2121 results about "Failure rate" patented technology

The invention discloses a power distribution cabinet fault detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: laying a multi-modal sensor array, constructing a multi-dimensional data collection network, collecting various types of sensing data in real time, and carrying out the data preprocessing; establishing a multi-modal data fusion model by using a deep learning algorithm, and forming a fault feature mapping relation through feature extraction, data correlation analysis and cross validation to realize collaborative perception of a data level; and constructing a multi-dimensional fault diagnosis decision tree based on a collaborative perception result, accurately positioning a specific device position where a fault occurs in combination with weight distribution and confidence evaluation of data of each sensor, and giving out evaluation results of a fault type and severity. The system effectively reduces the equipment failure rate, prolongs the service life, reduces the maintenance cost, improves the power supply reliability, promotes the development of an electric power system towards the intelligent operation and maintenance direction, and is of great significance to the improvement of the operation reliability and maintenance efficiency of a power distribution system.

The invention relates to the technical field of industrial equipment scheduling, in particular to a HarmonyOS industrial equipment intelligent scheduling and real-time cooperative control method, which comprises the following steps: S1, industrial equipment autonomous behavior design and dynamic role switching based on bionic swarm intelligence; by introducing bionic swarm intelligence, multi-modal perception, transfer learning and block chain technologies, the scheduling efficiency and task execution capability of industrial equipment in a complex dynamic environment are remarkably improved, and in the actual use process, the equipment can dynamically adjust task priority and resource allocation according to the real-time operation state and scene change, so that the service life of the equipment is prolonged. And meanwhile, environment data is collected by utilizing a multi-mode sensor, prediction and reasoning are performed through a machine learning model, and the equipment can pre-judge a fault risk and optimize a task chain execution scheme, so that the fault rate and energy consumption in the production process are reduced, and the production efficiency and stability are remarkably improved.

The invention provides an AI-based assembly state intelligent monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a multi-angle real-time assembly process image of a target product, constructing an assembly digital twin model, inputting the image into a hybrid neural network in combination with an assembly knowledge graph, analyzing the assembly node coordinate and torque data deviation, and obtaining an assembly state of the target product. And obtaining an optimal assembly path and torque control parameters, and finally generating an assembly quality evaluation matrix. Through fault feature extraction and mode recognition, potential faults are predicted, and a dynamic risk coefficient is calculated. And when the fault occurrence probability exceeds a preset threshold value, the position and torque compensation parameters are calculated, an execution instruction sequence is generated to control the assembly execution mechanism to perform adjustment, closed-loop optimization control in the assembly process is achieved, the assembly precision and efficiency are improved, and the fault rate is reduced.

The invention relates to the field of military insurance equipment diagnosis, examination and evaluation, and particularly discloses a military equipment maintenance support equipment diagnosis, examination and evaluation method, which comprises the following steps of: analyzing a performance index coincidence index of each piece of military insurance equipment by acquiring a diagnosis accuracy coefficient and a detection rapidity coefficient of the military insurance equipment; the fault rate, the average fault-free duration and the maintainability coefficient of each military insurance device are obtained, and the reliability index coincidence index of each military insurance device is analyzed; obtaining a maintenance period trend curve and a maintenance cost trend curve of each military protection device, and analyzing a maintenance index conformance index of each military protection device; further evaluating a comprehensive assessment evaluation index of each military insurance device; qualitative and quantitative analysis and comprehensive examination and evaluation are performed on the military equipment maintenance support equipment from multi-dimensional and multi-aspect indexes, so that comprehensive and objective diagnosis, examination and evaluation on the military equipment maintenance support equipment are realized, and a scientific basis is provided for model selection, use and improvement of the military equipment maintenance support equipment.

The invention provides a power equipment operation and maintenance state intelligent monitoring system, and relates to the technical field of power equipment operation and maintenance. According to the system, fault prediction and remote control are realized by monitoring the operation state of the power equipment in real time. The system comprises a data acquisition module, a data processing module, a fault prediction module and a remote control module. The data acquisition module is responsible for acquiring operation data of equipment, and the data processing module processes and analyzes the acquired data and extracts characteristic parameters related to the state of the equipment. And the fault prediction module performs fault prediction by using a machine learning algorithm based on the characteristic parameters, and provides early warning information for operation and maintenance personnel. And the remote control module allows operation and maintenance personnel to remotely monitor and control the equipment through a network, so that quick response and processing are realized. According to the system, through the functions of real-time monitoring, intelligent operation and maintenance, personalized configuration and the like, the operation and maintenance efficiency is effectively improved, the equipment failure rate is reduced, and remarkable improvement and promotion are brought to operation and maintenance management of power equipment.

The invention discloses a fault prediction and health management system, which comprises a data collection module, an equipment monitoring module and an equipment management and control module, and is characterized in that the data collection module is used for collecting state data in the operation process of medical equipment, historical fault data of the equipment and diagnosis data of a patient; the equipment monitoring module is used for detecting whether the running state of equipment is abnormal or not and predicting the failure rate of the equipment according to the running state of the equipment, and the equipment management and control module is used for recording historical maintenance data of the equipment, giving an alarm and managing the full life cycle and the part maintenance cycle of the equipment when the equipment fails. The data collection module, the equipment monitoring module and the equipment management and control module are electrically connected with one another, the data collection module comprises a historical data entry module, a data acquisition module and a diagnostic data entry module, and the data acquisition module is used for collecting equipment operation data.

The invention relates to the technical field of motor temperature intelligent control, and particularly discloses a motor temperature monitoring and regulation method and system based on intelligent control and a storage medium, and the method comprises the steps: carrying out data collection circulation: judging whether a sampling period is reached, if yes, reading multi-mode sensing data, and outputting a calibrated temperature value T, and if not, outputting a calibrated temperature value T; if yes, vibration noise is eliminated, the environment temperature is compensated, multimode sensing data is added, and then the calibrated temperature value T is output; intelligent analysis is conducted on the calibrated temperature value T obtained through data collection circulation, if the temperature is normal, data analysis and prediction are conducted, and if the temperature is abnormal, the calibrated temperature value T is analyzed, fault self-diagnosis is conducted, abnormal reasons are judged, the system is controlled to conduct self-adjustment, and temperature early warning is conducted. The problems that in the prior art, monitoring is delayed, regulation and control are rigid, and maintenance is passive, so that energy efficiency is wasted, the failure rate is high, and operation and maintenance cost rises are solved; and a real-time temperature monitoring means and an intelligent control system are lacked.

Methods, systems, and. devices may assist in new radio (NR) sidelink (SL) operation in licensed or unlicensed spectrum. In an example, if listen-before-talk (LBT) failure rate is low, then implicit acknowledgement (ACK) and explicit non-acknowledgement (NACK) may be used. A first SL timer may start when a. SL transport block is transmitted. If no explicit NACK, is received before the SL timer expires, then ACK. may be assumed. If LBT failure rate is high, then explicit ACK and implicit NACK may be used. A second SL timer may start. If no explicit ACK is received before the SL timer expires, then NACK may be assumed.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention relates to the technical field of data processing, and particularly discloses an improved inter-ERP (Enterprise Resource Planning) system data migration and synchronization method, which comprises the following steps of S1, metadata acquisition and preprocessing; s2, field intelligent matching; S3, machine learning recommendation; s4, mapping confirmation and feedback; s5, converting rule configuration; s6, carrying out total migration; s7, performing increment synchronization; and S8, monitoring and alarming: counting delay, throughput and failure rate of increment synchronization in real time, and automatically sending an alarm notification when an abnormal threshold is triggered. According to the method, a complete technical system which is infinitely infinitely and covers the whole migration and synchronous life cycle is constructed from metadata acquisition to intelligent mapping, rule driving and full / incremental ETL (Extract Transform Load), and then to monitoring alarm and rollback management; the method is suitable for efficient, reliable and extensible data migration and synchronization between heterogeneous ERP systems, and provides solid support for enterprise informatization upgrading, system splitting / merging and big data analysis enabling.

A cold rolling processing line-based digital steel coil real-time data tracking method and system. The method comprises: defining and configuring devices and positions on a cold rolling processing line, the configurations comprising position names and tracking modes, wherein the tracking modes include a first-stage tracking mode and a second-stage tracking mode; circularly traversing first-stage tracking message data and second-stage tracking message data, and matching each tracking position with material information; recording a material information change moment on each tracking position in real time to obtain the time of arrival and time of departure of a material at each tracking position; and meanwhile, on the basis of real-time production data of the material, in combination with the length by which and the moment at which the lead head of a steel coil passes the position, performing parameter calculation of the production data, including: length calculation, maximum / minimum average value calculation, multi-process standard hit rate calculation and alarm calculation. The method can accurately reflect actual production conditions, reduce the shutdown failure rate of the processing line, and greatly improve the product quality and the production efficiency.

The invention discloses an electric power material demand prediction analysis method and system based on big data analysis, and the method comprises the following steps: obtaining target electric power related data, and carrying out the preprocessing of the target electric power related data; based on the preprocessed target power related data, performing coupling analysis on the power grid equipment failure rate and the load fluctuation to obtain a candidate intervention variable set; performing screening operation on the candidate intervention variable set to obtain a final intervention variable set; and performing anomaly detection on the final intervention variable set, performing classification processing on anomalies according to the detected anomaly degree, dynamically adjusting a filling strategy through Bayesian optimization to minimize a data correction error, and finally outputting a cleaned training data set. According to the method, real causal variables are accurately screened through dynamic coupling analysis and triple causal verification, and the model prediction accuracy can be remarkably improved by combining a time domain-frequency domain joint analysis dynamic weight mechanism and an adaptive lagging order optimization technology.

The invention discloses a multidimensional monitoring system of an on-load tap-changer for a converter transformer and a fault prediction method, the system comprises a motor, a change-over switch core and other parts, and a data acquisition module is provided with a current, vibration and oil chromatography acquisition unit and a vacuum tube life monitoring unit. The current sensor collects motor current, the vibration sensor collects vibration signals of the change-over switch, the oil chromatography collection unit analyzes an oil sample, and the vacuum tube life monitoring unit evaluates the residual life of the vacuum tube. And a data transmission and processing analysis module. The fault prediction method comprises the steps of data acquisition, preprocessing, multi-dimensional data fusion, fault prediction model construction, feature extraction and selection, fault prediction and evaluation and the like. And screening characteristic parameters and fusion data by adopting a plurality of technical means, and constructing a machine learning model to predict faults. According to the invention, multi-source data acquisition and multiple analysis methods are integrated, the equipment state can be comprehensively reflected, the monitoring reliability is improved, faults can be predicted in advance, and the equipment fault rate is reduced.

The invention provides a flexible middle supporting structure of a vertical screw conveyor, which solves the problems of blanking buffering and the like of the screw conveyor, and comprises a conveying cylinder, the inner side of the conveying cylinder is movably connected with a connecting shaft through a screw blade, and a driving assembly and a flux adjusting assembly are arranged between the conveying cylinder and the screw blade. A flexible buffering assembly is arranged between the connecting shaft and the spiral blade. The device has the advantages of good structural stability, low failure rate and the like.

A system and device for generating focused inspection recommendations and estimating a cost for reconditioning pre-owned vehicles. Embodiments of the invention provide for individualizing and streamlining the process of inspecting a pre-owned vehicle, and more particularly, for identifying components with historically high failure rates. Through analyzing several data inputs, including vehicle data, inspection and arbitration data, third-party data, and sensor data, a recommendation of a focused inspection of one or more components is provided. Additionally, the analysis further determines an estimated cost for reconditioning of the vehicle, based upon the analysis of the data inputs. The focused inspection recommendation and the estimated reconditioning cost can then be displayed to the inspector using a programmed user interface.

The invention belongs to the technical field of drilling, and provides an active vibration reduction method and system based on self-adaptive adjustment of a drill rod load in order to effectively reduce vibration of a drill rod. A real-time feeding oil pressure signal is converted into real-time feeding thrust, and a real-time rotating oil pressure signal is converted into real-time rotating torque; filtering the original vibration signal by adopting a singular value subspace decomposition method to obtain a real vibration signal; according to the preset initial value of the feeding thrust and the preset initial value of the rotating torque, a neural network self-adaptive PID control algorithm is used for conducting self-adaptive comparison calculation, the vibration adjustment amount is obtained, and active vibration reduction is conducted according to the vibration adjustment amount; the system comprises six closed-loop control modules including a preset parameter input module, a signal acquisition module, a signal filtering module, a control algorithm module, a control output module and an execution module, accurate vibration reduction control is achieved, the bounce phenomenon of a drill bit is eliminated, the deviation error of a drill hole is reduced, meanwhile, stick-slip vibration is restrained, and the clamping stagnation failure rate of a drill rod is greatly reduced.

The invention provides an air conditioning system fault diagnosis method and device based on a parameter correlation Bayesian network, and the method comprises the steps: rapidly determining the real-time numerical values of key performance parameters of parts through the correlation between the material parameters of the parts, comparing the calibration numerical values of the parts when the parts leave a factory, and determining the real-time fault rates of various faults of the parts; and in combination with a Bayesian network model of the air conditioning system, calculating the real-time fault rate and the real-time posterior probability of each stage of sub-node in sequence, and judging whether the sub-node is a fault node or not by comparing the difference between the statistical posterior probability and the real-time posterior probability. According to the method, in real-time data measurement and processing, measurement is convenient, and operability is high; according to the method for judging the faults through the Bayesian network and the posterior probability, the diagnosis process is more flexible, fault positioning is more accurate, the method can be suitable for fault diagnosis of various types of air conditioning systems through slight adjustment, and high practical generalization is achieved.

The invention discloses a test data analysis method and device for a vehicle-mounted telescopic tent. The method comprises the steps that a performance index test data set and a corresponding test site set of the vehicle-mounted telescopic tent are obtained through measurement; the test site set comprises position information of test sites; the performance index test data set comprises a rainproof performance test data sequence, a snow load performance test data sequence, an expansion time test data sequence, a failure rate test data sequence, an energy consumption test data sequence and a storage time test data sequence; each piece of data in the test data sequence is obtained by testing at a test site in a test site set; data with the same serial number in all the test data sequences correspond to the same test site; preprocessing the performance index test data set to obtain a preprocessed data set; and performing evaluation calculation processing on the preprocessed data set to obtain a performance comprehensive evaluation result value of the field tent.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention relates to the technical field of pressure sensors, and discloses a structural design method and device applied to a brake pressure sensor, and the method comprises the steps: analyzing the material performance of a sensor material, and analyzing the material demand of a brake demand related to the sensor material; the material matching degree between the material performance and the material requirement is calculated, and the structural material of the brake application scene is determined from the sensor materials; querying geometric parameters of the assembled sensor, and performing force-electricity simulation on the assembled sensor through the piezoelectric constitutive model; the brake sensitivity of the assembled sensor is calculated according to the force-electricity simulation result, the brake failure rate of the assembled sensor is calculated according to the force-electricity simulation result, and defect reason analysis is conducted on the brake sensitivity and the brake failure rate; and querying a structure optimization material of the assembled sensor, and performing structure optimization on the assembled sensor by utilizing the reason analysis result and the structure optimization material. According to the invention, the structural design and materials can be optimized, and the sensitivity and environmental adaptability of the sensor are improved.

The invention discloses an error correction method of a neural network processor and a related device, and belongs to the technical field of neural networks. The method comprises the steps that according to a fault PE table obtained through error detection, the fault PE table comprises a two-dimensional list of fault occurrence and position information, a newly detected fault PE is positioned according to the fault PE table, and a multi-stage fault-tolerant decision is executed: i, a fine-grained error correction stage; ii, a coarse granularity error correction stage; according to the current error correction mode, a pipeline submission mechanism or a submission mechanism is selected, and a calculation result is output. According to the method, a mixed granularity fault recovery scheme is adopted, and the error correction function can be achieved under the condition that any standby PE is not added. The fine-grained scheme provides more flexibility and does not waste PE, and the coarse-grained scheme ensures that the reasoning precision is not reduced along with the increase of the failure rate.

The invention discloses a modern mobile informatization equipment management platform construction method and system, and relates to the technical field of informatization equipment management.The method comprises the steps that a management platform based on the full life cycle of equipment is used, and equipment life cycle nodes are associated; optimizing an equipment inspection flow, formulating an inspection standard and a dynamic task, and uploading data in real time in combination with a mobile terminal; based on multi-source data fusion analysis, equipment operation parameters are optimized, and decision support and abnormity early warning are provided. According to the method disclosed by the invention, the systematicness and comprehensiveness of equipment management are improved by establishing the full-life-cycle management platform and associating the equipment nodes; an embedded monitoring module is combined with a prediction algorithm, so that the equipment failure rate and the non-planned downtime are reduced, and the high efficiency and the reliability of equipment management are ensured; the abnormal early warning and decision support functions provide comprehensive optimization strategies and emergency response means for managers, so that the management efficiency and the equipment operation safety are improved.

The invention discloses a magnetic core assembling device and an assembling method thereof, and relates to the technical field of magnetic core assembling equipment.The magnetic core assembling device comprises a case and a mounting table arranged on one side of the upper end face of the case, a synchronous conveying belt is arranged on the mounting table, and a plurality of positioning die holders used for containing magnetic core frameworks are evenly connected to the surface of the synchronous conveying belt at intervals; the machine box is provided with a feeding assembly used for placing a magnetic core framework into the positioning die holder, magnetic core feeding vibration discs are arranged on the two sides of the upper end face of the mounting table correspondingly, and arc-shaped feeding channels are formed in the magnetic core feeding vibration discs. According to the automatic gluing device, automatic gluing operation on the butt joint faces of the magnetic cores can be achieved while pressing is conducted through the arranged gluing assembly, meanwhile, the gluing action, the magnetic core inserting action and the magnetic core framework feeding action are driven by the linkage arm serving as a connecting piece, the corresponding actions are completed, control over the device is simpler, and the production efficiency is improved. And the failure rate of equipment is effectively reduced.

The invention discloses a DC / DC converter reliability evaluation method and a related device, and the method comprises the steps: constructing an electrothermal coupling model of a power switch module of a Boost full-bridge boost isolation converter BFBIC combined DC / DC converter topological structure, and obtaining the power loss and junction temperature change conditions; constructing a life prediction model based on the junction temperature change condition, and calculating a failure rate in combination with a fatigue damage accumulation model; and establishing a calculation model of the outage probability of the series / parallel module based on the fault rate, and evaluating the reliability of the Boost full-bridge boost isolation converter combined DC / DC converter by using an improved reliability block diagram method. The method provided by the invention is helpful for reducing the evaluation error of the equipment reliability, and can provide data support for the construction and development of a novel power system.

The invention belongs to the technical field of motors, and relates to a vertical magnetic suspension motor. On the basis of the magnetic bearing control module, the cooling control module and the control evaluation module, closed-loop control of the rotor stably suspending at the target position is achieved, oscillation and steady-state errors are reduced, and the control precision is effectively improved; by dynamically calculating the temperature gradient difference and the cooling power regulation and control value and combining the PID controller and the liquid cooling / air cooling cooperation strategy, on-demand cooling is achieved, the cooling energy consumption is reduced, the temperature balance is improved, and the problems of local overheating or too high energy consumption are avoided; by performing normalized weighted analysis on key parameters such as magnetic bearing energy efficiency and cooling efficiency, feeding back control evaluation coefficients in real time and guiding system parameter optimization, the stability and reliability of long-term operation of the motor are ensured, the failure rate of the motor is reduced, the functional integrity is ensured, and system-level optimization is facilitated.

The invention discloses an offshore wind turbine generator fault prediction method and device considering the influence of environmental factors, and the method comprises the steps: dividing an offshore wind turbine generator into a sensor system, a mechanical system and a tower structure system according to the correlation between the fault rate and the environmental factors, and building fault detection models respectively; time convolutional network prediction models are respectively established in combination with unit operation data and environment data to predict the operation state of each system, and fault occurrence is predicted in combination with a fault prediction model; coupling each system fault prediction model, constructing a coupling fault prediction model based on unit operation and an offshore wind turbine generator fault tree, carrying out label classification on predicted faults of each system fault model, carrying out verification through the fault tree, carrying out analysis and classification on fault causes, and supplementing the fault tree when judging that a new fault type appears; according to the method, the correlation between the offshore environment and the fault generation increments of different parts in the wind turbine generator is considered, so that the fault generation prediction accuracy of the offshore wind turbine generator is improved.

The invention provides a method for predicting a single event effect induced failure rate of an FPGA (Field Programmable Gate Array) system in radiation. The method comprises the following specific steps: representing a radiation environment of the FPGA system; determining the sensitivity of the single particle effect of the FPGA system in the radiation environment; and establishing a fluence domain reliability model of the FPGA system in the radiation environment: converting the characteristics of the single event effect cross section sigma SEE into a corresponding average fault-free fluence MFTF, and establishing an FPGA system reliability model of each energy spectrum band or LET spectrum band in a given radiation fluence according to the average fault-free fluence MFTF and the total number phi of particles passing through a unit area in a radiation field. According to the method, physical process analysis and data driving means are combined, and the fluence domain reliability model is established, so that the reliability of related equipment in a special physical environment is improved.

The invention discloses a lifting platform for offshore pile top construction, and relates to the technical field of ocean engineering, the lifting platform comprises a main body mechanism, four lifting mechanisms are movably inserted in the inner surface wall of the main body mechanism, and two driving mechanisms are fixedly mounted on the outer surface walls of the four lifting mechanisms; the lifting mechanism comprises a fixing pile. According to the lifting platform, after the device is started, a motor can enable two half gears to synchronously and reversely rotate through a rotating shaft and a transmission gear, so that racks on the left side and the right side are sequentially driven to start to move, then the platform is driven to ascend and descend, and the platform is clamped by means of cooperation between two metal clamping blocks and positioning holes; compared with a traditional gear and rack clamping structure, the clamping tongue is matched with the fixing hole so that external interference can be better resisted and the stability of the lifting platform can be ensured on the sea where the environment is severe and stormy waves are large, meanwhile, the structure is relatively simple and reliable, the fault rate is low, and therefore the construction safety is improved.

The invention belongs to the field of equipment evaluation, particularly relates to an intelligent evaluation method and system for the operation quality of a flight simulator, and aims to solve the problems of single evaluation dimension, poor real-time performance, low intelligent degree and lack of quantitative standards in the prior art. The method comprises the steps that operation data such as fault time, duration and available time are collected in real time through a sensor network; calculating core indexes such as fault frequency per unit time, accumulated duration and malignant fault rate; dynamically weighting and fusing the indexes to generate a single-machine health index; a correlation model is constructed based on fault space-time correlation between devices, and cluster health scores are output in combination with single machine indexes; when the malignant fault exceeds a threshold value, driving causal inference by utilizing a knowledge graph containing entity causal relationships of fault types, equipment parts and the like, and outputting a root cause diagnosis and maintenance scheme; and acquiring intervened data to update atlas parameters and models to form closed-loop optimization. Multi-dimensional real-time monitoring and diagnosis closed loop of the operation state are realized, and the maintenance efficiency is improved.

The present application discloses a rigid pressure stabilizing device for an oil drilling pump, belonging to the field of oil drilling equipment. The key technical points of its technical solution include a housing, a separating ring, and a fixing column. Among them, the housing is provided with an inlet and outlet hole for fluid to enter and exit, which is communicated with the discharge pipe of the drilling pump; the separating ring divides the interior of the housing into a first space and a second space, and the two are communicated through a communication hole. A plurality of annular first fins are arranged in the first space, alternately arranged and provided with shunt holes, which helps to stabilize the fluid pressure; a plurality of annular second fins are arranged in the second space, also alternately arranged and provided with shunt holes. The inlet and outlet hole, the first space, the communication hole, and the second space form a circulation channel for fluid to circulate, achieving the effect of improving the working reliability of the drilling pump and effectively reducing the failure rate caused by pressure fluctuations.