1501results about "Machine gearing/transmission testing" patented technology

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention provides an airport luggage system speed reducer fault prediction and diagnosis method based on digital twinning, which relates to the technical field of fault prediction and comprises the steps of acquiring real-time operation data of a speed reducer, establishing a digital twinning model and updating the model in real time, constructing a multi-dimensional feature vector and then performing feature extraction and fusion through deep learning, identifying a fault type and evaluating the degree. And optimizing model parameters by using a particle swarm optimization algorithm to establish a residual life prediction model, generating early warning information and determining a maintenance scheme. Accurate prediction and early warning of faults of the speed reducer are realized, the maintenance cost is reduced, and the operation reliability of an airport luggage system is improved.

The invention relates to the technical field of fault diagnosis, in particular to an automatic anomaly detection method for a high-speed rotating transmission device, which comprises the following steps of: acquiring angular acceleration, rotating inertia and strain data of the high-speed rotating transmission device, calculating a transient strain gradient and matching a rotating speed change rate, adjusting a strain deviation range according to the change of the rotating inertia, and detecting the anomaly of the high-speed rotating transmission device. And screening strain abrupt change points to obtain a rotation strain matching result. According to the method, the dynamic strain response interval is set, the strain deviation range is adjusted, the strain abrupt change points are screened, the frequency response of the equipment is monitored in real time and compared with the normal frequency, so that continuous monitoring of the frequency deviation rate is more accurate, the frequency deviation rate jointly acts on identification and clustering of abnormal characteristics, the accuracy and the real-time performance of fault diagnosis are improved, and the fault diagnosis efficiency is improved. By calculating the deviation degree of the abnormal characteristics and the normal track, the abnormal detection threshold can be adjusted, potential faults can be identified and processed more effectively, the maintenance cost of the high-speed rotating transmission device is reduced, and the operation efficiency is improved.

The invention relates to the technical field of transmission system fault diagnosis, in particular to an operation fault monitoring method and system applied to an automobile transmission system. Determining the vibration characteristic coefficient of each period according to the amplitude difference of the vibration sequence in different frequency regions on the frequency domain; and a fault trend coefficient is constructed based on the data fluctuation frequency of the vibration signals of all periods and the change trend of the vibration characteristic coefficients of all periods so as to adjust the abnormal proportion, and based on the adjusted abnormal proportion, the operation fault monitoring of the automobile transmission system is carried out in combination with an isolated forest algorithm. The method improves the sensitivity of abnormal detection at the initial stage of a fault or during a slight fault, guarantees the dynamic balance of the false alarm rate and the missing report rate of the fault detection of the transmission system, and improves the quality of the fault detection of the automobile transmission system.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

A method for state-of-health monitoring of a powertrain component in an electric vehicle system includes: determining an equivalent circuit model of the powertrain component; modeling heat losses in the powertrain component considering both transient and steady-state conditions; modeling heat flow through the powertrain component based on one or more material properties of the powertrain component; determining a temperature of a particular structure within the powertrain component; and determining, using a Rainflow algorithm, a number of temperature cycles until failure of the particular structure based on the temperature of the particular structure.

The invention provides gear box full-automatic multi-station assembling equipment, and relates to the technical field of gear box assembling, the gear box full-automatic multi-station assembling equipment comprises a workbench, and the workbench is provided with a first disc cutting mechanism and a second disc cutting mechanism; a bearing pedestal feeding mechanism, a main shaft direction selecting mechanism, a roller feeding mechanism, a pin roller and steel ball feeding mechanism, a pin roller and steel ball detecting mechanism, an inner gear feeding mechanism, a first transmission fluted disc feeding mechanism, a first oil injection mechanism, a transmission gear feeding mechanism and a gear detecting mechanism are sequentially arranged on the periphery of the first disc dividing mechanism. The first disc cutting mechanism, the second disc cutting mechanism and a plurality of mechanisms around the first disc cutting mechanism and the second disc cutting mechanism are arranged on the workbench, all working procedures needed for gearbox assembly are highly integrated, continuous automatic operation of a series of working procedures from part feeding to assembly and detection is achieved, the assembly time is effectively shortened, the assembly efficiency is improved, and the production cost is reduced. Labor input is reduced, labor cost is reduced, and the quality of final products is effectively improved.

The invention provides a speed reducer fault positioning method and system based on artificial intelligence, and relates to the technical field of intelligent fault detection and positioning of industrial equipment. The method comprises the following steps: synchronously acquiring vibration, temperature and acoustic emission signals by using a multi-mode sensing module, carrying out filtering, correction and improved wavelet packet decomposition processing through an edge calculation unit, extracting time domain, frequency domain and nonlinear characteristics, calculating a vibration degradation degree and a state deviation index to construct a characteristic space, and calculating a state deviation index; fault large-class identification and fault source accurate positioning are realized through deep metric learning and a graph attention network, and credibility evaluation and online incremental learning optimization are carried out in combination with a D-S evidence theory. The system comprises a multi-modal sensing module, an edge computing unit, a cloud analysis platform, a self-adaptive optimization module and a man-machine interaction terminal. The method overcomes the defects that in a traditional method, a signal source is single, feature extraction depends on experience, real-time performance is insufficient, positioning is inaccurate and the like, and fault detection accuracy and equipment operation safety are remarkably improved.

The invention belongs to the technical field of industrial robot body rigidity testing, and particularly relates to a large-load robot transmission rigidity calibration device and method. The device comprises a switching frame, a fixed cross beam, a test torque cross beam, a counterweight mechanism, a measuring instrument, a locking mechanism and a position changing machine, the switching frame is connected with a position changing rotating disc of the position changing machine, a to-be-calibrated robot joint is installed on the switching frame, and an output shaft of the to-be-calibrated robot joint and the position changing rotating disc are coaxial. An input shaft of the to-be-calibrated robot joint is locked through a locking mechanism; the test torque cross beam is connected with an output shaft of a to-be-calibrated robot joint, and counterweight mechanisms are arranged at two ends of the test torque cross beam; the fixed beam is connected to an output end shell of a to-be-calibrated robot joint, the measuring instrument is arranged at one end of the fixed beam, one end of the test torque beam is loaded, the torsion angle of the other end of the test torque beam is measured through the measuring instrument, and therefore rigidity calibration of the robot joint is achieved. The rigidity testing device is simple in structure, capable of effectively controlling rigidity testing errors, accurate in testing result and low in cost.

The invention provides a wind turbine generator transmission chain fault detection method and device, and relates to the technical field of fault detection.The wind turbine generator transmission chain fault detection method comprises the steps that time sequence operation data and state waveform data of a wind turbine generator transmission chain are obtained; determining an abnormal characteristic index of the transmission chain of the wind turbine generator according to whether the time sequence operation data meets a preset target rule or not; according to the state waveform data, determining a time domain characteristic index and a frequency domain characteristic index of a transmission chain of the wind turbine generator; and based on the abnormal feature index, the time domain feature index and the frequency domain feature index, determining fusion features, and determining a fault detection result of the wind turbine generator transmission chain according to the fusion features, thereby solving the technical problems of incomplete data feature mining and low fault detection accuracy in the prior art.

The invention discloses a mechanical analysis method for flexible sleeve tooth connection considering three-dimensional friction force of a contact surface, which comprises the following steps of: firstly, dividing the flexible sleeve tooth connection into a plurality of shaft section infinitesimal elements along the axis direction, and simulating the contact behavior of a meshing tooth pair of each shaft section infinitesimal element by adopting a three-dimensional contact unit; wherein parameters of each contact unit are calculated according to geometric parameters and material parameters of the teeth. Summing the output results of all the contact units to obtain the relation between displacement and force or torque, and finally drawing a hysteresis loop and solving the rigidity and damping. According to the method, the non-linear rigidity can be calculated according to the size parameters of flexible sleeve tooth connection, a basis is provided for relatively accurately estimating the resonance rotating speed of the rotor system, and the method has important theoretical value and engineering significance for the safety design of the engine rotor system.

The invention relates to the technical field of equipment fault diagnosis, and discloses an intelligent fault detection method for a speed reducer, and the method comprises the steps: extracting fault feature knowledge from a source domain based on a domain adaptive transfer learning algorithm, migrating the fault feature knowledge to a target domain, and generating a synthetic fault sample based on physical model constraints; constructing interaction influence among the multi-subject causal capture equipment; executing causal intervention and anti-factual reasoning, and determining a fault root cause; constructing a fault knowledge graph and continuously optimizing the fault knowledge graph; and constructing a preventive maintenance decision system to generate an optimal maintenance strategy. According to the method, high-accuracy fault diagnosis can be realized under the condition of sample scarcity, interaction influence among multiple devices is analyzed, fault root causes are traced, and reliable preventive maintenance decision support is provided.

The invention discloses a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and relates to the field of collaborative robot joint fault diagnosis. In order to solve the problem that an existing harmonic reducer fault diagnosis method is low in diagnosis rate, the invention designs a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting the vibration, torque, temperature and other multi-sensor original data of a harmonic reducer in different fault modes; one-dimensional time-domain signals of a plurality of sensors are converted into two-dimensional time-frequency images through continuous wavelet transform, a fault feature time-frequency graph data set of the harmonic reducer is constructed, feature selection and noise suppression are performed through a residual shrinkage unit in a DRSN-SE algorithm, the feature representation capability is enhanced by using an SE-Net attention mechanism, and the fault feature time-frequency graph data set of the harmonic reducer is constructed. And fusing the features of the multi-source heterogeneous data to obtain final fused features, and outputting a fault diagnosis result through a classification head. According to the invention, the problem of poor reliability of single fault data can be effectively solved, and high-precision fault diagnosis and classification identification of the harmonic reducer are realized.

The invention discloses a power system monitoring device and method, and belongs to the technical field of engineering machinery, a power system comprises a gearbox, the gearbox comprises a box body, a bearing and a transmission shaft, the box body is provided with a bearing cavity, the bearing is arranged in the bearing cavity, the transmission shaft is in transmission connection with the bearing, and the power system monitoring device comprises a power generation assembly and a monitoring assembly. The power generation assembly comprises a thermoelectric power generation piece, the hot end of the thermoelectric power generation piece faces the bearing cavity, the cold end of the thermoelectric power generation piece is away from the bearing cavity, and lubricating oil in the bearing cavity can wash the hot end of the thermoelectric power generation piece so that the thermoelectric power generation piece can generate electric energy. The assembly comprises a circuit board and a plurality of sensors, the sensors are electrically connected with the circuit board, the power generation assembly provides electric energy for the circuit board, and the sensors comprise a vibration sensor, a temperature sensor and a torque sensor. The power system monitoring device is adopted in the power system monitoring method. And heat is fully utilized, monitoring can be carried out, the fault rate is reduced, and energy is saved.

The invention relates to the technical field of accurate transmission control, in particular to a transmission device and a gallium nitride production system. In the transmission device, a transmission shaft is used for enabling a power output shaft of the driver to be in transmission connection with the main shaft body. And the transmission sleeve is in transmission fit with the transmission shaft and sleeves the transmission column. The first barrel and the second barrel are coaxially and fixedly connected, the inner spaces of the first barrel and the second barrel are communicated, and the first barrel is located at the end, close to the transmission column, of the second barrel. The first barrel is provided with internal threads. The rotating part is provided with an external thread, the rotating part is rotatably matched in the first barrel, and the rotating part and the first barrel are movably sealed. The rotating piece is connected with the transmission column. And the piston is in sliding fit in the second barrel. The pressure sensor is arranged on the inner side of the sealing plate, and a first elastic piece is connected between the pressure sensor and the piston in an abutting mode. The transmission precision can be calibrated as required according to the actual transmission situation, and the device can adapt to the situation that short-distance transmission and long-distance transmission are mixed.

The invention relates to a motor beating running-in machine and a detection method. The motor beating running-in machine comprises a machine table, and the machine table is provided with a running-in testing assembly, a conveying belt and a material moving mechanical arm; the running-in test assembly comprises a support, a motor positioning seat is transversely arranged on the support in a sliding mode, and a beating assembly and a reset piece are arranged on the support. The upper surface of the motor positioning seat is provided with a positioning groove and a detection joint seat, the motor positioning seat is provided with a first pressing unit and a second pressing unit, the moving track of a pressing part of the first pressing unit is inclined, and when the pressing part is located at the upper end of the moving track, the pressing part avoids an area position directly facing the positioning groove; the pressing part of the first pressing unit is used for longitudinally pressing the motor and pressing the joint of the motor and the detection joint seat; according to the invention, the automatic electrified knocking running-in test of the motor can be realized, the knocking amplitude range is large, the simulation vibration test scene can be downwards compatible by reducing the knocking amplitude, and the applicability is better.

The invention discloses a face gear planetary transmission system dynamic uniform load test platform and test method, and belongs to the field of gear transmission, and the test platform comprises a rack, a drive motor, a step-up gear box, a face gear planetary transmission system, a magnetic powder brake, a torque sensor, a slip ring, a stress strain test system and the like. The face gear planetary transmission system comprises an input face gear, a planet wheel, a planet carrier, a fixed face gear and the like. According to the dynamic uniform load test, stress values at the tooth root of the face gear and the tooth root of the planetary gear are measured mainly through strain gauges, and the corresponding relation between the tooth root stress and the meshing force at the gear meshing point is obtained through finite element simulation. And calculating the dynamic uniform load coefficient according to the dynamic meshing force root-mean-square values and the average values obtained at the tooth roots of the face gear and the planetary gear. By testing and analyzing the dynamic uniform load coefficient, the load distribution characteristic of the face gear planetary transmission system can be accurately revealed, a scientific basis is provided for structural parameter optimization, meshing performance improvement and reliability design of the face gear planetary transmission system, and then accurate and high-performance design of the transmission system is achieved.

The invention relates to the field of mechanical engineering, discloses a speed reducer multi-field loss balancing method and system based on working condition self-adaptive feedback, and aims to solve the problems of local loss concentration, intensified temperature rise and low energy efficiency caused by neglect of a multi-physical field coupling relation in the prior art. The method comprises the following steps: acquiring mechanical, thermal, fluid and electromagnetic parameters in operation of the speed reducer through a distributed sensor, and generating a standardized working condition state vector; and inputting into a mechanical-thermal-fluid-electromagnetic four-field coupling model, and solving the space-time loss distribution of the key component by combining with a data driving correction term. According to the scheme, refined prediction and closed-loop regulation and control of multi-field loss distribution are achieved, the local temperature difference and energy consumption concentration are remarkably reduced, the energy efficiency of the whole machine is improved by 8%-15%, the service cycle is prolonged, and the method is suitable for a high-end equipment transmission system under the high-dynamic heavy-load working condition.

The invention relates to a transmission shaft bench test device and method, and the device comprises a bumping sliding mechanism which comprises a pedestal, the pedestal is in sliding connection with a sliding platform through a sliding rail, and the pedestal is provided with a first actuator which drives the sliding platform to do reciprocating lifting motion in the length direction of the sliding rail; the side-tipping rotating mechanism comprises a side-tipping platform rotationally connected with the sliding platform through a rotating shaft, and a second actuator for driving the side-tipping platform to rotate around the rotating shaft is connected between the sliding platform and the side-tipping platform; the steering driving mechanism comprises a gearbox fixed on the roll platform, an input shaft of the gearbox is connected with a driving motor through a coupler, an output shaft of the gearbox is connected with a transmission shaft to be tested, and the transmission shaft to be tested is connected with a dynamometer. Performance and endurance tests of the transmission shaft under multiple working conditions can be simulated and detected more truly, and objectivity and accuracy of detection of the detected transmission shaft are improved.

The invention relates to a harmonic reducer fault diagnosis method based on double-branch feature fusion. The harmonic reducer fault diagnosis method comprises the following steps: synchronously acquiring multi-sensor data of a harmonic reducer acquired by vibration, acoustic emission and current sensors; processing the multi-sensor data through a one-dimensional convolutional neural network to obtain data in a two-dimensional matrix form; the data in the two-dimensional matrix form is input into a pre-trained model, and the processing process of the model is as follows: feature extraction is performed on the data in the two-dimensional matrix form through an MCWGraphKAN branch feature extraction module and a GBiMama branch feature extraction module to obtain a first feature and a second feature, the first feature and the second feature are fused and then mapped to a sample space through a full-connection layer, and the data in the two-dimensional matrix form are obtained; and fault classification is realized. The method achieves the fault diagnosis of the harmonic reducer through a deep learning method, and is used for solving the problems that a single sensor cannot fully express the operation state of the harmonic reducer, and the fault feature extraction of a single-branch network is insufficient.

The utility model discloses a simulation test device for an aircraft rudder transmission mechanism, and belongs to the technical field of simulation test. A simulation test device for an aircraft rudder transmission mechanism comprises an experiment module, an air inlet and an air outlet are formed in the two sides of the experiment module respectively, sealing rings used for sealing are fixed to the air inlet and the air outlet respectively, a mounting base is arranged in the experiment module, a rudder transmission structure is arranged on the mounting base, and the rudder transmission structure is connected with the experiment module. A testing control surface is rotatably arranged on the rudder transmission structure, a rail is detachably mounted at the bottom of the testing control surface, a second sliding block is slidably arranged in the rail, and a power cabin is in threaded connection with the bottom of the experiment cabin. Through the integrated arrangement of the driving device and the experiment module, the sealing performance of the experiment module is enhanced, the accuracy of test data is improved, in addition, through the arrangement of a thread at the bottom of the power module, the disassembly of the power module is more convenient, and the later maintenance and repair are more convenient.

The invention relates to the technical field of machine tool spindle performance testing, in particular to a high-precision spindle performance testing method and system, and the method comprises the steps: initializing a mirror image prototype of a spindle in a performance testing platform according to the main structure and working condition environment constraints of a spindle body; establishing a load control instruction set according to the mirror image prototype and based on the motion state of the main shaft body in the composite performance test scene; according to the load control instruction set, dynamic disturbance is executed on the spindle body, and performance change characteristics of the spindle body are captured in the disturbance process; combining the performance change characteristics with the mirror image prototype, executing self-organizing calibration of prototype parameters of the main shaft body, and generating an enhanced mirror image body with working condition adaptability; and a load control instruction set is newly calculated based on the enhanced mirror body weight, dynamic disturbance of multi-axis linkage is triggered, and a performance detection result is output. According to the invention, the problem of real-time mismatch of main shaft performance simulation and actually measured data under the high variable load condition is effectively solved.

The embodiment of the invention discloses a fault detection method and device and a storage medium. The method comprises the following steps: acquiring an original signal of to-be-detected equipment; wherein the original signals of the to-be-detected equipment comprise a positive original signal and a negative original signal; generating a single-ended signal based on the positive pole original signal and the negative pole original signal, outputting a digital signal based on the single-ended signal and a reference voltage, and converting the digital signal into an analog signal; and determining whether the to-be-detected equipment fails or not according to the digital signal and the analog signal. According to the technical scheme provided by the embodiment of the invention, the analog signal and the digital signal can be mutually detected, the risk of misjudgment is avoided, the accuracy and reliability of fault detection are improved, and the cost of fault detection is effectively reduced.

A static blade adjusting mechanism motion simulation test bench belongs to the technical field of multi-body dynamics tests and comprises a driving assembly, a transmission assembly, an execution assembly and a measurement assembly. The driving assembly is connected with the transmission assembly, the transmission assembly is connected with the execution assembly, and the measuring assembly is arranged on the transmission assembly and the execution assembly. The driving assembly takes a motor as a power source, drives a triangular crank of the transmission assembly to rotate, further drives a plate type simulation linkage ring of the execution assembly to rotate and translate along an optical axis, and finally drives a plate type simulation blade to perform rotation angle adjustment through a rocker arm by the plate type simulation linkage ring. In the moving process of the stationary blade adjusting mechanism, the rotation angles of the triangular crank, the plate-type simulation blade and the plate-type simulation linkage ring can be measured in real time through angle sensors in the measuring assembly. The characteristics of rotation and translation of the linkage ring are fully considered, the movement of the stationary blade adjusting mechanism can be simulated more truly, and the dynamic characteristics of the stationary blade adjusting mechanism can be reflected more comprehensively.

The utility model discloses a speed reducer service life testing device. The speed reducer service life testing device comprises a test bench, a motor flange plate used for installing a servo motor, a speed reducer installing plate used for installing a speed reducer to be tested, and a torque sensor, wherein the motor flange plate, the speed reducer installing plate and the torque sensor are arranged on the test bench from front to back. The servo motor is connected with the speed reducer through an input shaft, and the speed reducer is connected with the torque sensor through an output shaft. The experiment table is provided with a plurality of key bars, a connecting plate and a mounting plate, the supporting assembly is connected with the key bars in a matched mode through mounting grooves, and the servo motor, the speed reducer, the torque sensor and the flange shaft are coaxially mounted. A positioning key strip is arranged on the test bench, and if the specification of the detected speed reducer needs to be replaced, the coaxiality in the horizontal direction is ensured while the length distance of a transmission chain is adjusted.

The invention discloses a three-axis movement adjustable floating device, and relates to the technical field of reduction gearbox testing. The device is applied to a reduction gearbox shell and comprises an upper pressing plate, guide columns are installed at the four corners of the bottom of the upper pressing plate, a supporting plate is fixedly connected to the bottom ends of the guide columns, a plurality of floating assemblies are installed on the side wall of the supporting plate, a mounting plate is installed at the bottom ends of the floating assemblies, and a clamping air cylinder is fixedly connected to the bottom end of the mounting plate. The clamping cylinder acts on the reduction gearbox shell; positioning assemblies are detachably installed on the four sides of the supporting plate, and the upper ends of the positioning assemblies are fixed to the side wall of the supporting plate. Through the arranged floating assembly, multi-directional floating fine adjustment in the horizontal direction and floating fine adjustment in the vertical direction can be achieved in the positioning and mounting process of the pin shafts and the pin holes, self-adaptive adjustment positioning between the pin holes and the pin shafts can be achieved conveniently, the positioning precision between the pin shafts and the pin holes is guaranteed, lossless mounting between the shell and the pin shafts is achieved, and the service life of the shell is prolonged. And the product quality is further improved.

The invention provides a transmission chain fault diagnosis system and method of a wind generating set. The system comprises a data acquisition module, a global rotating speed characteristic matrix determination module, a transmission logic characteristic matrix determination module, a graph data coding module, a characteristic distribution correction module and a fault diagnosis result generation module. According to the technical scheme provided by the invention, real-time intelligent monitoring can be accurately carried out on the transmission chain of the wind generating set, so that the fault early warning is automatically generated when the fault of a certain part of the transmission chain is detected, the operation safety of a fan is ensured, and the accuracy of fault diagnosis is improved.

The invention discloses an involute helical gear tooth surface waviness rapid detection method based on wavelet transformation, relates to the technical field of gear measurement and evaluation, and aims to solve the problems of low efficiency, insufficient anti-interference capability and limited extraction precision in existing tooth surface waviness detection. High-density data sampling is carried out on a specified tooth surface of an involute helical gear, a multi-scale characteristic decomposition signal of wavelet transform is utilized, a tooth surface waviness signal is separated from complex morphology characteristic components, and in combination with a threshold optimization algorithm, detail characteristics of waviness are effectively reserved, and meanwhile interference of non-correlation signals is restrained. In addition, efficient data processing and real-time waviness feature extraction are realized by designing a rapid analysis process and an automatic parameter adjustment mechanism. The method is suitable for gear quality detection in a batch production environment, and provides a reliable basis for defect diagnosis and optimization in a gear manufacturing process. Compared with a traditional detection method, the method has remarkable advantages in the aspects of detection precision, stability and speed.

The invention discloses a key phase pulse signal synchronous acquisition and multi-axis centering analysis method, which belongs to the technical field of mechanical monitoring and diagnosis, and comprises the following steps of: configuring a synchronous acquisition system, acquiring a conditioned key phase pulse signal and a conditioned vibration signal, and extracting an axis speed and a load; constructing a time drift monitoring model to compensate the key phase pulse signal in real time, and ensuring the time synchronization precision; the compensation signal is input into a shaft centering correction model based on dynamics and finite element analysis, and radial deviation and axial deviation under the dynamic load are calculated; establishing a nonlinear tolerance threshold model by adopting a nonlinear regression model, and dynamically adjusting an allowable centering deviation threshold in combination with the real-time shaft speed and the load; through closed-loop comparison of the shaft centering deviation and a threshold value, a centering qualified conclusion or a correction suggestion is automatically output, and triggering signal resampling is supported to adapt to working condition changes; the real-time and self-adaptive analysis of the centering deviation of the multi-axis system is realized, and the operation stability and the maintenance efficiency of the rotating machinery are improved.

The invention discloses an anomaly detection method and device for a tool changer tool changing speed reducer and a storage medium, and relates to the technical field of tool anomaly detection.The method comprises the steps that in the machining process of a machine tool, a high-frequency operation state parameter set of the tool changing speed reducer is monitored and obtained, and a coarse anomaly risk coefficient sequence is output; the operation state parameter set of the tool changing speed reducer is obtained through fixed-point monitoring according to the second collection frequency; if the coarse abnormal risk coefficient sequence meets a first preset early warning index, outputting a fine abnormal risk coefficient sequence; and if the fine abnormal risk coefficient sequence meets a second preset early warning index, abnormal early warning is carried out on the tool changing speed reducer. The technical problems that in the prior art, due to the fact that graded early warning cannot be achieved for abnormal state detection of the tool changing speed reducer, sensitivity is insufficient, and reliability is low are solved, and the technical effects that graded early warning for the abnormal state of the tool changing speed reducer is achieved, and detection sensitivity and reliability are improved are achieved.