Acquisition Module Research Articles

Research on multi-fault diagnosis method and test platform of main transmission Machinery of wind Turbine based on electrical signal

An optimised design was carried out based on the original semi-physical simulation system for the main drive chain of a wind turbine. The input of upper computer simulating wind condition, the safety control of electrical signal and vibration signal acquisition module, relay and AC contactor and dual motor control were added. Simultaneously, the simulation schemes of some common faults on the main drive of wind turbines were also designed in order to perform the experimental simulation of the single fault and multi fault. The primary purpose of simulation is to use practical approaches(such as FFT,EMD)to analyze the changes of the fault signals based on the collected electrical signals, and to determine whether there is a fault through comparison and theoretical analysis. In addition, it aimed for verifying the feasibility of electrical signals under multi-fault conditions, and discussing advantages of electrical signals over vibration signals.

Respiration Monitoring using a Motion Tape Chest Band and Portable Wireless Sensing Node

The measurement of vital signs (such as respiration rate, body temperature, pulse, and blood pressure), especially during strenuous activities, is essential for physical performance and health monitoring. A variety of wearable chest band sensors have been developed, commercialized, and widely used in consumer and healthcare settings. The plethora of technology choices also means that each unique chest band sensor may require different data acquisition hardware and software systems, and data may not be transferable between platforms. Therefore, the objective of this work was to develop a low-cost, disposable, respiration sensor that could be attached onto any elastic chest band. The approach was to spray-coat graphene nanosheet (GNS)-based thin films onto unidirectionally stretchable elastic fabric to form a piezoresistive material. Snap buttons were incorporated at the ends of the fabric so that they could be attached onto any chest band, removed at any time, and replaced for a new data collection event. The resistive nature of the nanocomposite sensor means that they can be easily interfaced (e.g., using a voltage divider) with any existing data acquisition (DAQ) module while adding respiration monitoring capabilities. To facilitate testing of these nanocomposite respiration sensors, a miniature DAQ module with four sensing channels was also prototyped. Then, tests were performed with human subjects wearing a nanocomposite chest band and a reference commercial respiration monitoring chest band. Simultaneous measurements of subject respiration verified the respiration monitoring performance of these low-cost, disposable, nanocomposite fabric sensors.

Green Building Design Based on 5G Network and Internet of Things System

An information-based and intelligent comprehensive energy consumption management system should not only collect, manage, and analyze energy consumption data but also monitor the environmental data inside the building, to manage and display the real energy consumption and internal environmental quality more effectively and reliably. In order to solve this problem, the design and construction of the front-end hardware of the energy consumption management system are proposed, mainly including the design of data acquisition node and data centralization end. The design of data acquisition node includes the selection of DSP controller, wireless communication module, and data acquisition module and the design of main circuit and peripheral circuits. The design of data centralization end includes the selection of its high-performance processor and the transplantation of Linux operating system. This paper only introduces the design and research of lighting control algorithm and applies fuzzy control algorithm to intelligent lighting. The fuzzy control system is introduced, and a fuzzy controller based on intelligent lighting is created. The fuzzy control rule table and fuzzy control query table of the intelligent lighting system are obtained through the fuzzy control algorithm. According to the basic rules of light intensity control, when the indoor light intensity is greater than the set value of light intensity, the LED lamps are turned off, so when e < 0 , the LED lamp is off and the PWM output value is 0. Therefore, when designing the fuzzy controller of intelligent lighting system, the basic domain of illumination intensity error E is 0500 , excluding the case that the indoor illumination intensity is greater than the set value of illumination intensity.

Heat current method-based real-time coordination of power and heat generation of multi-CHP units with flexibility retrofits

Real-time coordination and optimization of multiple combined heat and power (CHP) units under off-design conditions benefits overall efficiency improvement, whereas the coupling of power and heat generations among multi-units makes the system more complex. The existing software cannot tackle model accuracy and prompt optimization simultaneously. Herein, a heat current method-based real-time coordination method of power and heat generation is proposed. An optimization platform consisting of four software modules and corresponding hardware is developed to search for the optimal operating state of multi-units in a CHP plant with flexibility retrofits. After system modeling based on the heat current method, the data acquisition and curation module reads data from the distributed control system (DCS) server and then the parameter identification module provides the characteristic parameters of each component for optimization. The hierarchical and categorized algorithm is applied to solve the heat current-based model of the integrated cogeneration system, and the genetic algorithm is used for optimization. The information output module displays the optimized results on the DCS client interface. The platform is deployed in a practical plant with two connected CHP units. Field test results show that the reduction of standard coal consumption of the whole plant reaches up to 3.10 g/kWh.

Experimental investigation of temperature gradients in a three-cell concrete box-girder

To study the temperature field of a three-cell concrete box girder, a box girder model was poured, and 258 temperature sensors were embedded in the model. Environmental parameters, including air temperature, air humidity, wind speed, wind direction and solar radiation, were recorded by a weather station nearby the model. Data receiving frequency was once every 30 mins, and a wireless acquisition module for data collection was used. After a year of temperature observation, the temperature field of a three-cell concrete girder was obtained. Temperature prediction equations for a three-cell box girder were established for the first time, which used air temperature, solar radiation, and wind speed at a moment, rather than temperature prediction equations of previous studies, which used the maximum single-day temperature difference, the total single-day solar radiation and the average wind speed. The lateral temperature difference of the sunny side web was more obvious in winter. The lateral temperature difference with a 98% exceeded probability was 19.49 °C, which was larger than the recommended value of the European standard (15 °C). The negative temperature gradient influenced by cold wave was considered. The maximum lateral temperature gradient of the top plate and bottom plate in winter was larger than that in summer. The vertical and lateral temperature difference calculated by the present equations agreed well with the experimental values recorded by a five-tower six-span cable-stayed bridge.

Battery-free wireless moisture sensor system for fruit monitoring

Fruit quality monitoring has been more and more important. This paper aims to propose and develop a battery-free wireless moisture sensor system for fruit monitoring (BWMS). The BWMS is made up of the battery-free moisture sensor tag and the near filed communication (NFC) reader. The battery-free moisture sensor tag includes the sensor acquisition module and NFC module. The storage temperature and weight sensor data of the fruit could acquire by the battery-free moisture sensor tag and wirelessly transmitted to the NFC reader by 13.56 MHz when receiving the data acquisition command from NFC reader. The BWMS could effectively monitor the MLR by acquiring the temperature and weighing sensor data with NFC, and predict the shelf-life of the fruit with in need by the shelf-life prediction model. All the data obtained by the BWMS was in real time monitored, stored, processed and finally displayed in the app of the NFC reader to improve the temperature, MLR and shelf-life transparency of the fruit, and finally ensure the quality and safety of fruit. The proposed BWMS has a potential application in many kinds of food monitoring by NFC and it could realize the shelf-life prediction for the simple and intuitive quality indicating of food.

Artificial Intelligence in the Auxiliary Guidance Function of Athletes’ Movement Standard Training in Physical Education

Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. This paper is based on the research of AI in the auxiliary guidance function of athletes’ standard training in physical education. It aims to conduct data mining from different aspects such as different joint angular speeds, motion injury screening and different parts of sports injuries and then integrate these aspects. Create a way to reduce athletes’ injuries and scientific training. In order to improve the recognition efficiency of athletes’ movement patterns, nonlinear auto regressive neural networks are used to recognize the movement patterns of athletes’ limb surface signals. Through this research work, it can provide a certain reference basis and practice platform for the research on the auxiliary guidance role of AI in the sports standard training of athletes in physical education. Performance design and implementation include four modules: Image acquisition, preprocessing, motion detection and human motion recognition. Between them, the image acquisition module uses a memory mapping path to determine the format of the camera frame image, and the image format conversion is completed through channel conversion. Experimental data shows that athletes’ strength quality, speed of action response, technical continuity, psychological stability and physical control ability have all been greatly improved. Among them, the most obvious is that with the assistance of AI technology, the psychological stability has reached 9.2; the strength quality has reached 9.1.

Development and in-situ application of a real-time cutting tool forces monitoring system in TBM tunnelling

In order to determine the operational status of tunnel boring machines (TBMs), a real-time cutting tool force monitoring system is established based on the proposed cutting tool force measurement and calculation method. The monitoring system is composed of strain sensors, a data acquisition module, a data transmission module, and a data analysis module. The sensor layout and the complete monitoring system operating principle are presented. A dynamic rotary rock cutting test was carried out to verify the effectiveness of the proposed cutting tool force measurement method and corresponding monitoring system. Finally, the monitoring system was applied to the ‘Colorful Clouds’-named TBM in the Gaoligongshan Tunnel (China). This monitoring system can provide continuous power supply, unattended automatic operation, and monitor error information feedback, thereby realizing real-time monitoring, remote control, and data sharing. The field monitoring results show that the cutter's maximum peak normal force and maximum peak side force appear every few seconds. Every two normal force peaks mark a complete penetration process. The maximum and minimum normal force peaks are approximately 800 kN and 50 kN, respectively, and the maximum normal force peak is approximately 6–7 times the peak side force. The normal force frequency indicates that the cutter's normal force is concentrated from 0 to 300 kN, accounting for approximately 95% of the overall proportion.

SPRING-RIO TSE: 2D T2 -Weighted Turbo Spin-Echo brainimaging using SPiral RINGs with retraced in/out trajectories.

PurposeTo develop a new approach to 2D turbo spin ‐echo (TSE) imaging using annular spiral rings with a retraced in/out trajectory, dubbed “SPRING‐RIO TSE”, for fast T2‐weighted brain imaging at 3T.MethodsA long spiral trajectory was split into annular segmentations that were then incorporated into a 2D TSE acquisition module to fully exploit the sampling efficiency of spiral rings. A retraced in/out trajectory strategy coupled with spiral‐ring TSE was introduced to increase SNR, mitigate T2‐decay induced artifacts, and self‐correct moderate off‐resonance while maintaining the target TE and causing no scan time penalty. Model‐based k‐space estimation and semiautomatic off‐resonance correction algorithms were implemented to minimize effects of k‐space trajectory infidelity and B0 inhomogeneity, respectively. The resulting SPRING‐RIO TSE method was compared to the original spiral‐ring (abbreviated “SPRING”) TSE and Cartesian TSE using simulations, and phantom and in vivo acquisitions.ResultsSimulation and phantom studies demonstrated the performance of the proposed SPRING‐RIO TSE pulses sequence, as well as that of trajectory correction and off‐resonance correction. Volunteer data showed that the proposed method achieves high‐quality 2D T2‐weighted brain imaging with a higher scan efficiency (0:45 min/14 slices versus 1:31 min/14 slices), improved image contrast, and reduced specific absorption rate compared to conventional 2D Cartesian TSE.Conclusion2D T2‐weighted brain imaging using spiral‐ring TSE was implemented and tested, providing several potential advantages over conventional 2D Cartesian TSE imaging.

SAR-Connect: A Socially Assistive Robotic System to Support Activity and Social Engagement of Older Adults.

Multi-domain activities that incorporate physical, cognitive, and social stimuli can enhance older adults' overall health and quality of life. Several robotic platforms have been developed to provide these therapies in a quantifiable manner to complement healthcare personnel in resource-strapped long-term care settings. However, these platforms are primarily limited to one-to-one human robot interaction (HRI) and thus do not enhance social interaction. In this paper, we present a novel HRI framework and a realized platform called SAR-Connect to foster robot-mediated social interaction among older adults through carefully designed tasks that also incorporate physical and cognitive stimuli. SAR-Connect seamlessly integrates a humanoid robot with a virtual reality-based activity platform and a multimodal data acquisition module including game interaction, audio, visual and electroencephalography responses of the participants. Results from a laboratory-based user study with older adults indicates the potential of SAR-Connect that showed this system could 1) involve one or multiple older adults to perform multi-domain activities and provide dynamic guidance, 2) engage them in the robot-mediated task and foster human-human interaction, and 3) quantify their social and activity engagement from multiple sensory modalities.

An IOT based efficient energy management in smart grid using DHOCSA technique

• Energy management system for distribution system with IoT with hybrid technique. • Deer hunting optimization (DHO) algorithm and crow search algorithm (CSA). • Optimally control the power and distribution system resources depends on IoT. • Distribution system interconnected with the data acquisition module. • IoT-based communication structure utilized to facilitate the demand response. • IoT distribution system maximizes that flexibility of network. This article proposes an Energy Management System (EMS) for distribution system with Internet of Things (IoT) framework through hybrid technique. The proposed hybrid system is the joint implementation of Deer Hunting Optimization (DHO) and Crow Search algorithm (CSA) and thus it is known as DHOCSA method. The major purpose of the proposed work is optimally control that power and distribution system resources through continuous data monitoring as communication framework depends on IoT. In the proposed system, the distribution system interconnected with the data acquisition module, which is IoT object through single IP address that results at mesh wireless network of devices. The IoT-based communication structure utilized for facilitating the development of demand response (DR) for the EMS distribution system. This framework collects the DR from load and broadcast the data to a centralized server. The transmitted data is processed using the DHOCSA technique. In this manner, the IoT distribution system maximizes that flexibility of network provides optimal utilize of the available resources. Also the proposed system is dependable to satisfy that global supply and energy demand. Finally, the proposed system performance actualized on MATLAB/Simulink platform with three test cases and compared with various approaches.

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology.

This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge pixel count was calculated as per doctor’s opinion and later these data are used for calculating the thresholds using hybrid k-means algorithm, for giving a decision on the presence of cataract and grade its severity. Low value of uniformity and high value of other texture parameters confirm the presence of cataract as clouding in eye lens causes the uniformity function to take lower value due to presence of coarse texture. Higher the edge pixel count value, this confirms the presence of starting of cataract as solidified regions in lens are nonuniform. Lower value corresponds to fully solidified region or matured cataract. Proposed algorithm was initially developed on MATLAB, and tested on over 300 patients in an eye camp. The system has shown more than 98% accuracy in detection and grading of cataract. Later a cloud based system was developed with 3D printed image acquisition module to manifest an automated, portable and efficient cataract detection system for Tele-Ophthalmology. The proposed system uses a very simple and efficient technique by mapping the diagnostic opinion of the doctor as well, giving very promising results which suggest its potential use in teleophthalmology applications to reduce the cost of delivering eye care services and increasing its reach effectively. Developed system is simple in design and easy to operate and suitable for mass screening of cataracts. Due to non-invasive and non-mydriatic and mountable nature of device, in person screening is not required. Hence, social distancing norms are easy to follow and device is very useful in COVID-19 like situation.

Analysis of a Kind of Harr Characteristic Big Data Algorithm for College Students’ Safety Education Method

In order to improve the effectiveness of safety education in colleges and universities and to maintain the long-term effectiveness of college students’ safety awareness, combined with the current safety problems in colleges and universities, by analyzing the relevance of campus safety accidents and safety education, a Harr characteristic big data algorithm for college students is proposed. Safety education methods have constructed a safety education system for college students. The status quo of safety education is analyzed for college students, a safety education system image acquisition module is designed based on Harr characteristic big data algorithm, Harr characteristic big data algorithm and Adaboost algorithm are used to shorten the training time of college students’ safety education image, learning resources are saved, and remote teaching is realized. A set of safety knowledge question banks based on recommendation algorithm has developed a set of practical safety knowledge online learning and testing systems, and targeted solutions to prevent and reduce campus safety accidents are proposed. The experimental results show that the number of student safety incidents in the school within 1 year after the application of this system is significantly reduced to 58, the system’s experimental response time is only 0.05 s on average, and the student’s satisfaction with the system reaches 93%. The application effect of the system is obvious, and it can effectively prevent and reduce the occurrence of campus safety accidents.

Development of Cloud Computing Platform Based on Neural Network

Aiming at the problems of small data storage, small platform throughput, and high energy consumption in the development of existing cloud computing platforms, this paper develops a cloud computing platform based on neural networks. In the design of the platform, firstly, the functions of the cloud computing platform are determined. Based on the function design, the hardware and software of the cloud computing platform are designed. In the hardware design, the topology of cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed. In the software design, the neural network is mainly used to remove the redundancy of data stored in a cloud computing platform, design the data processing flow of cloud computing platform, and complete the development of cloud computing platform based on neural network. The experimental results show that the cloud computing platform based on the neural network designed in this paper runs faster, the throughput of platform data has been significantly improved, and the operating energy consumption of the platform is low.

Computer Network Intrusion Anomaly Detection with Recurrent Neural Network

Network intrusion anomaly detection technique has been widely employed in computer network environments as a highly effective security prevention method. As network technology and network applications have advanced at a rapid pace, so too has network data traffic, resulting in an increase in virus and attack kinds. In the face of large-scale traffic and characteristic information, traditional intrusion detection will have problems such as low detection accuracy, high false negatives, and reliance on dimensionality reduction algorithms. Therefore, it is particularly important to establish a fast and efficient network intrusion anomaly detection method to deal with the current complex network environment. This work designs a computer network intrusion detection model with a recurrent neural network in order to explore a new intrusion detection method. The main purpose of this article include the following: (1) design a network security emergency response system architecture with the recurrent neural network model. This system consists of a management center module, a knowledge database module, a data acquisition module, a risk detection tool module, a risk analysis and processing module, a data protection module, and a remote connection auxiliary module. The modules cooperate with each other to complete system functions. (2) Aiming at the risk analysis and processing module, a network intrusion detection model combining bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) is designed. In view of the lack of consideration of the before-and-after relevance of intrusion data features and the multifeature problem in existing models, the use of BiLSTM to extract the relevance between features and the use of DNN to extract deeper features are proposed. Aiming at the problem that the model lacks consideration of the importance of features, it is proposed to embed an attention mechanism into the network to increase consideration for the importance of features. (3) Massive experiments have verified the reliability and effectiveness of the method proposed in this work.

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Quantitative assessment of cellular forces and motion advanced considerably over the last four decades. These advancements provided the framework to examine insightful mechanical signaling processes in cell culture systems. However, the field currently faces three problems: lack of quality standardization of the acquired data, technical errors in data analysis and visualization, and perhaps most importantly, the technology remains largely out of reach for common cell biology laboratories. To overcome these limitations, we developed a new experimental platform - Integrative Toolkit to Analyze Cellular Signals (iTACS). iTACS consists of two components: Acquisition and Training Module (AcTrM) and Analysis and Visualization Module (AnViM). AcTrM is based on µManager - an NIH-ImageJ-based microscope control software - and facilitates user self-training and automation of common image acquisition protocols. AnViM is based on NIH-ImageJ and facilitates user-friendly automation of data analysis and insightful visualization of results. These experiments involve culturing adherent cells on hydrogels, imaging fiducial markers embedded in the hydrogel, and finally extracting from these images a comprehensive mechanical characterization of the cells. Currently, iTACS enables the user to analyze and track a wide array of properties, including morphology, motion, cytoskeletal forces, and fluorescence of individual cells and their neighboring region. The quality standardization issue was addressed in AcTrM with, a reference image-guided refocusing technique. The technical issues in data analysis were addressed in AnViM with a multi-pronged image segmentation procedure, a user-friendly approach to identify boundary conditions, and a novel cellular property-based data visualization. AcTrM is designed to facilitate the straightforward transformation of basic fluorescence microscopes into experimental cell mechanics rigs, and AnViM is equipped to enable users to measure cellular mechanical signals without requiring an engineering background. iTACS will be available to the research community as an open-source suite with community-driven development capabilities.

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence.

Quantitative assessment of cellular forces and motion advanced considerably over the last four decades. These advancements provided the framework to examine insightful mechanical signaling processes in cell culture systems. However, the field currently faces three problems: lack of quality standardization of the acquired data, technical errors in data analysis and visualization, and perhaps most importantly, the technology remains largely out of reach for common cell biology laboratories. To overcome these limitations, we developed a new experimental platform - Integrative Toolkit to Analyze Cellular Signals (iTACS). iTACS consists of two components: Acquisition and Training Module (AcTrM) and Analysis and Visualization Module (AnViM). AcTrM is based on µManager - an NIH-ImageJ-based microscope control software - and facilitates user self-training and automation of common image acquisition protocols. AnViM is based on NIH-ImageJ and facilitates user-friendly automation of data analysis and insightful visualization of results. These experiments involve culturing adherent cells on hydrogels, imaging fiducial markers embedded in the hydrogel, and finally extracting from these images a comprehensive mechanical characterization of the cells. Currently, iTACS enables the user to analyze and track a wide array of properties, including morphology, motion, cytoskeletal forces, and fluorescence of individual cells and their neighboring region. The quality standardization issue was addressed in AcTrM with, a reference image-guided refocusing technique. The technical issues in data analysis were addressed in AnViM with a multi-pronged image segmentation procedure, a user-friendly approach to identify boundary conditions, and a novel cellular property-based data visualization. AcTrM is designed to facilitate the straightforward transformation of basic fluorescence microscopes into experimental cell mechanics rigs, and AnViM is equipped to enable users to measure cellular mechanical signals without requiring an engineering background. iTACS will be available to the research community as an open-source suite with community-driven development capabilities.

High sensitivity and fast detection system for sensing of explosives and hazardous materials

We demonstrate a high sensitivity and fast detection system for sensing of explosives and hazardous materials based on the external cavity quantum cascade laser (ECQCL). In this system, one galvanometer is utilized as the tuning component of the ECQCL to get high wavelength scanning speed. Meanwhile, the driving module of the galvanometer and the analog to digital conversion (ADC) module for data acquisition are synchronized, which brings in great significance for fast high sensitivity detection. For one thing, this synchronization exactly defines the data of each scanning period during data acquirement and processing, which enables our system to avoid data dislocation and cumulative error. For another, enough averaging times are allowed to obtain high sensitivity while detection speed is high enough for practical application. To verify the practical application effect, this system was utilized to detect trace amount of adsorbed trinitrotoluene (TNT) on the People's Republic of China Resident Identity (ID) Card. Although the wavelength tuning rate was as high as 17400 cm−1/s over the range of 1280–1454 cm−1(∼6.88–7.81 µm) with scanning frequency of 100 Hz, a TNT detection sensitivity of 1 μg/cm2 was achieved with detection speed of one result per second through multiple averaging technique.

An automated structural health monitoring system developed for Ekaterinburg Arena

Introduction. The paper presents those essential Structural Health Monitoring (SHM) implementation details that have not been sufficiently documented in Russian national guides or regulations. The study aims to systematize and share the experience of applying the previously developed SHM theoretical principles to the existing large-span unique sports facility Ekaterinburg Arena. The paper provides information about determining monitored structural elements, instrumentation alignment, and monitoring parameters’ threshold value.
 Materials and methods. The long-term SHM system of the large-span structure of the Arena is organized around hardware and software for processing the data constantly incoming from tiltmeters, strain gauges, accelerometers, a motorized total station, and a weather station.
 Results. A unified information system for automated SHM of the Arena has been created. It comprised modules for data acquisition, high-performance databases for storing metrics, metadata, and the project information, services for evaluating and visualizing structural parameters, components for the instrumentation self-diagnosis, an application development interface for sophisticated data analysis, and a 3D model of the structure. Engineering methods for evaluating the initial values of the controlled parameters and their thresholds acceptable for regular operation are proposed. The technique of calculating internal forces based on the strain gauge data with a redundant measurement pattern has been tested.
 Conclusions. The obtained monitoring results have become a valuable source of initial data for developing automated methods for detecting and locating damages in structures, improving structural analysis and design methods, predictive maintenance, surveys, and solving other knowledge-intensive tasks. Applying the SHM theoretical principles to the real structure can significantly contribute to developing the current immature scientific and regulatory framework for implementing similar monitoring systems at complicated construction facilities.

Research and Design of Remote Online Supervision System of Coal Mine Electromechanical Equipment

Increasing the supervision of coal mine electromechanical equipment is an urgent need to ensure the safe production of coal mines. This paper designs and develops a coal mine electromechanical equipment supervision system, which includes four parts: mine-side monitoring subsystem, mine-side data acquisition and transmission module, local-side data analysis and storage module, and coal mine electromechanical equipment supervision platform. It realizes coal mine ventilator, drainage pump, air compressors, hoists and other major electromechanical equipment online monitoring and equipment failure analysis. The system satisfies the remote online inspection and management of equipment by multi-level departments, reduces the possibility of enterprise accidents, innovates the supervision mode, and improves the efficiency of supervision.