95results about How to "Realize fault warning" patented technology

The invention discloses a cloud-edge cooperative amusement equipment fault prediction and health management system and method. The system comprises a distributed data acquisition system, edge computing equipment, a cloud computing platform and a management and control center. The data acquisition system distributed on the amusement equipment can acquire various heterogeneous state data including operation parameters, mechanical system parameters, hydraulic and pneumatic system parameters, electrical system parameters and the like in real time; and the edge computing equipment comprises an edgedata management functional module, a prediction service orchestrator functional module, an edge model training functional module, a fault early warning functional module and a communication service functional module, so the data caching capacity, the equipment health diagnosis capacity and the early warning capacity of an edge platform are achieved. The cloud computing platform comprises a data warehouse module, a model training module, a health management module and a communication service module, and achieves the data storage and model training capability of the cloud. The management and control center provides man-machine interaction, and is used for monitoring operation parameters and health conditions of the amusement equipment and receiving a fault prediction message.

The invention relates to a power system fault pre-alarming method based on the traveling wave electric amount measurement, belonging to the field of power system relay protection; the method comprises: collecting the traveling wave electric amount measured on part or all the power equipment in the power system in real time and synchronously monitoring whether perturbation occurs on the power system; recording the occurrence time of the perturbation; and comparing the characteristics of the traveling waves electric amount on all the power equipment and determining the power equipment where the perturbation occurs; comparing whether the power equipment where the last three times perturbations including the current perturbation occur is the same power equipment; if yes, calculating the interval between the first perturbation and the third perturbation of the last three perturbations; the time interval being smaller than or equal to the preset time interval, judging that the power equipment has a fault and outputting the result of the fault pre-alarming. The invention can promptly provide power system fault pre-alarming information, thus reducing the power system faults, improving the power system supply reliability and ensuring the safe and stable operation of the power systems.

The invention discloses a device for measuring an elevator motion. The device comprises a camera arranged on an elevator car, and a target arranged in an elevator hoistway, wherein the camera is used for shooting a target image and sending the target image to a processor; and the processor calculates a motion state of an elevator according to the target image. In addition, the invention further provides corresponding method and system for measuring the elevator motion. The device is high in measuring accuracy based on a visual system.

The invention provides a digitalized coal yard system based on Internet of Things and an artificial intelligence system, relates to the technical field of fuel management, digitalized factories and power generation, and particularly relates to a digitalized management system of a coal yard. The method is based on the Internet of Things. The invention discloses a digital coal yard system of an artificial intelligence system. Digitization and intelligentization of coal yard and fire coal data are achieved by means of an artificial intelligence system, coal quality test equipment and entrance detection equipment are combined by means of coal yard related equipment and an information acquisition intelligent terminal, fire coal digital management is achieved, a data reference basis is providedfor coal blending combustion and fire coal planning, and actual economic benefits are brought to enterprises. According to the invention, a management chain from entry to evaluation is required to beestablished, and closed-loop management of fuel is realized, so that a coal yard management platform and a coal-fired data center are provided on the basis of an overall scheme architecture, and digitalization, visualization and intelligentization of coal yard management of power generation enterprises are realized through construction of the coal yard management platform and the coal-fired data center.

The invention discloses an automobile manufacturing MES data acquisition system based on the industrial Internet. Data acquisition and data transmission are included. The data acquisition comprises sensor data acquisition, high speed acquisition card data acquisition, PLC-to-PLC data acquisition, PLC-to-PC data acquisition, and data acquisition from an industrial personal computer to a data server. The data transmission is set to be in the way that the acquired data is transmitted to a big data platform. According to the invention, data acquisition is normalized; by use of the high speed dataacquisition technology, the sensing technology and the buffering technology, by use of the distributed acquisition mode, each system data in a workshop currently and data of production devices are acquired by use of the microsecond-grade and millisecond-grade high frequency acquisition technology; wide-coverage and high-frequency data acquisition is achieved; and by use of the big data technology,stability and efficiency of system operation are ensured.

The embodiment of the invention provides an online monitoring and diagnosis method and system of a station unit. The method includes the steps of obtaining operating data of all measuring points of all parts in the station unit, wherein the operating data includes bushing vibration data, load data or temperature data; fitting the operating data of the measuring points and a historical operating curve of the measuring points to obtain predicted operating data of the measuring points; determining early warning information of the measuring points on the basis of the predicted operating data and apreset threshold; and conducting frequency spectrum analysis with early warning information of any part within preset time as the operating data of the measuring point giving an alarm, and determining a failure diagnosis result of the part on the basis of a frequency spectrum analysis result and a preset diagnosis rule. By means of the method and system, the real-time monitoring and failure earlyearning of the station unit are realized, the failure diagnosis intelligence of the station unit is realized, the operation and maintenance pressure of the station unit can be effectively relieved, operation and maintenance cost is reduced, and operation and maintenance efficiency is improved.

The invention relates to an on-line early warning method for failure of a cross-linked polyethylene power cable, belonging to the field of on-line monitoring of power cables and comprising a cross-linked polyethylene power cable to be detected. The on-line early warning method for failure of the cross-linked polyethylene power cable is characterized in that when restorable failure of a certain cross-linked polyethylene power cable to be detected is detected and recorded on line in real time, an instantaneous electromagnetism transient signal flowing through a metal shielding layer earthing conductor of the power cable to be detected is generated, and the instantaneous electromagnetism transient signal is analyzed by utilizing a specific criterion so as to make an early warning for the power cable to be detected. By adopting the on-line early warning method of the restorable failure of the power cable, on-line early warning is performed on the restorable failure before the cable is damaged, which is beneficial for the power supply departments to overhaul in advance or in a planned way, avoiding sudden failure of the cable to cause power failure, reducing economic loss caused by the power failure and adverse effect brought for enterprise production and people's life, and improving the reliability of the power supply.

The invention discloses a high-voltage insulator online monitoring system, and belongs to the technical filed of high-voltage protection electric appliances. The system comprises a data acquisition module, a lightening protection module, a microprocessor, a data transmitting module and a GPS module. The monitoring system wirelessly transmits signals to a monitoring center via the data transmitting module. The data acquisition module is used for acquiring insulator current, onsite temperature, atmosphere humidity and atmosphere flow velocity data and is connected with the lightening protection module. The data acquisition module is connected with the microprocessor to transmit the data to the microprocessor. The GPS module is connected with the microprocessor and used for positioning the location of each insulator to transmit the location to the microprocessor. The microprocessor transmits the acquired insulator data to the monitoring center via the data transmitting module. Problems that insulator online faults are various, the working system is instable and prediction results are difficult to notify timely can be solved so that the high-voltage insulator online monitoring system has advantages that the data signals are collected and analyzed timely, the early warning phenomenon is discovered and alarm is performed timely and fault early warning is realized.

The invention provides a service performance monitoring and fault positioning method. The method comprises the following steps: separately setting different types of service performance monitoring points at different locations of a network; monitoring the service performance of the network at the different types of monitoring points; and when the network anomaly is detected at the monitoring points, positioning the network faults according to the anomaly type monitored by the different monitoring points. The invention also provides a service performance monitoring and fault positioning device.

The invention relates to a sulfur hexafluoride gas leakage monitoring system and method based on Internet of Things. The system comprises a detection unit, a multi-channel alarm controller, cloud platform management and a user terminal; the detection unit comprises a gas detection transmitter; the gas detection transmitter is used for collecting data information; the gas detection transmitter is internally provided with an SF6 sensor; the multi-channel alarm controller comprises a main controller, an alarm control module, a fan and an acousto-optic alarm; the cloud platform management comprises a data center, historical data, early warning information, equipment management, a management center and strategy management; the user terminal comprises a mobile phone terminal and a tablet computer terminal. According to the sulfur hexafluoride gas leakage monitoring system and method based on Internet of Things, the monitoring of the states of different transmitters can be achieved, the information uploading of a plurality of transmitters of different manufacturers and models can be achieved, the fault early warning and fault preprocessing can be achieved, a cloud platform management module can store data information, the centralization and one-stop management of equipment and data in the system can be achieved, manual on-site monitoring can be avoided and manpower time and economic cost can be saved.

The invention discloses a wind turbine generator bearing early warning method based on feature fusion. The method comprises the following steps: preprocessing CMS data; obtaining a time domain characteristic index; obtaining trend characteristic indexes; obtaining a frequency domain characteristic index; obtaining envelope characteristic indexes; carrying out feature fusion; training an extreme gradient lifting model; training a function set in the classification model, and constructing a learning objective function of XGBoost; and calculating the learning objective function of the XGBoost. According to the technical scheme, the vibration data of the CMS state monitoring system is utilized, the generator bearing fault mechanism is analyzed from the time domain feature, the trend feature, the frequency domain feature and the envelope feature, the four features are fused, the feature vector representing the running state of the generator is effectively extracted, the method greatly improves the recognition sensitivity of the algorithm for the generator set bearing fault, achieves the fault early warning through employing a limit gradient lifting method, and is higher in accuracy.

The invention discloses a power distribution switch machinery characteristic on-line monitoring system and a monitoring method. The power distribution switch machinery characteristic on-line monitoring system comprises a power distribution switch, a power distribution terminal and main station waveform recording and analysis system which are successively connected. The power distribution switch has an operation mechanism and controls the on-and-off of the power distribution circuit through the operation mechanism and realizes the fault clearing, isolation and a node for load transferring; the power distribution terminal is matched with the power distribution switch for collecting voltage and the current waveform of the operation mechanism, performing statistic on the motion times of the switch, collecting the switch position signals, realizing the pre-processing and initial determination of the information, and uploading the data to the main station waveform recording and analysis system; and the main station waveform recording and analysis system receives the waveform transmitted by the power distribution terminal. The invention can retrieves the waveform of the operation mechanism of any power distribution switch and the machinery characteristic determination result on the power distribution terminal by the power distribution switch and realizes the fault alarm and fault positioning of the operation mechanism of the power distribution switch in order to provide the convenience to the maintenance staff to maintain.

The invention discloses a vehicle fault early warning method and a vehicle fault early warning device. The method includes acquiring current travelling data of a vehicle, wherein the travelling data comprise variable values of at least one parameter; comparing the current travelling data of the vehicle with a normal travelling numerical value range and an abnormal travelling numerical value range of the vehicle, and determining a numerical value range to which the traveling data belong; generating corresponding early warning information when the travelling data are in the abnormal travelling numerical value range. By the vehicle fault early warning method and the vehicle fault early warning device, the technical problem that travelling safety of trains is influenced due to the fact that existing trains do not receive fault early warning information and hidden danger of faults cannot be cleared away before fault occurrence is solved.

The invention discloses a big-data-based method for analyzing the state-based maintenance of subway signal equipment. The method includes: an analysis processing device receives the operation state information, transmitted by each subsystem in a subway signal system, of each signal equipment in each subsystem in a preset time period and the maintenance record information, transmitted by a maintenance support system, of each signal equipment; the operation state information and the maintenance record information of all signal equipment are stored in a distributed manner; when the state-based maintenance of at least one signal equipment is analyzed, the operation state information and the maintenance record information of the signal equipment are searched from a HDFS according to the identification of the signal equipment; acquiring the fault probability of the signal equipment according to the searched operation state information, the searched maintenance record information and a preset formula. By the method, the use, maintenance and replacing of the signal equipment can be monitored, the fault rate of the signal equipment can be lowered effectively, and the safety operation of the signal equipment is guaranteed.

The invention relates to the field of low-voltage distribution equipment, in particular to a miniaturized intelligent online monitoring device for low-voltage distribution equipment. The intelligent online monitoring device comprises a sensor unit, a processor unit, a wireless transmission unit, a power management unit and a storage unit, wherein the processor unit is connected with the sensor unit, the wireless transmission unit, the power management unit and the storage unit separately; and the processor unit is connected with the sensor unit, receives running state information and carries out storage, management and processing operation. The invention further relates to a monitoring method for the low-voltage distribution equipment. Compared with the prior art, the miniaturized intelligent online monitoring device has the beneficial effects that the information, such as temperature of incoming and outgoing lines, line current and voltage and smoke of the low-voltage distribution equipment is monitored online in real time through a wireless Internet of things and a novel sensing technology, and real-time computational analysis is carried out, so that fault early warning of a low-voltage distribution system is achieved and a fire accident is completely eradicated.

The invention discloses an intelligent roller structure of a belt conveyor. The structure comprises a roller shell, a roller shaft and detection mechanisms, wherein the roller shaft penetrates throughthe roller shell and is connected with the roller shell through two first bearing assemblies, and one group of detection mechanism is arranged on the inner side of each first bearing assembly; the first bearing assemblies comprise first bearing blocks, and the first bearing blocks sleeve the roller shaft; the detection mechanisms comprise circuit boards, vibration sensors, temperature sensors and microphones, wherein the temperature sensors are connected to the outer walls of the first bearing blocks, first positioning rings sleeve the positions, on the inner sides of the first bearing blocks, of the roller shell, and the vibration sensors and the temperature sensor are connected to the first positioning rings; and stators and rotors are arranged at the inner ends of the first positioning rings, the stators are connected to the roller shaft in a sleeve mode, the rotors are located outside the stators and are connected with the roller shell through first connecting assemblies, and circuit boards are arranged on the positions, in the rotors, of the roller shaft. According to the roller structure, a roller state monitoring unit is installed inside the roller structure in an embeddedmode, the structure is novel, and the creativity is high.

The invention discloses an AR technology-based intelligent power distribution station operation and maintenance system and method. The system comprises an AR glasses end and a cloud, management and end layer integrated in the AR glasses end. The AR glasses end comprises an information acquisition module, a security encryption module, an application program and a communication module. The cloud layer is used for acquiring and processing information and performing full-dimensional perception and prediction on the power distribution station, and is integrated in the information acquisition module; the pipe layer is used for providing a data transmission channel for the cloud layer and the end layer based on an information security protocol and encrypting transmission data, and is integrated in the security encryption module; the end layer is used for providing basic data in the operation and maintenance process for the cloud layer and obtaining an operation and maintenance decision schemeaccording to information acquired by the cloud layer, realizing an end layer function based on the application program; and the cloud layer, the pipe layer and the end layer are sequentially connected from top to bottom through a wired or wireless communication line.

The invention discloses a safety stereoscopic monitoring cloud platform, system and method for a hospital central air conditioner. The safety stereoscopic monitoring cloud platform is used for receiving real-time operating data of the central air conditioner, real-time operating data of electrical equipment and safety factor data of the ambient environment of the electrical equipment sent from anintelligent monitoring terminal, generating alarm information according to a received operating state of data monitoring equipment when the data are abnormal, sending an image acquisition instructionto the intelligent monitoring terminal, receiving current image data of the equipment with the abnormal data, and sending the alarm information and the image data to a user terminal. According to thesafety stereoscopic monitoring cloud platform, system and method for the hospital central air conditioner, the operating data of the equipment and the safety factor data of the ambient environment areacquired through the intelligent monitoring terminal, so that stereoscopic monitoring the hospital central air conditioner is achieved, an alarm is given in advance before an accident happens, and safe and stable operation of the hospital central air conditioner is ensured.

The invention discloses an undersea sealing system, a tidal power generation unit applying the same and an application method. The undersea sealing system comprises a movable sealing member and sealing bodies, and the movable sealing member and the sealing bodies sleeve a rotating member. Pressure cavities are formed on two sides of the movable sealing member by the sealing bodies. Sealing connection between the movable sealing member and the rotating member is enhanced through differential pressure between the pressure cavities on the two sides of the movable sealing member. The undersea sealing system is good in sealing performance, safe and reliable, not easy to wear and long in service life. Transmission shafts and cabins of the undersea tidal power generation unit applying the undersea sealing system can be effectively sealed in a long time. Sealing states can be transmitted to an offshore monitoring center through pressure sensors, sealing conditions of the undersea unit can be monitored in real time, and early warning and timely maintenance can be realized.

The invention discloses a train storage battery pack fault early warning system and method. The system comprises a storage battery pack, a charger, a train control and management system, a data processing unit and a fault early warning unit. The charger uploads the real-time state data of the storage battery pack to the train control and management system. The train control and management system sends the real-time network data to the data processing unit. The data processing unit analyzes the received real-time network data and extracts real-time state data of the storage battery pack from the real-time network data. The fault early warning unit analyzes the extracted real-time state data of the storage battery pack to obtain the state of the storage battery pack and outputs fault early warning prompt information. The technical problems that an existing train storage battery pack fault early warning method is insufficient in coverage, early fault symptoms are difficult to find, and early fault early warning cannot be achieved can be solved.

The invention provides an RFID (Radio Frequency Identification Devices) elevator monitoring control panel. The RFID elevator monitoring control panel is provided with an RFID card reader, a human-face fuzzy recognizer, a wireless communication part, an infrared terminal, a radio transmission part, a CPU (Central Processing Unit) and an interface control panel. The RFID elevator monitoring control panel disclosed by the invention can be used to realize the functions of uploading real-name information of elevator mounting points, reporting faults of a remote monitoring fixed-point position, automatically alarming for calling a help, reporting the maintenance work of maintenance personnel, carrying out video monitoring on the maintenance work of the maintenance personnel, carrying out detailed management on a maintenance work flow and the like.

The embodiment of the invention discloses an automatic modeling method and system for equipment fault early warning, and relates to the technical field of equipment fault early warning, and the methodcomprises the following steps: obtaining original data, and carrying out the backup of the original data; performing data preprocessing on the original data based on a first digital filtering algorithm; based on a second digital filtering algorithm and a clustering algorithm, performing data filtering on the preprocessed data, and storing a filtering condition; based on the clustering algorithm,performing pattern division on the filtered data, and storing a pattern division condition; obtaining and storing the health data model after mode division; and adjusting parameters of an algorithm byanalyzing the filtering condition, the mode division condition and an early warning result obtained based on the health data model. Compared with the existing modeling process, the embodiment of theinvention is clearer in logic, higher in modeling speed and more accurate in data filtering effect, is driven by data, and does not need complex system and service analysis.

The invention belongs to the technical field of automobile electronics, and relates to an automobile fault diagnosis and monitoring system and method based on an IVI host. The system comprises an IVIhost, a controller, a gateway, a TBOX and a cloud server. The IVI host is connected with the controller through the gateway and a bus; the IVI host is connected with the TBOX through a USB or an Ethernet. The TBOX sends information to the cloud server through a 4G or 5G network; the cloud server stores a fault diagnosis process and deploys a remote fault diagnosis system; fault diagnosis, data monitoring and fault early warning of the controller are realized through the IVI host; a fault code and a fault analysis code are displayed through a central control display screen; automobile running parameters are displayed in a data stream or graphical form through the central control display screen; an expert system of the cloud server judges the automobile running state according to the automobile real-time running data and the fault code; fault diagnosis query is realized through a human-computer interaction interface of the central control display screen, and fault code reading or vehiclerunning state monitoring can be realized without special diagnosis equipment.

The invention discloses a prediction method for a machine fault. The method comprises the following steps of using plurality of kernel functions to create a multi-core combined relevance vector machine model cluster; respectively inputting the signal characteristics of a field signal, the signal characteristics of a normal signal and the signal characteristics of the fault signal of a machine into a multi-core combined relevance vector machine model cluster; generating a model particle set corresponding to the signal; performing initialization weight operation, iterative prediction operation,weight updating operation and resampling operation on each model particle in the various model particle sets; screening out model particles with the largest number of particles to serve as a multi-core combined relevance vector machine with optimal corresponding signals; and finally, generating a corresponding map according to the optimal multi-core combined correlation vector machine, and selecting the map with the highest similarity with the field signal map as a prediction result of the machine fault by judging the similarity between the field signal map and the normal signal map and the similarity between the field signal map and the fault signal map, thereby realizing fault early warning of the machine.

The present invention discloses an early warning method and system of shield construction failure based on association rules. The method comprises the following steps: S1 acquiring shield design data,a geological type, construction data and warning data in historical projects; S2 pre-processing the construction data and encoding the geological type and a warning type corresponding to the pre-processed construction data; S3 respectively acquiring an association rule of the warning type for the construction data of different shield models under different geological types according to the encoded geological type and the warning type; and S4 querying the association rule of the warning type according to the warning type in the current shield construction, and performing an early warning on afailure to be generated, to complete the early warning of the shield construction failure. The method can effectively help construction personnel to correct improper construction operation in time ormake corresponding emergency measures in advance according to the early warning of the failure to avoid the occurrence of the failure or shorten failure processing time and effectively improve the efficiency of the shield construction.

The invention discloses a contactless elevator taking system. The contactless elevator taking system comprises a cloud server, an elevator gateway and an elevator controller, wherein the cloud serveris in communication connection with the elevator gateway through a mobile communication network, and the elevator gateway is in communication connection with an Internet of Things communication port of the elevator controller. The invention further discloses a contactless elevator taking method. By the adoption of the contactless elevator taking system and the contactless elevator taking method, auser can automatically take an elevator without making contact with elevator keys, implementation and operation are easy, and the safety of the elevator taking process is improved.

The invention discloses a Profibus-DP photoelectric signal conversion system. The Profibus-DP photoelectric signal conversion system comprises a photoelectric converter, a data receiving and transmitting logic control circuit and an electric isolation and differential signal conversion circuit, wherein the photoelectric converter receives optical signals and converts the optical signals into TTL level signals, or receives the TTL level signals and converts the TTL level signals into the optical signals; the data receiving and transmitting logic control circuit comprises a three-state buffer connected with the photoelectric converter, and a receiving and transmitting control end of the three-state buffer is sequentially connected with a delay circuit and a first Schmitt inverter; the electric isolation and differential signal conversion circuit comprises a differential level converter, the differential level converter is connected with the photoelectric converter and is used for receiving and transmitting the TTL level signals, the differential level converter is connected with an output end of the first Schmitt inverter and is used for receiving, transmitting and controlling temporal logic signals, and an output end of the differential level converter is connected with a Profibus-DP bus and is used for receiving and transmitting Profibus-DP differential signals. The Profibus-DP photoelectric signal conversion system provided by the invention has greater real-time performance and flexibility.

The invention discloses a lighting lamp fault warning method. The method comprises the following steps: a sensor obtains at least one internal environmental parameter of the temperature of the lighting lamp, the humidity inside the lighting lamp and the brightness of the lighting lamp, and sends the parameter to a processor; The processor compares at least one of the temperature of the lighting lamp, the humidity inside the lighting lamp and the brightness of the lighting lamp acquired by the sensor with a corresponding preset parameter value range. The processor sends an early warning signalwhen at least one of the temperature of the lamp, the humidity inside the lamp, and the brightness of the lamp exceeds the preset parameter value range. At least one of the temperature of the lightinglamp, the humidity inside the lighting lamp and the brightness of the lighting lamp is compared with a corresponding preset environmental parameter value, whether the lighting lamp is abnormal is judged, and an early warning signal is sent to the manager. The invention also provides an illumination lamp and an early warning control system.

The invention discloses a sulfur hexafluoride gas leakage monitoring system based on the internet of things. The system comprises a detection unit, a main controller, a cloud platform management unit,a user terminal and a plurality of alarm units and fans, wherein the detection unit is used for collecting gas concentration information and feeding back the gas concentration information to the maincontroller; the main controller controls the alarm units and the fans to realize alarming and exhausting; meanwhile, in combination with time, the gas concentration information and alarming and exhausting information are sent to the cloud platform management unit to be stored and are sent to the user terminal; and a user performs lookup management through the cloud platform management unit through the user terminal. The corresponding acousto-optic alarms and fans can be started through monitoring data, so that the gas concentration in space is reduced, the risk of entering the space by working personnel is reduced, the centralized and one-stop management of system equipment and data is achieved, and a solid foundation is laid for establishing an automatic, informationized, networked and normalized SF6 detection alarm type device based on the internet of things.

The invention discloses a server fault monitoring method and system based on a density clustering algorithm, belongs to the technical field of server fault monitoring, and aims to solve the technicalproblem of how to perform fault monitoring and early warning on a server and reset a downtime fault through the density-based clustering algorithm. The method comprises the following steps: acquiringhealth information data of a server through a BMC and constructing a sample; normalizing the sample data; constructing a monitoring model based on a DBSCAN algorithm, optimizing parameters of the monitoring model, and obtaining a trained monitoring model; analyzing the sample data in the test sample through the trained monitoring model and outputting a monitoring result; if the server has a faultin the monitoring result, feeding back the monitoring result is to a to-be-displayed web page, and if the server crashes, resetting the server through BMC. The system comprises a data acquisition module, a data preprocessing module, a model training module and a result output module.