight, high strength, small coefficient of thermal expansion, good vibration damping performance, etc., the use of its production drive shaft can reduce weight, improve the intrinsic frequency, transmission efficiency and precision, and has been used in helicopters, heavy duty machine tools, wind turbines, ships and other high-end equipment. Once the damage occurs, it will cause adverse effects such as reduction of mechanical efficiency of the system, degradation of transmission system performance, and decrease of reliability and safety. The existing non-destructive testing technology for carbon fiber reinforced plastic has limitations such as inconvenient use, complex equipment, and no real-time online monitoring. To address this problem, this paper adopts Fiber Bragg Gratings (FBG) sensors to obtain real-time strain field data of CFRP drive shaft, and uses the linear and nonlinear mapping ability of Back Propagation Neural Network (BP neural network) to identify and locate the damage of CFRP drive shaft. At the same time, the GWO optimization algorithm is used to optimize the BP neural network to construct a CFRP drive shaft damage identification and localization system based on FBG sensing network and GWO-BP neural network. The experiments show that the CFRP drive shaft damage identification and localization system constructed in this paper can obtain the CFRP drive shaft strain field information in real time and accurately identify the damage and its location.