219356 results about "Electric machinery" patented technology

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention is suitable for the technical field of brushless motor control, and provides an intelligent brushless motor and a control method thereof, and the method is applied to a control system of the intelligent brushless motor. The system comprises a data acquisition module, a model construction module, a training tuning module, a model verification module, a parameter setting module, an operation application module, a wireless communication module, a memory, an alarm, a processing center and an intelligent mobile terminal. The data acquisition module, the model construction module, the training optimization module, the model verification module, the parameter setting module, the operation application module, the wireless communication module, the memory and the alarm are respectively connected with the processing center; the invention also provides an intelligent brushless motor. According to the method, the AI model is constructed and trained by collecting the daily motor operation quality data, and the operation control parameters are matched according to the model, so that various operation quality abnormalities are prevented, and accurate control of the motor is ensured.

The invention relates to the technical field of vehicle motors, in particular to a vehicle motor stator and rotor life prediction method and system, and the method comprises the steps: obtaining temperature distribution data, vibration spectrum data and current harmonic characteristic data during the operation of a stator and a rotor; performing time-frequency domain fusion analysis based on the temperature distribution data and the vibration spectrum data to obtain thermal coupling degradation characteristics, and obtaining electromagnetic performance degradation characteristics based on the current harmonic characteristic data; training to obtain a life prediction model based on a mapping relation between historical degradation characteristics and a failure threshold value, inputting the thermal coupling degradation characteristics and the electromagnetic performance degradation characteristics into the life prediction model, and outputting a residual life prediction value of the stator and the rotor; and dynamically adjusting operation parameters of the motor according to the residual life prediction value. According to the method, the problems that the life prediction precision is low and the operation strategy cannot be dynamically optimized due to the fact that the collaborative degradation effect of the stator and the rotor of the vehicle motor under the multi-physical field coupling effect is difficult to quantify in real time are effectively solved.

The invention discloses a machine tool control method and system based on mechatronics, and the method comprises the steps: outputting an optimal cutting track sequence according to the three-dimensional model features and material attribute parameters of a machined workpiece; based on the optimal cutting track sequence, motion interference among shafts of the machine tool is eliminated through dynamic weight distribution, and an anti-interference optimization machining instruction is generated; constructing a nonlinear vibration wave propagation model according to the coupling relation between the spindle rotating speed and the feeding speed, and generating a steady speed regulation and control signal; generating an energy efficiency optimal parameter set including motor torque, cooling power and lubricating frequency based on the energy consumption efficiency constraint condition and the steady speed regulation and control signal; and outputting a closed-loop control signal and synchronously updating the three-dimensional machining precision thermodynamic diagram according to the real-time data in the machining process and the predicted deviation of the digital twin model. According to the embodiment of the invention, global optimization, dynamic adaptation and efficient operation of the machine tool can be realized, and technical support is provided for intelligent upgrading of the modern manufacturing industry.

Disclosed is a motor fuzzy PID parameter tuning method based on an improved whale algorithm. The method comprises: building a brushless direct-current motor speed control system model, and using a fuzzy PID controller to perform motor speed control. A conventional whale algorithm is optimized by using a chaotic convergence factor, a fractional order, and Levy flights, so as to obtain an improved whale algorithm. Secondly, the overshoot of the system is used as a component of an ITAE performance index to obtain an improved ITAE performance index, and the improved ITAE performance index is used as a fitness function for the improved whale algorithm. Finally, the improved whale algorithm is used to optimize input and output membership functions of a fuzzy controller, so as to obtain optimal ΔKp, ΔKi, and ΔKd values, and Kp, Ki, and Kd parameters of a PID controller are tuned to implement motor speed control. The present invention addresses the difficulty of tuning parameters of conventional PID controllers and solves the problem of low precision of motor speed control, has the advantages of high anti-interference capability, little overshoot, and short adjustment time, and improves the dynamic characteristics and robustness of the controllers.

A dual-motor mixed-hybrid powertrain system, by performing pulse modulation control, i.e., series-hybrid intelligent start-stop control or parallel-hybrid intelligent power switching control, on the instantaneous power time-varying functions of an engine and a battery pack, can convert the surface working condition of an analog electric control engine into a simpler line working condition of a digital pulse control (DPC) engine, either a pre-determined high-state line working condition in the high-efficiency combustion area or a pre-determined non-combustion low-state line working condition with zero fuel consumption and zero pollutant emissions multiplexed in time. The traditional fixed one-to-one bidirectional mapping between an engine working condition and a vehicle working condition is converted into a dynamically adjustable many-to-many bidirectional mapping to achieve the full coverage of any overall vehicle working condition, achieving decoupling between a DPC engine working condition and the overall vehicle working condition and decoupling between software and hardware of a hybrid powertrain.

The invention discloses a motor fault detection method and system based on voiceprint recognition. According to the method, an annular microphone array is adopted to collect motor sound signals in a non-contact mode, a three-channel time-frequency data set is constructed through empirical mode decomposition (EMD) and a Mel-frequency cepstral coefficient (MFCC), fault diagnosis is carried out in combination with a CNN + ResNet network, and dynamic time warping (DTW) and CNN fusion matching is supported. The system comprises a preprocessing module, a fault template library and a matching algorithm, integrates wavelet denoising and multi-beam acquisition technologies, covers a frequency band of 50Hz-20kHz, can display a fault type and trend analysis in real time, and triggers secondary verification when the confidence coefficient is insufficient. According to the scheme, the anti-interference capability is improved through array signal processing, model parameters are optimized in combination with transfer learning, non-contact detection is achieved, the real-time performance and accuracy of fault diagnosis are remarkably improved, and the method is suitable for industrial motor health monitoring.

The invention relates to the technical field of motor anomaly detection, in particular to a motor data anomaly detection method based on the Internet of Things. The method comprises the following steps: acquiring motor operation data, evaluating motor operation demand overload and load response conditions, and detecting bearing kinetic energy chain coupling recession and motor structure connection coordination decline trends, so as to determine dynamic magnetic field unbalance conditions in motor operation; based on the dynamic magnetic field unbalance condition, estimating the electromagnetic interference risk and the frequency oscillation growth trend of the motor, monitoring the error accumulation condition, and detecting the abnormal state of the motor; performing optimal configuration processing on motor operation parameters in combination with an abnormal detection result; according to the invention, the motor operation is more stable and efficient through the motor data anomaly detection.

A cutting device includes a base plate, a motor attached to the base plate, a driving cord including a first end and a second end, the first end being attached to the motor, and a cutting assembly. The cutting assembly includes a fixed blade that is fixed to the base plate, and a driven blade that moves with respect to the fixed blade. The second end of the driving cord is attached to the driven blade to move the driven blade with respect to the fixed blade when the motor rotates.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention relates to a wind field sensing and wind resistance control device and method for a cross-domain unmanned aerial vehicle, belongs to the technical field of air-ground unmanned aerial vehicles in the field of air-ground coordination, and solves the problem that in the prior art, an unmanned aerial vehicle is poor in wind resistance flight capacity under the conditions that the wind environment is complex and the wind speed changes drastically. Original data are collected through a multi-mode sensor group and processed to obtain a standardized data set; s2, constructing a PINN model, and taking the standardized data set as input processing to obtain a wind field prediction result; s3, the PINN model is trained, and a trained PINN model is obtained; s4, inputting a standardized data set acquired and processed in real time into the trained PINN model to obtain a wind field prediction result output in real time; s5, establishing an active-disturbance-rejection controller, and outputting a final control quantity based on a wind field prediction result output in real time; and S6, processing the final control quantity to generate a motor distribution instruction, and providing the motor distribution instruction to a motor of the unmanned aerial vehicle.

The water tank assembly comprises a water tank body and a sealing side wall, the interior of the water tank body is hollow, a containing cavity is formed in the water tank body, the two ends of the water tank body are in a transverse semi-barrel shape, the middle of the water tank body is in a square column shape, and the middle of the upper portion of the water tank body is in a notch shape capable of containing a hinge head assembly to rotate. A water inlet is formed in the top wall of one end of the water tank body; a water outlet is formed in the middle of the lower part of the water tank body; the hinge head clamping cover body of the cleaning head can clamp the hinge assembly between the cleaning head shell body and the water tank body and between the cleaning head shell body and the hinge head clamping cover body through the connecting mechanism. The cleaning head comprises a cleaning head shell assembly, a cleaning roller, an extrusion roller and a water tank assembly, a driving motor is installed on the cleaning head shell assembly to drive the cleaning roller to rotate to wipe the floor, and a clean water outlet in the cleaning head shell assembly can spray out clean water to directly wash the surface of the cleaning roller.

Surface cleaning device, comprising a cleaning head (12), at least one cleaning roller (32) rotatable about a rotation axis (96), a drive motor (200) for the at least one cleaning roller (32), and a holding device (94) on which the at least one cleaning roller (32) is rotatably mounted and which holds the at least one cleaning roller (32) to the cleaning head (12), characterized in that the holding device (94) has at least one pivotable flap (144) to which a spring device (156) is assigned, wherein the at least one cleaning roller (32) is held clamped to the holding device (94) by the at least one flap (144), and in that a wiper device (166) for wiper-off dirty fluid from the at least one cleaning roller (32) is seated on the at least one flap (144).

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention discloses a cleaning head shell assembly and a cleaning head, the cleaning head shell assembly comprises a cleaning head shell body, the cleaning head shell body comprises a shell body and two side walls connected with the shell body, one side wall is fixedly connected with a hinge seat, the hinge seat is hinged to one end of a driving motor, and the other end of the driving motor is fixedly connected with the shell body. The output end of the other end of the driving motor is clamped with the other side wall; the driving motor can be sleeved with a cleaning roller, and the output end is coupled to drive the cleaning roller to rotate to scrub a to-be-cleaned surface; the shell body is provided with a water guide tip part which is propped against the outer layer of the cleaning roller and is used for guiding sewage; the cleaning head shell body can be detachably connected with the water tank assembly, and the water tank assembly provides clean water for washing the surface of the cleaning roller. A rotatable extrusion roller is clamped between the two side walls, clear water can be sprayed out from a clear water outlet formed in one side wall to directly wash the surface of the cleaning roller, the cleaning head shell body extends upwards away from the cleaning roller to form an anti-overflow plate, and the water guide tip, the anti-overflow plate, the extrusion roller and the cleaning roller define a water tank space.

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention discloses a water and dust circulation cleaning head and a cleaning tool. The water and dust circulation cleaning head comprises a cleaning head shell assembly, a cleaning roller and an extrusion roller. The cleaning head shell assembly comprises a cleaning head shell body provided with two side walls, one side wall is fixedly connected with a hinge base, the hinge base is hinged to one end of a driving motor, and the output end of the other end of the driving motor is connected with the other side wall in a clamped mode. The cleaning roller sleeves the driving motor, and the output end of the cleaning roller is coupled to drive the cleaning roller to rotate to scrub a to-be-cleaned surface; the cleaning head shell body is provided with a water guide tip which abuts against the cleaning roller in the saturated state in the axial direction of the whole cleaning roller and guides sewage. The extrusion roller is rotatably clamped between the two side walls, clear water can be sprayed out from a clear water outlet formed in one side wall to directly wash the surface of the cleaning roller, an anti-overflow plate upwards extends from the cleaning head shell body away from the extrusion roller relative to the water guide tip part, and a water tank space is defined by the water guide tip part, the anti-overflow plate, the extrusion roller and the cleaning roller.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

The invention discloses a data model dual-drive motor life prediction system and method. The system comprises an information acquisition module, a model fusion module, a model training module and a weight adjustment module. The information acquisition module is used for acquiring state data of different motors; the model fusion module is used for effectively fusing the data driving model and the physical information model; the model training module is used for training and optimizing the fused mechanism-data dual-drive model according to the motor state data provided by the information acquisition module; the weight adjusting module plays a key adjusting role in the whole model training process. The method not only depends on a data-driven model for prediction, but also combines a physical information model to make up for the problem that a pure data-driven method lacks interpretability. Multi-physical-field factors such as environmental stress, vibration impact, thermal management and an electromagnetic field are brought into modeling, so that the performance degradation of the motor is accurately described and evaluated, and the accuracy and physical interpretability of a prediction result are enhanced.

The invention relates to a physical information constraint embedded non-stationary industrial process anomaly detection method, and belongs to the technical field of industrial process time sequence anomaly detection. According to the method, a dynamic coupling relation between key variables is extracted through frequency domain lagging correlation, original time sequence data is decomposed into two subspaces, namely a slowly-varying trend subspace and a quickly-varying disturbance subspace, through slow feature analysis, and long-term stable change and transient disturbance change are modeled respectively. Furthermore, a high-dimensional linear evolution model is constructed in a fast space and a slow space by adopting a Kupman operator, so that the multi-scale dynamic modeling precision is effectively improved. In addition, two types of physical information supervision mechanisms are introduced in the model training process: based on a water pump flow-pressure second-order dynamic equation and a motor electric power conservation law, the physical consistency and engineering interpretability of the prediction process are significantly enhanced. According to the invention, abnormal working condition identification and early warning under variable working conditions in a non-stationary industrial process can be realized.

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.

The invention relates to the technical field of motor control, and discloses a brushless motor adaptive control method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-point temperature data of a motor, building a thermal dynamic model, and reconstructing the complete temperature field distribution; identifying a relation function between motor parameters and temperature, and establishing a temperature sensitive parameter model; in combination with temperature field distribution and a temperature sensitive parameter model, learning long-term influence of control actions on temperature distribution, and predicting temperature evolution trends under different control strategies; constructing a multi-objective optimization control strategy, and generating a motor control instruction and a heat dissipation control instruction; dynamically selecting an optimal heat dissipation strategy combination according to the generated control instruction, the current working condition and the predicted heat influence; according to the invention, high-precision control and thermal management optimization of the brushless motor in a temperature change environment are realized.

A high-speed wind-gathering booster fan is provided. The high-speed wind-gathering booster fan includes a housing. An air duct is arranged inside the housing. A motor base is arranged at an axis of the air duct. Part of a circumferential wall surface of the motor base extends radially outward to form guide vanes. The air guide vane includes a parallel part and a torsional wind cutting part. The parallel part is close to the side of an air outlet. The parallel part is in parallel with an axis of an air outlet. The torsional wind cutting part is close to the side of an air inlet. The torsional wind cutting part is deviated from a plane where the parallel part is. An extension surface of the torsional wind cutting part is axially intersected with a wind-deflecting fan blade set.

A method for cooperative vehicle operation includes receiving a first target angle signal associated with a handwheel of a vehicle, receiving a second target angle signal associated with an autonomous steering system of the vehicle, and, in response to a determination that a handwheel angle error between the first target angle signal and the second target angle signal is less than a handwheel error threshold: generating a handwheel motor torque command based on a position control motor torque command and a torque control motor torque command; and controlling a position of the handwheel of the vehicle based on the handwheel motor torque command.

The present invention relates to a coal mine intelligent directional drill and its drilling method, belonging to the technical field of coal mine drills. It comprises a moving platform, machine frame, clamper, main manipulator, power head, drill pipe storage system, control system, and hydraulic system. The power head structure is improved by adding an angle adjuster with accurate angle adjustment, locking, and anti-rotation functions. The angle adjuster is positioned on the gearbox side opposite the main motor, connected via a driving shaft. The control system includes a toolface azimuth detection and initialization system that works with the angle adjuster. This solves prior issues such as the absence of specialized toolface azimuth adjustment devices, low precision, low track adjustment efficiency, and challenges in achieving automatic directional drilling in the prior art. The invention enhances precision and efficiency, enabling automated, intelligent directional drilling.

The invention discloses a permanent magnet motor demagnetization fault diagnosis method based on deep learning, and relates to the technical field of permanent magnet motor fault diagnosis, and the method comprises the steps: collecting the operation state data of a permanent magnet synchronous motor and a fan in real time through a sensor network, and generating a basic variable; multi-modal features in the basic variables are extracted, a health index sequence is constructed, and a demagnetization risk score is generated; setting a self-adaptive diagnosis opportunity control strategy, and determining the opportunity of next diagnosis detection according to the current demagnetization risk score; establishing a demagnetization risk prediction model, predicting a demagnetization risk score at the next diagnosis time, generating a dynamic risk threshold value, and judging whether a demagnetization early warning decision is triggered or not; allocating a maintenance task priority according to the current demagnetization risk score and the historical mode; comprehensively calculating the overall health index of the fan system, and predicting the residual life of the fan permanent magnet motor. According to the method, the problem of resource waste caused by unreasonable diagnosis time in the demagnetization fault diagnosis of the permanent magnet motor is solved.

A surface cleaning device is provided, comprising a cleaning head (12) with a cleaning head body (14), at least one cleaning roller (32) rotatable about a rotation axis (92), a drive motor (200) for the at least one cleaning roller (32), at least one cassette (92) with a holding device (94) for the at least one cleaning roller (32), wherein the at least one cleaning roller (32) is rotatably mounted on the holding device (94) and the at least one cassette (92) is removably seated on the cleaning head body (14), a receptacle (84) for the at least one cassette (92), which is arranged on the cleaning head body (14) and which is closed towards a first lateral side (80) of the cleaning head body (14) by a transverse wall (86) and is open towards a second lateral side (82) of the cleaning head body (14) opposite the first lateral side (80) through an opening (88),wherein the at least one cassette (92) on the second lateral side (82) at the opening (88) is removable from the cleaning head body (14) in a removal direction (186) and is insertable onto the cleaning head body (14) in an insertion direction (184), wherein a fixing device (192) is provided by which the at least one cassette (92) is fixable to the cleaning head body (14) in the region of the transverse wall (86) relative to the removal direction (186) and the insertion direction (184).

The invention relates to the technical field of motor fault diagnosis, in particular to a motor fault diagnosis algorithm based on multi-sensor fusion, and the algorithm comprises the steps: injecting a step excitation signal into a motor, synchronously collecting the original response waveforms of vibration and current sensors, and calculating the inherent response delay. Establishing a mapping relation library of delay values and current sensor filtering parameters, calling the delay values in real time according to the filtering parameters, performing reverse time offset compensation on a current harmonic signal time sequence, performing time alignment on the two types of data, finally performing cross-domain coupling analysis on the aligned data, extracting vibration pulse peak frequency and current harmonic fluctuation quantity, and determining the vibration pulse peak frequency and the current harmonic fluctuation quantity. Early faults are judged by combining the bearing outer ring fault characteristic frequency band and the load rate dynamic threshold value, graded alarm is generated by tracking characteristics, the problem of fault false judgment and missed judgment caused by sensor data space-time dislocation is solved, and the early fault diagnosis accuracy of the motor is improved.