293 results about "Motor system" patented technology

The invention relates to a multi-parameter fusion motor performance evaluation method, equipment and a medium. The method comprises the following steps: firstly, acquiring electrical signals, mechanical vibration signals and thermal temperature data in operation of a motor, and performing preprocessing and standardization to form a first data set; secondly, dimension reduction is carried out on multi-dimensional parameters of the first data set, and core parameter combinations are extracted to generate a second feature data set; then, classifying and marking the second feature data set by utilizing a classification algorithm, and determining a key performance index distribution interval to obtain a third classification result set; and finally, constructing a time sequence anomaly detection model based on the third classification result set, analyzing an index change trend, identifying an abnormal point deviating from a preset threshold value, and generating a fourth anomaly detection result. According to the method, the data quality and consistency can be improved, the abnormal state in motor operation can be effectively identified, data support is provided for motor performance evaluation and fault early warning, and the method is suitable for real-time monitoring and operation optimization of a motor system.

The invention discloses a high-precision linear vibration feedback motor system, and relates to the technical field of precise electromagnetic driving, and the system comprises a core driving module which is a moving magnet type linear motor of a Halbach array magnetic circuit structure, and is integrated with a multi-physics field sensing unit; the dynamic parameter tracking module is used for acquiring a reed rigidity coefficient, a damping coefficient and a magnetic constant in real time when the motor runs, and establishing a dynamic displacement model based on a recursive least square method; a double-closed-loop control framework is adopted, an inner loop adopts field-oriented control, and an outer loop is based on model prediction control; the aging prediction unit activates a temperature rise compensation algorithm when the accumulated number of vibration times is greater than a set threshold value; the self-adaptive calibration engine is used for injecting a sweep frequency excitation signal when the system is started, and automatically compensating individual difference parameters according to the harmonic peak offset; and the digital twin mapping unit is used for establishing a real-time mapping relationship between the physical parameters of the motor and the virtual model, and dynamically correcting the model parameters by comparing the actual displacement with the model displacement.

The invention relates to the technical field of motor control, and particularly provides a permanent magnet synchronous motor current harmonic disturbance suppression method and a motor system.The permanent magnet synchronous motor current harmonic disturbance suppression method comprises the steps that firstly, a permanent magnet synchronous motor mathematical model considering harmonic disturbance under the d-q axis of a synchronous rotating coordinate system is constructed, and a theoretical basis is provided for subsequent control strategy design; an improved resonance controller is set and embedded into the extended state observer, comprehensive estimation of d-q axis non-periodic disturbance and periodic harmonic disturbance is achieved, finally, an enhanced current loop active disturbance rejection controller based on the improved resonance-extended state observer is built, unexpected peak values are eliminated, and the disturbance rejection rate of the current loop is improved. And comprehensive suppression of aperiodic disturbance and periodic disturbance is realized. According to the method, aperiodic and periodic disturbance can be suppressed, unexpected peak values are eliminated, system instability caused by noise amplification is avoided, and the steady-state performance and robustness of current control of the permanent magnet synchronous motor are remarkably improved.

The invention belongs to the field of robots, and discloses a six-degree-of-freedom intelligent force control modular dexterous hand, a palm structure comprises a palm shell and a skeleton, and the skeleton is used for assembling fingers, a thumb and a motor control system; the motor system comprises a first motor and a second motor; the fingers are connected with a first motor through a connecting rod mechanism, and the first motor drives the bending action of each finger; the thumb has two degrees of freedom including the bending degree of freedom and the transverse swinging degree of freedom of the thumb, bending of the thumb is controlled by a first motor and a connecting rod mechanism, and transverse swinging of the thumb is controlled by a second motor; the thumb is controlled by the second motor to swing transversely, so that palm-to-palm movement is achieved, and complex grasping tasks are completed in cooperation with other fingers. The thumb is designed to have two independent degrees of freedom, one degree of freedom is used for controlling bending of the thumb, the other degree of freedom is used for achieving transverse swinging of the thumb, the thumb can work in cooperation with other fingers, and therefore the flexibility and accuracy during grabbing are improved.

The invention provides an optimal trajectory tracking control method and device for a nonlinear motor servo system, and relates to the technical field of artificial intelligence, and the method comprises the steps: building an error dynamic model for tracking control of a motor system, introducing a new state variable, converting the new state variable into an augmented system model, and carrying out the tracking control of the motor system; determining a description form of an optimal tracking control problem under the constraint of a state inequality; based on the augmented system model, establishing an identification model of an unknown dynamic system by using a function approximation method; estimating unknown parameters in the identification model by using online data and historical data through a parallel learning method; and based on the identification model, updating control input through an iterative algorithm, and solving an optimal tracking control problem under the constraint of the state inequality to obtain an optimal tracking control strategy. By adopting the scheme, the method does not depend on an accurate model of the motor system, and the tracking error and the control energy consumption in the limited time domain are minimized while the condition that the state of the motor system meets the existing constraint is ensured.

The invention discloses an electric drive system motor spatial-temporal characteristic neural network fault diagnosis method and system, and is suitable for a motor system under various complex working conditions. According to the method, vibration signals under various fault motors are collected to serve as original signals, the original signals are preprocessed to form a data sample set, multi-scale decomposition is conducted on data in the sample set to obtain spatio-temporal characteristics to be output, population coding is conducted on the spatio-temporal characteristics to form time pulses, and the time pulses are output. And finally, the constructed pulse convolutional network model is utilized to train input, a gradient descent method is introduced into the training model, and convolution and batch standardization time layer strategies are combined, so that the stability and classification precision of the network are effectively improved. Wherein the local features of the signals are extracted through multi-scale decomposition, and the local features of the signals are matched with the motor fault features in terms of local properties, so that the motor fault diagnosis accuracy is remarkably improved, and reliable operation of the electric drive system of the electric power carrying equipment is effectively guaranteed.

The invention discloses a variable-working-condition motor fault diagnosis method based on a vibration signal, and the method comprises the steps: collecting the vibration acceleration data of a housing of a motor in various states, carrying out the data preprocessing and feature extraction, constructing a motor fault diagnosis model through employing the extracted features, carrying out the training and parameter optimization of the model, and carrying out the fault diagnosis of the motor. Therefore, the fault diagnosis model capable of quickly and accurately diagnosing the motor fault is obtained and is further used for real-time monitoring and fault diagnosis of the motor, so that the monitoring cost and the time cost are reduced, the accuracy of motor fault diagnosis is improved, and the safety and the stability of a motor system are improved.

The invention relates to the field of motor control, and particularly discloses a motor parameter identification method and system, and the method comprises the steps: building a to-be-identified parameter model based on a motor dynamics model considering external disturbance and nominal deviation; gaussian process regression training is carried out on the training data of the to-be-identified parameter model to obtain prior distribution; and obtaining posterior distribution of test data of the to-be-identified parameter model based on the prior distribution, and performing parameter identification on the to-be-identified parameter model based on the posterior distribution to obtain a target parameter model. Priori distribution is established through Gaussian process regression training, posterior distribution under a given input condition is presumed in combination with the prior distribution, parameter identification is performed on the to-be-identified parameter model based on the posterior distribution, and the obtained target parameter model has statistical significance and relatively high generalization ability. High-dimension, small-sample and non-linear term motor parameter identification can be realized with relatively high precision, and the efficiency and stability of motor system operation are improved.

The invention belongs to the technical field of motor control, and more specifically relates to a stepping motor system resonance detection and processing method. The stepping motor system comprises a stepping motor connected with a stepping motor controller, a first gyroscope, a second gyroscope and a bearing, the first gyroscope is fixedly arranged on the portion, on one side of a non-output shaft of the stepping motor, of a body shell, and a non-rotating platform is built on an output shaft, connected with a load, of the stepping motor system. The platform is connected with an output shaft of the stepping motor through a bearing, the platform is connected with the fixing body through a universal joint, a second gyroscope is installed on the platform, and the first gyroscope and the second gyroscope transmit data to a stepping motor controller through wireless transmission; whether vibration detected by the first gyroscope and vibration detected by the second gyroscope are consistent or not is judged, if not, the stepping motor system does not resonate, otherwise, the stepping motor system resonates, and the PWM frequency and the torque pulse frequency of the stepping motor are shifted out of the inherent frequency range of the stepping motor.

The invention discloses a motor system fault qualitative diagnosis method based on current sample feature classification analysis, which comprises the following steps: S1, carrying out multi-point sampling on a current sample of a motor system to obtain an original current signal, and converting the original current signal into a comprehensive current signal; s2, constructing a pre-training data set and a deep learning model, and obtaining a pre-training weight coefficient matrix; s3, fault type, fault degree and fault position information classification is carried out on the fault samples; s4, establishing a fault current vector and a typical fault average current vector; s5, sampling motor current in real time, calculating a fault comprehensive current signal and constructing a fault prediction current vector; and S6, calculating the vector difference between the fault prediction current vector and the typical fault average current vector, and determining the fault type through the minimum value of the vector difference. According to the invention, rapid identification and accurate diagnosis of motor faults can be realized, the method has the advantages of rapid response and strong adaptability, and efficient support is provided for fault diagnosis and maintenance of a motor system.

An exercise system that may be rail-less, frameless, and / or portable is described. The exercise system includes a carriage having a plurality of wheels and a stationary platform. The stationary platform is independent of and separate from the carriage, and is freestanding. A tension member having a first end is attached to the carriage and a second end is detachably attached to the stationary platform such that the carriage is movable on a horizontal plane parallel to a ground surface while the stationary platform remains in a fixed position. The carriage and the stationary platform have a width suitable for performing various exercises.

The invention relates to the technical field of motor control, in particular to a dead-beat predictive current control method for a permanent magnet synchronous motor and a motor system.The method comprises the steps that a permanent magnet synchronous motor discretization mathematical model considering control system delay under the d-q axis of a synchronous rotating coordinate system is constructed, parameter mismatch is considered, modeling is conducted on introduced disturbance, and a dead-beat predictive current is obtained; an improved generalized extended state observer is constructed, an observation value of d-q axis disturbance is further obtained, real-time accurate identification of inductance parameter changes is achieved through an inductance parameter identification method based on a model reference adaptive system, and inductance parameters in a controller and the observer are dynamically corrected based on lumped disturbance and an inductance parameter identification result. Therefore, the robust dead-beat predictive current control method of the motor is formed. According to the invention, current harmonics are effectively reduced, and the robustness and control performance of the system are greatly improved.

The invention relates to the field of fault diagnosis, discloses a brushless motor controller fault diagnosis method and system, and is used for providing powerful guarantee for reliable operation of a brushless motor controller. The method includes the steps that three-phase current and bus voltage signals are collected in real time through current and voltage sensors, and a characteristic frequency interval is extracted; the method comprises the following steps: extracting topological invariants by adopting a fractional order derivative and a continuous coherence algorithm, describing mechanical damage characteristics, taking the topological invariants and the mechanical damage characteristics as inputs, simulating motor system dynamic behaviors by virtue of a quantum state evolution model, calculating phase space trajectory divergence, triggering a graded fault decision according to the divergence, and accurately distinguishing parametric, mechanical, winding and pre-out-of-step faults. And outputting the highest-level fault instruction according to the priority and executing protection during multi-fault concurrence. The method comprehensively and accurately extracts fault features, has a dynamic adaptive capability, can predict a fault trend and effectively process a multi-fault concurrency condition, provides a solid guarantee for reliable operation of a brushless motor controller, and is suitable for multiple fields.

The invention relates to the technical field of multi-motor cooperative control, and discloses a multi-motor cooperative control system and method based on digital twinning. The multi-motor cooperative control method based on digital twinning comprises the following steps: acquiring real-time operation data of each motor in a multi-motor system, and generating a physical parameter data set containing time sequence information; based on the physical parameter data set, calculating digital twinborn model parameters of each motor, and generating a motor digital twinborn body; analyzing a topological connection relationship between the motors, and generating a cooperative control constraint condition; and inputting the real-time state data of the motor digital twin and the cooperative control constraint condition into a cooperative optimization algorithm, and outputting a multi-motor cooperative control instruction. According to the method, the problem that accurate digital mapping between a physical motor and a control system is lacked in a traditional method is solved, and the technical problem that a cooperative control strategy cannot be optimized in real time is solved.

The invention provides a motor health assessment method based on multi-dimensional parameters. The motor health assessment method comprises the following steps of S1, collecting the multi-dimensional parameters of a motor during operation through a sensor; s2, cleaning, denoising and normalizing the collected data to obtain fusion state parameters; s3, the fault samples are marked according to fault types, degrees and position information, and healthy samples and fault samples are divided; s4, constructing a health evaluation function based on the health sample; s5, solving the evaluation function to obtain a health evaluation value; and S6, evaluating the health state of the motor according to the evaluation value. According to the method, data input is optimized by fusing multi-dimensional parameter information and weighted calculation, and the health state is quantified in combination with the Euclidean distance function, so that the evaluation result is more accurate. According to the method, the health level and the fault risk of the motor can be effectively identified, and an intelligent and scientific health assessment means is provided for maintenance and operation of a motor system.

Various implementations of the systems and methods disclosed herein allow a user to build at least a portion of the musculature used for the front crawl stroke and / or offer a platform for practicing and learning proper form for certain exercises, such as, for example, swimming the front crawl stroke. In addition, the devices, systems, and methods disclosed herein may be useful for strengthening overall body tone and aerobic strength. Various implementations include an exercise system that includes an arm movement system and / or a torso movement system. The arm movement system may be provided and / or used independently of the torso movement system. Various implementations of the devices, systems and methods disclosed herein simulate the resistive forces of water encountered by a human body while swimming and allow the user to learn and maintain proper form while strengthening muscles used to counter-act those resistive forces, without the complications of a water environment.

A surgical instrument system including a motor system and a control circuit is disclosed. The motor system includes a motor and a driveline coupleable to the motor and configured to actuate a firing member through a staple firing stroke. The control circuit is coupled to the motor, where during the staple firing stroke the control circuit is configured to monitor a current draw of the motor, and initiate and provide a sequence of oscillating impact signals to the motor based on the monitored current draw of the motor. The oscillating impulse signal includes a pulse amplitude selected based on the monitored current draw and a pulse width based on the monitored current draw.

The invention relates to the technical field of motor fault diagnosis, and discloses a motor overload fault detection method and system based on locked-rotor current characteristics. The method comprises the following steps: collecting a one-phase working current signal of a target driving motor; extracting three types of locked-rotor precursor features; calculating a locked-rotor risk score by using a nonlinear integral enhancement mechanism; judging whether to enter a motor overload state or not by adopting a multi-factor dynamic threshold joint judgment mechanism; compared with the prior art that locked-rotor judgment depends on single current amplitude abrupt change or an overcurrent threshold, and especially in a climbing scene that instantaneous locked-rotor current of a motor rises suddenly due to rapid load increase, stable perception and fault-tolerant response to overload faults cannot be achieved, the technical problem that in the prior art, locked-rotor judgment depends on single current amplitude abrupt change or overcurrent threshold judgment, and in the climbing scene that the instantaneous locked-rotor current of the motor rises suddenly is solved. Due to the fact that multi-dimensional precursor feature extraction and a nonlinear integral enhancement mechanism are combined, the problem of overload misjudgment or missed judgment is avoided, and the fault detection accuracy of the motor system under the complex working condition is improved.

The invention discloses a motor fault detection method and system based on time-frequency dual channels. Fault judgment is carried out based on power grid side currents in different states and corresponding fast Fourier transform analysis; after a fault is judged, power grid side current signals under two power frequencies are collected, a fault signal is converted into a time domain image through a Grubby angle field, and the fault signal is converted into a frequency domain image through short-time Fourier transform; a residual convolutional neural network is constructed in a two-channel mode, features are extracted from time domain and frequency domain images respectively, and finally the extracted features are fused for open-circuit fault classification of different types of inverters in a motor system. According to the invention, the existing product does not need to be disassembled, so that a user can deploy conveniently; normal and fault states can be quickly distinguished, and the consumed resources are small; the fault type can be quickly identified, and a user can conveniently process the fault in time.

The invention discloses a method and a device for rapidly detecting an optimal efficiency point of a variable frequency motor system, and belongs to the technical field of motor system detection. Selecting an initial test point, and obtaining a rough search range based on the initial test point; efficiency testing is carried out on each test point in the rough search range, the optimal efficiency of the current range is obtained, and whether the global optimal efficiency is updated or not is judged; if the global optimal efficiency is updated, obtaining a high-efficiency area based on the updated global optimal efficiency; carrying out fine mesh division on the high-efficiency region, and then testing each fine mesh test point; and if the variable quantity of the efficiency value meets the preset requirement, outputting the global optimal efficiency and the corresponding torque value and rotating speed value. According to the method, the torque and rotating speed range, the step length and the test point duration can be adaptively adjusted, and the search range and the convergence condition are allowed to be customized; the testing probability of a non-efficient working condition area is reduced, meanwhile, the testing precision of an efficient working condition area is remarkably improved, and the detection efficiency and precision are effectively optimized.

An articulable sleep system has a first head section, a second head section, and a joined leg section that are each separably articulable. A head motor system includes first and second head motors operably connected to the first and second head sections. A leg motor system includes first and second leg motors operably connected to the joined leg section. A first user controlling device is hard wired to each of the first head motor, the first leg motor, and the second leg motor. A second user controlling device hard wired to each of the second head motor, the first leg motor, and the second leg motor.

The invention relates to the technical field of motor system energy saving, and discloses a motor energy-saving optimization and intelligent regulation and control platform, which comprises a motor data analysis module, a motor system simulation module, a characteristic parameter extraction module and a control strategy generation module, and is characterized in that performance analysis is performed on acquired operation data to obtain performance data; constructing a dynamic simulation model by using the obtained motor system parameters; calculating dynamic characteristics of the motor system according to the performance data and the dynamic simulation model, and performing characteristic parameter extraction on the operation data based on the dynamic characteristics to obtain mode characteristic parameters; and performing control parameter optimization iteration on the dynamic simulation model based on the mode characteristic parameters, performing strategy evaluation on an iteration result, and screening out an optimal control parameter from the candidate parameter set according to a strategy evaluation result. According to the invention, the problem that the existing motor control system is still insufficient in control accuracy in motor energy-saving optimization can be solved.

The invention provides a system for health monitoring and dynamic analysis of a bridge structure. The system is particularly suitable for collecting and analyzing non-stationary vibration signals of a linear time-varying structure for simulating trolley bridge crossing. The system comprises a time-varying structure, an excitation system, a multi-mode sensor, a measurement and analysis system, a motor system, a dynamic parameter control module and a signal processing module. Bridge structures with different time-varying characteristics are simulated by changing the mass size and the movement mode of the mass block. The two ends of the bridge are fixed to the top end of the supporting table body through screws, and the middle of the bridge is suspended. The vibration exciter makes contact with a bridge supporting body, and the motor system controls the movement mode of the movable mass block. The multi-mode sensor is vertically arranged on the bottom face of a bridge, and parameters of the mass block and the bridge are adjusted in real time through the dynamic parameter control module. The signal processing module can quickly acquire the dynamic response of the system by combining a real-time feedback control mechanism with signal analysis technologies such as wavelet transform and the like, and errors are reduced. The system effectively solves the problem that a traditional experimental device cannot accurately simulate a linear time-varying structure of a trolley gap bridge, and the dynamic monitoring and analysis capability of the bridge structure is remarkably improved.

The invention discloses a motor micro-channel heat dissipation online optimization method based on deep reinforcement learning. The method comprises the following steps: firstly, modeling a control problem of a motor micro-channel heat dissipation system as a Markov decision process; secondly, a double-delay depth deterministic strategy gradient algorithm is adopted, a deep neural network based on an actor-commentator framework is constructed to serve as a strategy network, and mapping from a state vector to an optimal control action is achieved; then, in a digital twinborn simulation environment, performing security pre-training on the strategy network to obtain an initial agent model; and finally, deploying the model to an embedded controller of an actual motor system, carrying out online fine adjustment with security constraints through online acquisition of interaction data, and adding a security fault-tolerant mechanism to ensure stable operation of the system. According to the method, self-learning can be carried out, a global optimal balance point of heat dissipation efficiency and energy consumption can be found, on-demand heat dissipation and prospective control are realized, local hot spots are effectively inhibited, and the temperature control precision and the energy-saving effect under complex working conditions are remarkably improved.

An exercise system includes a platform and a frame. The platform includes a first vertically oriented motor and a second vertically oriented motor. The first vertically oriented motor is associated with a first cable and the second vertically oriented motor is associated with a second cable. The frame includes a first adjustable attachment point and a second adjustable attachment point. The first cable is coupled to the first adjustable attachment point and the second cable is coupled to the second adjustable attachment point. The platform is located at a bottom portion of the frame.

To provide an abnormality detection device suppressing erroneous detection in an abnormality determination of a motor.SOLUTION: An abnormality detection device detecting an abnormality of a motor has an acquisition part acquiring a characteristic value with respect to the motor per sampling period, and a processing part processing the characteristic value acquired by the acquisition part. The processing part executes steps of (a) acquiring the characteristic value with respect to the motor as inspection data, (b) updating an average value of the inspection data, (c) updating a standard deviation of the inspection data, (d) updating an abnormality degree by dividing squares of a difference between the inspection data and the average value by squares of the standard deviation, and (e) calculating a cumulative sum of the abnormality degree based on a threshold value.SELECTED DRAWING: Figure 1

The invention discloses a model-free predictive control method and device for a permanent magnet synchronous motor, and the method comprises the steps: constructing a hyper-local model, carrying out the disturbance observation of the hyper-local model through an ESO observer, and generating a model-free current prediction controller; obtaining current and voltage vectors of a plurality of historical control periods, constructing a first cost function, and calculating a first descent gradient of the first cost function about an input gain value of the hyper-local model and a second descent gradient about a bandwidth value of the ESO observer; performing parameter updating based on the first descent gradient and the second descent gradient, and calculating a current prediction value at the (k + 1) th moment through the model-free current prediction controller after parameter updating; and constructing a second cost function, and based on the current predicted value at the (k + 1) th moment, selecting a switching state which enables the second cost function to be minimum to control the motor system. The method can get rid of dependence on motor parameters, and the prediction precision and control robustness of the control system are improved.

The invention discloses a permanent magnet synchronous motor adaptive speed control method based on a time-varying internal model. The method comprises the following steps: S1, establishing a permanent magnet synchronous motor mathematical model; s2, describing a speed tracking and interference suppression problem of the permanent magnet synchronous motor under the condition that the external interference frequency is unknown and time-varying as a self-adaptive servo control problem; s3, designing a time-varying internal model, and converting an adaptive servo control problem of the motor system into a robust stabilization problem of an augmented system composed of the motor system and the time-varying internal model; and S4, a speed-current loop cascade structure is adopted, a self-adaptive controller based on a time-varying internal model is designed, a PI controller is designed for a current loop, and a final controller is given. According to the method, all motor parameters are allowed to be unknown, through the adaptive controller based on the time-varying internal model, the problem of high-precision speed tracking control of the permanent magnet synchronous motor under the conditions that the external interference frequency is unknown and the time is varying is solved, and good speed tracking performance is achieved.

The invention belongs to the technical field of biology, and discloses a biological tissue transparentizing kit, an imaging method and application, the biological tissue transparentizing kit comprises a biological tissue transparentizing agent and an antibody incubation agent, and the antibody incubation agent comprises a first incubation agent and a second incubation agent; according to the tissue transparentizing reagent disclosed by the invention, the permeability of antibodies in tissues is optimized, the antibodies are uniformly distributed in the tissues, and the efficiency and effect of tissue transparentizing treatment and subsequent immunolabeling are greatly improved; the tissue transparentizing imaging method can enhance the tissue transparentizing efficiency and degree, is especially suitable for integrated visual detection of multiple tissues such as blood vessels, nerves, lymphatic vessels, muscles, osteochondral tissues and the like of a motor system, and has a better detection effect on proteins with low expression quantity and unstable properties.

Systems, devices, and methods are described for providing, among other things, a multi-axis, multi-exercise device including an extendable and articulated cervical positioning assembly; a head affixing assembly configured to secure to a head of a user; and a multi-exercise system. In an aspect, the extendable and articulated cervical positioning assembly includes at least one articulation element and at least one telescopic element configured to allow maneuverability and repositioning of the head affixing assembly with respect to the multi-exercise system. In an aspect, the multi-axis, multi-exercise device includes at least one of a mechanical resistance component, electromagnetic resistance component, magnetic resistance component, electromechanical resistance component, hydraulic resistance component, or a pneumatic resistance component.