134 results about "Fault avoidance" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention discloses a high and low voltage power distribution cabinet operation monitoring system based on data analysis, and relates to the technical field of power distribution cabinet operation monitoring, the system comprises an operation monitoring platform, the operation monitoring platform is in communication connection with a data acquisition module, a fault risk analysis module, a fault risk diagnosis module, a fault positioning module and a monitoring early warning module, the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing power distribution equipment data of the high-low voltage power distribution cabinet. According to the invention, through integration of the data acquisition module, the fault risk analysis module, the diagnosis module and the positioning module, comprehensive monitoring of the operation state of the high-low voltage power distribution cabinet is realized, a fault risk diagnosis model is established by using a deep learning algorithm, and potential fault risks are accurately identified in combination with real-time data and historical cases, so that the accuracy and timeliness of early warning are significantly improved, and the working efficiency of the high-low voltage power distribution cabinet is improved. And operation and maintenance personnel can intervene in advance according to the early warning information and take preventive measures, thereby effectively avoiding faults and ensuring stable operation of the power system.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention provides a mechanical equipment fault diagnosis model construction method, a diagnosis method, a system and equipment, and relates to the technical field of mechanical fault diagnosis, and the fault diagnosis method comprises the steps: obtaining the vibration signal data of a rotating machine in real time; and inputting the vibration signal data into the fault diagnosis model to obtain the fault type of the rotating machine. According to the method, the specific fault type of the rotating machine can be accurately identified, so that measures can be taken in time for repair or maintenance, further deterioration of faults is effectively avoided, equipment safety is protected, production efficiency is improved, and maintenance cost is reduced.

The invention discloses a gas compressor blade state monitoring method and system based on a health management platform, and relates to the technical field of gas turbines. Comprising the following steps: acquiring real-time operating parameters of the gas turbine, and transmitting the real-time operating parameters to a health management platform at a fixed sampling frequency; performing data preprocessing and feature extraction on the real-time operation parameter data; calculating the performance parameter degradation quantity of the gas turbine according to the real-time operation parameter data; performing blade fault identification according to the parameter degradation quantity and a preset fault diagnosis criterion; calculating a fault early warning index through the real-time operation parameter data, and judging whether the gas turbine is in a normal operation condition or not; and judging a fault early warning level according to the parameter degradation quantity and sending out a corresponding early warning signal. According to the method, the blade fault type can be accurately predicted, false alarms and false alarms can be reduced, meanwhile, early warning level signals are sent out, measures can be taken, fault expansion is avoided, and downtime and maintenance cost are reduced.

The invention relates to the technical field of intelligent power distribution network automatic protection, and discloses a power distribution network fault strategy generation method, system and device based on a self-healing loop graph, and a medium, and the method comprises the steps: constructing a power distribution network topological graph, and recognizing a self-healing loop; generating a cooperative protection constant value based on the incidence relation of the dynamic correction path impedance and the protection device; a delay parameter is adjusted through conflict detection of the dynamic impedance track and the high-frequency phase sequence; simulating a fault action to generate an SOE event chain; identifying an abnormal time delay combination in combination with historical time delay distribution and marking conflicting nodes; adjusting parameters for the second time to generate a self-healing strategy matched with the topology; through multi-dimensional time sequence conflict detection and closed-loop feedback optimization, precise control of cooperative action of the protection device is realized, fault isolation delay and maloperation of a non-fault area are effectively avoided, and self-healing reliability and power supply recovery efficiency in a complex power distribution network scene are improved.

The invention relates to the technical field of safety valve fault monitoring, in particular to a safety valve full-life-cycle fault detection method and system, and aims to solve the problems that sudden safety accidents occur frequently due to the fact that a traditional method depends on leakage flow monitoring and cannot capture hidden faults such as valve element jamming and spring fatigue, a power supply system mostly adopts a single power generation mode, and the power consumption is low. In a low discharge scene, the electric quantity of the energy storage module is used up, and monitoring interruption is caused. According to the invention, the problems of safety valve hidden fault monitoring deficiency and unstable power supply are effectively solved. By means of multi-dimensional data collection, spring elasticity attenuation and valve element movement abnormity can be found in advance, and faults are prevented from being evolved into serious leakage. The composite power supply system maintains basic monitoring in the non-discharge stage, energy is quickly supplemented in the discharge stage, and continuous operation of the whole life cycle is guaranteed. A model analysis result can guide maintenance personnel to accurately position a fault part, and unnecessary shutdown maintenance is reduced.

The invention discloses a transformer protection method and device applied to an oiled paper capacitor type bushing, and relates to the technical field of electric power, and the method comprises the following steps: collecting pressure data and temperature data in the bushing in real time; performing fault analysis according to the pressure data and the temperature data to obtain a fault analysis result; the fault analysis result is that the equipment has a fault or does not have a fault; when the fault analysis result is that the equipment has a fault, a tripping signal is sent out; the tripping signal is used for controlling the transformer of the bushing to perform tripping operation. According to the application, whether the equipment has a fault is judged through the pressure data and the temperature data in the bushing acquired in real time, and the tripping signal is sent out when the equipment has the fault is analyzed, so that the transformer of the bushing is controlled to execute the tripping operation, further expansion of the fault is avoided, and the safety of the equipment and the safety of a related operating environment are guaranteed.

The present invention relates to the technical field of fault prediction of wind turbines, and discloses a fault prediction system for wind turbines, including a wind turbine environmental data acquisition module, a wind turbine internal data acquisition module, a fault analysis module, and a fault warning scheduling module. The system preliminarily analyzes by collecting and sending electrical data and transmission data, and judges whether to report a fault. If no warning is given, an internal fault factor is output. Then, in combination with the altitude and the number of impacts on the blades of the wind turbine, an environmental fault prediction factor is analyzed and calculated. After obtaining the environmental fault prediction factor, combined with the internal fault factor, a comprehensive fault factor is obtained. Based on the comprehensive fault factor, a secondary fault prediction judgment is carried out, which can provide strong support for the fault warning scheduling module, so as to be able to identify wind turbines with higher fault risks and ensure avoiding safety accidents caused by faults at critical moments.

The invention discloses a power grid application data protection system and method based on an active-active architecture, and the system comprises a power grid data scheduling server, a power grid scheduling center unit, a core storage center, a data monitoring module, and a plurality of remote storage centers. The power grid dispatching center unit can transmit electric energy information data to the power grid data dispatching server, and the data monitoring module can send out an uploading fault alarm according to the uploading state of the electric energy information data and monitor the server states of the core storage center and the multiple remote storage centers based on a weighted polling algorithm. When any storage center breaks down, the network monitoring device immediately sends out a system fault alarm and transmits fault information to the power grid data scheduling server, and the power grid data scheduling server can select a method based on an active-active mechanism standby storage center. And one remote storage center is selected from the multiple remote storage centers to serve as a new core storage center, so that the problem of data loss caused by faults is avoided.

Disclosed in the present application are a fault detection method, a fault detection apparatus, a computer device, and a storage medium. The method comprises: on the basis of the opening degree of a flow control apparatus and a preset mapping function, calculating a first flow coefficient; on the basis of a first operating parameter of the flow control apparatus and a second operating parameter of a compressor, calculating a second flow coefficient; and on the basis of a deviation between the first flow coefficient and the second flow coefficient, performing fault detection on the flow control apparatus, so as to determine a fault detection result. By comparing a flow coefficient, which is obtained by means of real-time detection, with a theoretical flow coefficient to obtain a deviation therebetween, fault detection can be performed on a flow control apparatus in a timely manner, which is conducive to avoiding the impact of a faulty component in a timely manner, thereby improving the stability and reliability of an air conditioning system.

The invention provides a hydraulic power plant system oscillation fault processing method and device, and relates to the technical field of power grid fault processing, and the method comprises the steps: monitoring the operation data of a hydraulic power plant, and determining an oscillation state according to the operation data; determining an oscillation type and an oscillating unit in response to oscillation represented by the operation data; determining oscillation reasons and implementing corresponding emergency measures, and adjusting operation parameters of a speed regulator, an excitation system and an auxiliary engine system in the target unit; and rechecking the target unit to determine the running state. Oscillation is found in time by monitoring operation data of the hydraulic power plant, corresponding fault processing measures are taken, effective monitoring and management of the power plant are achieved, normal operation of the power plant is prevented from being affected by faults, and operation safety of the power plant is improved.

Current data from a SaaS system and associated remote data storage system is used to automate proactive fault avoidance. Machine learning models are used to predict faults. Rules are used with a rules engine to calculate corresponding fault avoidance recommendations. The model and rules are trained and created using source data from the SaaS system and remote data storage system with which the model and rules will be used. Current data from those systems is used to predict future faults and calculate recommendations to avoid the predicted faults. Implementation of a recommendation triggers a re-test to determine whether the predicted fault is still likely to occur.

The invention discloses a power distribution control method and system based on an urban network, and relates to the technical field of power distribution control, and the method comprises the steps: monitoring the operation parameters of a power grid in a governed power distribution region in real time at an edge calculation terminal disposed at the edge side of a power distribution network; and when it is monitored that the power grid operation parameters are abnormal, determining fault occurrence and a specific position of a fault section based on a preset fault diagnosis model. According to the method, the edge computing terminal is deployed on the edge side of the power distribution network, real-time monitoring of the operation parameters of the power grid in the jurisdiction area is achieved, when the parameters are abnormal, the fault section can be rapidly positioned by means of the preset fault diagnosis model, the fault response speed is greatly improved, and the problem that decision-making time is long in a traditional centralized master station mode is solved. The edge computing terminal can directly generate an instruction isolation fault, and the fault range is prevented from being expanded.

The invention provides an axial flux motor distributed driving safety redundancy system which comprises a safety monitoring module, a fault diagnosis module, a fault early warning module, a main controller module, a redundancy controller module and a motor module. The signal end of the fault diagnosis module is connected with the signal end of the fault early warning module and the signal end of the safety monitoring module. According to the method, the data of multiple sensors are fused, the fault condition of the motor is predicted by comprehensively analyzing the data, the prediction accuracy is improved, the sensor data are trained and learned by adopting the fault prediction algorithm to identify the characteristics and modes of the motor fault, and the fault prediction accuracy is improved along with continuous accumulation of the data and continuous optimization of the algorithm. Compared with the prior art, the accuracy of the fault prediction algorithm can be continuously improved, and the method enables a driver or a maintainer to have enough time to take measures to solve the problem by finding potential fault risks in advance and sending out an early warning signal, thereby avoiding the occurrence or expansion of the fault.

The invention discloses a 5G-TSN (Time Sensitive Network) clock synchronization error prediction method based on LSTM (Long Short Term Memory), and belongs to a clock synchronization technology of a time sensitive network (TSN) and 5G (5th Generation) mobile communication. According to the method, the synchronization error trend can be captured in the 5G-TSN clock synchronization process, and an effective prediction mechanism is provided for clock synchronization regulation and control and fault prevention. Compared with the existing research on 5G-TSN clock synchronization, the invention proposes that an error prediction mechanism is introduced into a 5G-TSN clock synchronization process for the first time, and a monitoring mechanism is established for advanced calibration regulation and fault avoidance of clock synchronization.

The invention provides a soft start cooperative control method and system for a large industrial air conditioning unit, and the method comprises the steps: a central cooperative controller collects the operation parameters and start dependence conditions of all parts through a high-speed real-time communication network, builds a fault feature database, and carries out the real-time recognition of an abnormal signal; a fault tree analysis module is adopted, faults are divided into three levels according to the types and severity of the faults, if communication interruption occurs, a hardware watchdog circuit of a safety protection unit executes emergency shutdown within 500 ms, out-of-control is prevented, and the method relates to the technical field of large-scale industrial air conditioning unit control. The fault grade and the influence range are dynamically evaluated, rapid positioning and collaborative response of faults are achieved, fault spreading is avoided, the safety and reliability of the large industrial air conditioning unit starting process are improved, the problems of power grid impact and system reliability during multi-device starting are solved, and the method is suitable for large air conditioning units of data centers, factories and the like.

The invention belongs to the technical field of fault judgment, and discloses a wind turbine generator system fault judgment method based on a Relief algorithm and a related device. The wind turbine generator system fault judgment method based on the Relief algorithm comprises the steps of selecting characteristic quantities and preprocessing the characteristic quantities, iteratively calculating the weight of the preprocessed characteristic quantities according to the Relief algorithm to obtain a characteristic variable set, establishing a regression model between the average wind speed and the characteristic variable set and calculating the regression model, obtaining a normal reference characteristic quantity state of the wind turbine generator system under the real-time state data, and judging whether the wind turbine generator system has a fault or not through a state judgment algorithm according to the real-time state data and the normal reference characteristic quantity state of the wind turbine generator system under the real-time state data; the abnormal condition of the wind turbine generator system can be found in time, an early warning mechanism is triggered, the early warning accuracy is remarkably improved, and occurrence or expansion of faults is avoided.

The invention discloses processing and feeding equipment for an organic electroluminescence flexible transparent display screen and a using method, and relates to the technical field of display screen processing equipment, the equipment comprises a bottom plate, and two sets of self-locking universal wheels are installed on the bottom face of the bottom plate. According to the invention, a current sensor and a vibration sensor are installed, the working state of a motor is accurately monitored in combination with a motor fault judgment algorithm, a motor fault is accurately identified in an early stage, and fault deterioration is avoided; the defects on the surface of the display screen material and the material blocking condition are found in time, the product quality problem and the material loss risk caused by the fact that the fault is not found in time are reduced through the comprehensive and accurate fault detection capacity, and the controller automatically adjusts the working parameters of the motor when the motor breaks down. According to the fault processing mechanism, fault expansion is effectively avoided, material waste and further damage to equipment are reduced, and therefore the production cost is reduced.

The invention provides a method for reducing the failure rate of a unit air cooling island speed reducer. The method comprises the following steps that S1, a multi-module cooperative prevention and control system is built; s2, configuring a parameter monitoring module assembly; s3, standardizing the installation standard of the sensor; s4, building a data transmission and analysis platform; s5, starting an intelligent diagnosis and data tracing function; s6, executing a monitor calibration and maintenance process; s7, carrying out preventive maintenance operation; s8, starting a working condition linkage adjusting mechanism; s9, activating an operation and maintenance training auxiliary module; and S10, a supplement lubricating oil leakage monitoring link. According to the method for reducing the failure rate of the unit air cooling island speed reducer, full-dimension real-time monitoring and intelligent diagnosis of operation parameters of the speed reducer are achieved by building a multi-module cooperative prevention and control system, and by combining graded early warning and preventive maintenance, fault hidden dangers are accurately positioned and handled; the working condition linkage adjusting mechanism can actively adjust the operation state when parameters are abnormal, and fault expansion is avoided.

The invention belongs to the technical field of electrical equipment. The intelligent reclosing type circuit breaker control system comprises a scene monitoring unit, a data analysis and decision-making unit, a circuit monitoring unit and a self-adaptive control unit. According to the control system, real-time and planned operation data are combined, dynamic fault critical characteristics are generated and integrated and analyzed, and precise regulation and control of the circuit breaker are achieved. Therefore, timely opening can be realized to avoid faults, timely closing can be realized to restore power supply, misoperation and power supply interruption are reduced, circuit safety and power supply continuity are improved, and the circuit is adaptive to complex load scenes.

This application provides a method, apparatus, and storage medium for collecting memory fault information, relating to the field of data storage, and capable of preventing missed fault information reports. The method is applied to a server, which includes memory, an out-of-band controller, and an in-band controller. The method includes: during server operation, the in-band controller performs fault detection on the memory and sends the detected fault information to the out-of-band controller; during server restart, if the server's startup mode is a cold restart, the in-band controller determines whether new fault information exists in the memory, wherein the new fault information is fault information not stored in the out-of-band controller; if new fault information exists in the memory, the in-band controller sends the new fault information to the out-of-band controller.

The invention relates to the technical field of industrial automation control, and discloses an auxiliary control workshop control system based on a DCS, and the system comprises a process topological graph construction module which is used for mapping a physical workshop into a dynamic graph containing an equipment digital object in real time; the task analysis module is used for converting the advanced task instruction into a quantitative target state set on a graph; the dynamic path planning module is used for automatically searching and generating an optimal atomic operation instruction sequence for reaching a target state based on the real-time state of the map; and the instruction execution and transaction management module and the exception handling and secure rollback module are used for ensuring atomic execution of the instruction sequence and secure rollback during failure. According to the method, a process control problem is converted into a graph theory path planning problem, so that the control system has the capabilities of dynamically avoiding faults and autonomously deciding, and the automation level, the reliability and the safety of the operation of the auxiliary control system are remarkably improved.

The present invention discloses a fault repair method, device, electronic device and storage medium. The method comprises: if it is in the current extensible firmware interface pre-initialization stage, then obtaining the base address of the preset base address register of the H2B device; obtaining the fault address information and the first information check value according to the base address; wherein the fault address information and the first information check value are stored in the preset storage location corresponding to the preset base address register; performing a preset check operation on the fault address information to obtain the second information check value, and determining whether to perform the fault repair operation according to the first information check value and the second information check value. By running the technical solution provided by the embodiment of the present invention, fault handling is performed without entering the SMM mode; and it can avoid the problem that when the fault address information changes after storage, the fault handling cannot be performed, or the fault is not handled correctly if the fault address information is still used, thereby improving the accuracy and effectiveness of fault handling.

The invention discloses a wind facing deviation identification method and device for a wind turbine generator, electronic equipment and a storage medium, and belongs to the technical field of wind turbine generator fault diagnosis, and the method comprises the steps: obtaining historical fault signals of a wind direction sensor of the wind turbine generator, generating a fault signal data set, and counting fault features of the wind direction sensor of the wind turbine generator; carrying out dimensionality reduction processing on the fault signal data set according to the fault features to obtain a fault signal feature set, carrying out training calculation on the fault signal feature set, generating a wind turbine deviation fault classifier, collecting to-be-identified signal data of a wind direction sensor of a wind turbine generator in real time, carrying out feature extraction on the to-be-identified signal data, and carrying out similarity calculation to obtain a wind turbine deviation fault classifier; according to the calculation result, the fault class of the to-be-identified signal data is judged, signal fault identification of the wind direction sensor of the wind turbine generator is completed, the situation that the faulted wind direction sensor cannot be found due to long-time fault operation can be avoided, power generation loss is reduced, and the service life of a fan is prolonged.

The present invention discloses a method for locating faults in an automobile engine based on data analysis, belonging to the technical field of automobile fault detection, including Step 1: formulating the normal operation data range of each monitoring item of the engine under different environmental condition values; Step 2: judging whether the operation data of each monitoring item under this environmental condition value is normal according to the obtained operation data information; Step 3: when the operation data of the monitoring item is abnormal, generating a corresponding fault alarm and determining the danger level of the fault; Step 4: when the operation data of the monitoring item is normal, predicting whether there is a potential fault risk for each monitoring item. When the engine has not failed, the present invention predicts whether there is a potential fault risk for each monitoring item according to the historical accumulation of the operation data at the positions of each monitoring item, so as to timely remind the user to perform corresponding inspections and repairs on the corresponding positions, thereby avoiding the occurrence of faults.

The application discloses a fault distance measurement method and system for an electrified railway AT power supply mode, relates to the technical field of fault distance measurement, determines whether load current is contained in fault current according to impedance angle, and calculates fault current without containing load current; that is, the mixed load current is removed in the fault current calculation process, so that the accuracy of the fault current is ensured; the fault type is judged based on the self-coupling variable absorption current direction characteristics, so that the fault type judgment error caused when the fault occurs at the end section of the traction network is avoided, the fault type identification accuracy is greatly improved, and the fault distance measurement precision is improved.

The application discloses a vehicle fault processing method, a battery cooperative controller and a vehicle. The vehicle fault processing method is applied to the battery cooperative controller which is connected with a high-voltage battery and a low-voltage battery. The vehicle fault processing method comprises the following steps: obtaining fault diagnosis data of a plurality of function management units in the battery cooperative controller, wherein the plurality of function management units comprise a low-voltage battery management unit, a high-voltage battery management unit, a voltage conversion unit and a charging communication unit; performing fault cooperative decision on the fault diagnosis data to obtain a global fault processing scheme; and controlling the plurality of function management units to perform corresponding fault processing operations according to the global fault processing scheme. The application realizes fault information sharing and cooperative processing across subsystems, can avoid problems such as fault island and decision conflict, and improves the consistency and real-time performance of fault response.

The invention discloses a motor anomaly detection early warning method based on big data, and relates to the technical field of motor detection, multi-index historical data of a target motor is obtained, indexes comprise a preceding index and a lagging index, and the occurrence time of each index anomaly value is recorded; the method comprises the following steps: segmenting historical data, dividing data intervals according to fault events, and calculating a time difference between a preceding index anomaly and a lagging index anomaly in the same interval to form a time delay sample set; and a time delay probability distribution model is constructed based on the sample set, and normal time delay ranges corresponding to different index combinations are determined. Through the real-time monitoring and abnormity determination module, fault expansion can be avoided, motor operation safety and reliability can be improved, and loss caused by faults can be reduced.

This application provides a method, device, apparatus, and storage medium for selecting computing power nodes. The method includes: acquiring multiple undetermined service requests received through the same entry computing power routing node; and determining, based on whether the senders of the multiple undetermined service requests are the same user, selecting target computing power nodes that meet the requirements of each undetermined service request according to a first rule or a second rule. This application addresses specific application scenarios by prioritizing the processing of multiple service requests from the same user using the same or similar computing power nodes, thereby achieving parallel data forwarding, reusing transmission resources, and improving resource utilization. Furthermore, by using different computing power nodes belonging to different computing power routing nodes to process service requests of the same type from multiple users as much as possible, it avoids service paralysis across the entire region due to faults, thus accurately and specifically selecting the optimal computing power node for multiple concurrent computing power services in specific application scenarios.