1037results about "Machine gearing/transmission testing" patented technology

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.

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 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 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 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 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 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.

The invention relates to the field of internet big data and fault diagnosis, in particular to a cross-working-condition gearbox fault diagnosis method based on IDANN, feature extraction is performed through an improved time-frequency transformation network, and the improved time-frequency transformation network combines an interpretable time-frequency feature network and a deep residual network and integrates a soft threshold noise reduction module; meanwhile, on the basis of a prediction loss function and an adversarial loss function, class-level alignment of a target domain level, namely a difference loss function of the target domain, is introduced, reliable abnormal target domain sample difference measurement is carried out by adding two label classifiers and adopting a Wasserstein distance, the measurement considers the property of probability space bottom layer geometry, and the measurement accuracy is improved. An abnormal target sample can be effectively detected even under the condition that distribution is not supported by sharing; and finally, a total loss function is calculated based on the prediction loss function, the difference loss function of the target domain and the confrontation loss function to optimize model parameters, so that the accuracy and efficiency of fault diagnosis of the gearbox are improved.

The invention relates to the technical field of intelligent operation and maintenance of mechanical equipment, in particular to an electromechanical composite transmission system self-lifting diagnosis method based on a memory sub-network, and the method comprises the steps: constructing a self-lifting fault diagnosis framework which comprises a health category prediction network, a stable information memory sub-network and a rapid information memory sub-network; in the autonomous lifting learning stage, a fault diagnosis data set A is randomly extracted, and a data set B is randomly extracted from a learned information space; predicting the data set B by using the two sub-networks, and selecting an optimal playback log; predicting the data set A and the data set B on the basis of a health category prediction network to obtain two logs; calculating the total loss based on the playback logits and the two logits of the health category prediction network, and updating the weight and the learned information space of each network; and carrying out multiple times of autonomous lifting learning until a training target is met. According to the invention, the fault diagnosis network can be continuously trained from the continuously added data streams, and the autonomous improvement of the network is realized.

The invention discloses a robot joint transparency testing method and device, and belongs to the technical field of robot joint testing. According to the method, the transmission transparency is evaluated by testing three indexes including the ratio of joint reverse drive torque to the maximum output torque, the torque control bandwidth and the correlation between motor current and output torque; the device comprises a driving mechanism, a loading mechanism and a test platform with a sliding assembly, the driving mechanism is connected with a joint input end through a coupling, and the loading mechanism is connected with an output end. According to the invention, a quantitative evaluation system of transmission transparency is established, a modular device capable of separably testing joint motor reverse drive torque (distinguishing charged / uncharged states) and speed reducer forward / reverse drive efficiency is designed, and accurate data support is provided for joint performance optimization.

The invention relates to the technical field of detection equipment, in particular to an automatic worm wheel comprehensive monitor which comprises a worm wheel positioning unit, a worm matching unit, an industrial control driving unit, a worm wheel detection analysis unit and a detection feedback unit. According to the invention, the meshing data of the tooth surface of the to-be-detected worm gear and the worm in the meshing rotation process are detected to analyze the rim roundness of the to-be-detected worm gear, the coaxiality of the worm gear shaft hole and the circle center corresponding to the rim, the tooth surface contour precision of the to-be-detected worm gear and the uniformity of the worm gear disc; and determining the tooth surface quality parameter of the to-be-measured worm gear according to the angle deviation value of the worm gear rotation angle and the worm rotation angle in the meshing rotation process of the to-be-measured worm gear and the worm. The quality condition of the worm gear can be comprehensively detected, defects or problems possibly existing in the manufacturing or assembling process of the worm gear can be found in the early stage, and the quality and reliability of products are improved.

The invention provides a wind turbine generator gearbox fault monitoring method and system, and the method comprises the following steps: obtaining sample data of a wind turbine generator gearbox, and carrying out the preprocessing of the sample data, and obtaining the preprocessed sample data; performing local score calculation on the preprocessed sample data based on KNN-MST to obtain a local score; constructing a dual fault detection model, and calculating to obtain a final anomaly group and a corresponding anomaly group distance matrix based on the normalized local score of the sample and the dual fault detection model; and collecting real-time operation data of the wind turbine gearbox, calculating an abnormal score of the operation data in combination with the final abnormal group and the abnormal distance matrix, and obtaining an equipment operation state diagnosis result, thereby monitoring whether the wind turbine gearbox is abnormal or not. According to the method, the fault point is subjected to double diagnosis by calculating the local score of the data progress and combining the improved CUSUM algorithm and the improved Euclidean distance method, so that the occurrence of false alarm of the fault point is avoided, and the operation and maintenance cost is reduced.

The present invention discloses a load-bearing capacity detection device and detection method based on an automobile transmission shaft, comprising a connecting seat, wherein both ends of the upper side of the connecting seat are provided with a fixed frame, an automobile transmission shaft is placed on the upper ends of the two fixed frames, the tops of the two fixed frames are fixedly connected to a cylinder, the bottoms of the two cylinders are connected to a pressure frame, a hydraulic cylinder is provided at the top of the middle part of the connecting seat, and a pressure member is connected to the bottom of the hydraulic cylinder. The present invention relates to the technical field of load-bearing capacity detection of automobile transmission shafts. This load-bearing capacity detection device and detection method based on an automobile transmission shaft, when the automobile transmission shaft is deformed, the deformation part of the upper end of the automobile transmission shaft pushes the rotating frame to rotate, and the rotating end of the rotating frame drives the pointer to rotate. When the user closes the hydraulic cylinder, the pointer and the dial are observed through the rectangular lens to obtain the rotation angle of the deformed end of the automobile transmission shaft. During the deformation process of the upper end of the automobile transmission shaft, the splashing debris generated by the automobile transmission shaft is blocked by the rotating frame.

The invention provides an impeller high-temperature over-rotation test device. The impeller high-temperature over-rotation test device comprises a long shaft, an impeller, a press-fitting assembly, a buffer assembly and a fixing piece. The long shaft comprises a first shaft body and a second shaft body which are concentric and different in diameter, the impeller is sleeved on the first shaft body, and press-fitting parts are formed at two ends. The first press-fitting assembly and the second press-fitting assembly are pressed on the press-fitting parts on the two sides of the impeller correspondingly, relative rotation is prevented through the rectangular keys, the double press-fitting assemblies are symmetrically arranged to balance axial force, and rotation stability is improved. The left / right clamping sleeve is matched with the conical surface of the left / right taper sleeve, and a tensioning wire groove is formed to adapt to impellers of different sizes and compensate size changes. The buffering assemblies are arranged in a mirror symmetry mode, the horn mouth design optimizes stress distribution, long-axis thermal expansion is absorbed, and vibration impact is resisted. The fixing piece provides axial fixing force, and the axial rigidity of the system is optimized. The device can ensure the reliable positioning of the impeller at high temperature and ultrahigh speed, and improves the test precision and reliability.

A gearbox simulation test device disclosed by the present invention comprises a device seat and an adjusting device, an analysis assembly is installed on one side of the device seat, a driving assembly is horizontally erected on the rear side of the analysis assembly, and a movable table group is arranged on one side of the power output end of the driving assembly. The adjusting device is arranged on the side, away from the driving assembly, of the movable table set, and a fixed angle plate is arranged on the side edge of the device base. According to the gearbox simulation test device, the adjusting device and the movable table group are arranged, the adjusting device can simulate complex variable loads borne by a gearbox of a helicopter in different flight states, such as different load conditions during takeoff, cruising and landing, and the movable table group is used for installing and fixing the gearbox and meanwhile, can simulate the complex variable loads of the gearbox in different flight states. Structural self-adaption is achieved through cooperation with operation of the adjusting device, and the device base, the analysis assembly, the driving assembly, the movable table set and the adjusting device have structural disassembly and assembly performance so that structural maintenance and adjustment can be facilitated.

The invention provides a forage grass crushing degree detection system and method of a TMR mixer truck. The forage grass crushing degree detection method for the TMR mixer truck comprises the steps that a preset crushing degree scoring data interval is obtained and determined based on the type of the TMR mixer truck, the type of animals, the feeding stage of the animals and the feeding requirements of the animals; obtaining weight data of forage grass, torque data of a transmission shaft and rotating speed data of the transmission shaft in real time; preprocessing the collected forage grass weight data, torque data and rotating speed data; determining forage grass crushing degree scoring data based on the standardized forage grass weight data, the standardized torque data and the standardized rotating speed data; and determining the forage grass crushing degree based on the relationship between the forage grass crushing degree scoring data and the corresponding preset crushing degree scoring data interval. The forage grass crushing condition can be accurately and timely reflected, and a basis for judging forage grass crushing in real time is provided for an operator of the TMR mixer truck, so that the optimal crushing condition is achieved.

The embodiment of the invention provides a commercial vehicle transmission shaft maintenance monitoring method, device and equipment and a vehicle, and the method comprises the steps: monitoring the working temperature of an intermediate support of a commercial vehicle transmission shaft, and determining the working condition of the intermediate support of the transmission shaft; accumulating the duration of the transmission shaft intermediate support under various working conditions; determining the consumption life of the transmission shaft intermediate support according to the duration time under various working conditions and the corresponding life influence coefficient; determining the residual service life of the transmission shaft intermediate support according to the rated service life and the consumption service life of the transmission shaft intermediate support; and generating maintenance alarm information according to the residual service life of the transmission shaft intermediate support and a preset minimum reminding threshold value. By utilizing the method, the working state of the transmission shaft intermediate support can be monitored in real time, the residual life can be accurately calculated according to the actual working condition, the replacement reminding can be timely sent out, and the problems of unreasonable maintenance period and inaccurate state monitoring in the prior art are solved.

A device for measuring the torque of a rotating shaft of a rear driving wheel of a tractor is composed of a magnetic ring, a Hall sensor and a torque measuring ECU. 32 permanent magnets are uniformly arranged in a circular ring in an annular manner, two magnetic rings are respectively sleeved on the left rear driving wheel rotating shaft and the right rear driving wheel rotating shaft, one magnetic ring is close to a gearbox, and the other magnetic ring is close to a wheel hub flange; and the four Hall sensors are fixed on the rear axle shell, and the sensing end surfaces of the Hall sensors are 2-10mm away from the magnetic rings. An output pin of the Hall sensor is electrically connected with a signal input pin of a PLL chip of the torque measurement ECU, a phase difference output pin of the PLL chip is electrically connected with a GPIO pin of the single-chip microcomputer, a torque value is calculated according to a formula (1) by measuring the duty ratio of a high level in a phase difference square wave signal, and the single-chip microcomputer issues torque information and can respond to a CAN bus and output the torque value. According to the invention, slip ring friction in the torque measurement process is avoided, the cost of torque measurement is reduced, and digitization of the tractor is facilitated.

The invention belongs to the technical field of high-speed rotating transmission devices, and particularly relates to an online monitoring method for a high-speed rotating transmission device based on multi-sensor cooperation, which comprises the following steps of: acquiring industrial data including a rotating speed parameter, a vibration parameter, a torque parameter, an angle parameter and a temperature parameter on a rotating part in real time by using sensors when power source equipment rotates; after a vibration intensity value of a rotating part is obtained through calculation, a multi-dimensional data matrix is generated through fusion processing, meanwhile, the multi-dimensional data matrix is compared with a safety value, a threshold mechanism is set, three-level response is triggered according to severity, intelligent diagnosis is achieved based on a historical data training model, a 3D dynamic model is generated, and the real-time state of a transmission device is mapped. And comparing historical data analysis trends, finally automatically generating a monitoring report, and uploading maintenance suggestions to a data management terminal. Comprehensive data coverage is realized, data one-sidedness of a single sensor is avoided, and the equipment life cycle cost is reduced through predictive maintenance.