442870 results about "Electric machinery" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

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 relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention discloses an energy self-control base station for battery replacement based on solar power supply with independent UAV take-off and landing, which comprises a UAV take-off and landing device, an automatic battery replacement device and an energy supply device. The foldable solar panel structure is driven by a motor. The invention has simple structure and is convenient for automatic battery replacement. The UAV damage caused by UAV position deviation is effectively avoided when the platform descends. The invention adopts an electric mechanical claw to cooperate with a liftable battery compartment, so as to automatically replace UAV batteries. After the electric mechanical claw is aligned with the predetermined position, the batteries can be grasped and placed by lifting the battery compartment or the annular lifting platform, which simplifies the mechanical structure of the automatic battery replacement device.

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

Disclosed is a clamping device having an automatic direction adjustment function in a vehicle body welding conveying line, including a conveying frame. A driving motor is fixedly connected to a side wall of the conveying frame, and an output shaft end of the driving motor is fixedly connected to a rotating shaft I. The present disclosure facilitates the adjustment of a direction when vehicle bodies to be welded are conveyed on a turning conveying line, and vehicle bodies being conveyed can be corrected to the same horizontal state, to avoid a situation where the clamping device on the conveying line cannot smoothly clamp inclined vehicle bodies for conveying because the vehicle bodies cannot be in the same horizontal plane when being put in.

An aerial vehicle configured for operation within indoor spaces has a meshed construction with a housing defined by upper and lower sections having meshes provided above and below propellers and motors of the aerial vehicle. The aerial vehicle also includes a suite of sensors such as LIDAR sensors, time-of-flight sensors, cameras, ultrasonic sensors, or others. Meshes of the upper and lower sections include central openings along with spokes and concentric rings provided about the central openings. Meshes of the lower section have substantially larger central openings than meshes of the upper section, but feature more dense spokes or concentric rings beneath tips of the rotating propellers, which may be hinged or foldable in nature. Data captured by sensors of the aerial vehicle may be utilized for any purpose, such as to generate environment maps of an indoor space, or to monitor the indoor space for adverse conditions or events.

The present invention relates to the technical field of drilling machines. Disclosed is a numerical control vertical drilling machine having an automatic chip removal function. The numerical control vertical drilling machine comprises a console. A workbench is mounted at the upper end of the console; a vertical plate is connected to the upper end of the console, and a moving frame is slidably connected to the side wall of one side of the vertical plate; a lifting / lowering assembly for driving the moving frame to move in a vertical direction is mounted on the vertical plate; a movable plate is slidably connected into the moving frame, and a moving assembly used for driving the movable plate to move horizontally is mounted in the moving frame; a machining head is mounted at the lower end of the movable plate; and a mounting bracket having an L-shaped section is connected to the side wall of one side of the console. The defects in the prior art are overcome in the present invention, and by means of mutual cooperation of structures such as the console, the workbench, the moving frame, the movable plate, the machining head, a cylinder, a moving plate, a first electric push rod, a cleaning brush, a motor, a lead screw, and a second electric push rod, chips generated after part machining can be automatically cleaned, so that the burden of cleaners is relieved, thereby improving the applicability of a device.

The invention relates to the technical field of woodworking instruments and discloses a four-sided grinding machine, characterized in that it comprises a frame and a conveyor arranged thereon, a positioning guide mechanism, a left-side grinding mechanism, a right-side grinding mechanism, an upper wedge-strip grinding mechanism, an upper flat grinding mechanism, a lower wedge-strip grinding mechanism, a lower flat grinding mechanism and a chamfer grinding mechanism, wherein the left-side grinding mechanism comprises a left triangular belt grinding device, a left sponge wheel belt grinding device, a right triangular belt grinding device and a right sponge wheel belt grinding device, which are arranged on the other side of the conveyor.The left side grinding mechanism comprises a left triangular belt grinding device and a left sponge wheel belt grinding device arranged on one side of the conveyor device, and the right side grinding mechanism comprises a right triangular belt grinding device and a right sponge wheel belt grinding device arranged on the other side of the conveyor device; the upper key grinding mechanism comprises a key grinding lifting and lowering mechanism, a box, a key pressure plate assembly, and an annular grinding belt assembly; the upper flat grinding mechanism comprises an upper flat grinding motor seat, an upper drive transverse sliding seat, and an upper flat grinding assembly, and the upper drive transverse sliding seat is connected to the upper flat grinding motor seat and the upper flat grinding motor seat.The upper drive transverse sliding seat is connected between the upper flat launching motor seat and the upper flat launching assembly. The present invention is characterized by high production efficiency, good stability, and good manufacturing quality.

The disclosed embodiments relate to systems and methods for a surgical tool or a surgical robotic system. A tool driver is coupled to a distal end of a robotic arm and includes a roll drive disk driven by a rotary motor. One or more processors are configured to detect an attachment of a surgical tool to the tool driver. The surgical tool includes a roll tool disk to be engaged with the roll drive disk of the tool driver, actuate of the roll drive disk through the rotary motor, determine that a measured torque of the rotary motor exceeds a preset torque threshold for a preset period of time since the actuation, and report a successful engagement between the roll drive disk and the roll tool disk.

The invention relates to a control method and device for wind load resistance of a mooring unmanned aerial vehicle and a storage medium. The method comprises the following steps: collecting and modeling a mooring unmanned aerial vehicle to obtain a system dynamics model; state estimation is carried out to obtain system wind load disturbance data and expected attitude data; performing optimal control solution on the plurality of mooring points to obtain tension distribution control data of each mooring point; inputting into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; performing adaptive robust control on the attitude of the mooring unmanned aerial vehicle to obtain a rotating speed control instruction of each motor of the four rotors; and performing safety threshold judgment, and when any monitoring data exceeds a preset threshold, outputting a working mode switching instruction and executing safe landing control. According to the invention, a multi-stage safety protection mechanism is established, and reliable operation of the mooring unmanned aerial vehicle under extreme conditions is ensured through real-time monitoring and mode switching.

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 relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The invention discloses a water receiving mechanism for a cold drink machine and a liquid level detection method. The water receiving mechanism comprises a bearing seat and a water receiving disc, the bearing seat is installed on a box body of the cold drink machine, a motor seat is arranged at one end of the bearing seat, an ice making bin is arranged at one end of the motor seat, and one end of the ice making bin is installed on the motor seat; a sliding rail groove and a water receiving disc installed in the sliding rail groove are arranged in the other end of the bearing seat, the water receiving disc is connected with the sliding rail groove in a sliding mode, and a water receiving groove in the water receiving disc is located below the ice making bin; external water receiving channels are defined between the ice making bin and the bearing seat, are respectively positioned on two sides of the bearing seat, and are communicated with the water receiving groove; at least one liquid level detection device is arranged on the upper portion of the water receiving tank and electrically connected with a controller of the cold drink machine, and the controller is further electrically connected with a sound-light alarm device on the cold drink machine. By adopting the ice-making bin, materials flowing out of the ice-making bin can be collected, and the liquid level condition in the water pan can be monitored in real time, so that stable implementation of water receiving work of the ice-making bin is ensured, and the use comfort of a user is improved.

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.

The invention discloses a grinding and polishing control method and system based on sliding mode control, and relates to the field of robot industrial control. The method comprises the steps that a force signal of a force sensor at the tail end of a mechanical arm is collected and decomposed into a normal force component perpendicular to the surface of a workpiece; determining a deviation value between the normal force component and preset target pressure, and constructing a sliding mode surface expression of sliding mode control; determining a force control component according to the force control gain coefficient; position deviation and speed deviation are determined according to the position signals of the joint encoders and a preset polishing track; determining a position control component according to the position deviation, the speed deviation, the position gain coefficient and the speed gain coefficient; constructing a selection matrix, and performing orthogonal decomposition and superposition on the force control component and the position control component to obtain a comprehensive control instruction; and the driving signals are converted into driving signals of the joint motors, and the driving signals are transmitted to the mechanical arm for grinding and polishing. By implementing the method, force control of robot grinding and polishing can be optimized, and response delay is reduced.

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.

A solar table mobile transport with sideshift capability is described that moves a solar table to a point of installation. The solar table mobile transport comprises multiple motors that allow movement within a three-dimensional coordinate system as well as provide angular controls of pitch, yaw, and roll. Extended horizontal movement of the solar table is provided by a sideshift element that extends the solar table beyond an edge of a transport component of the solar table mobile transport. The mobile transport may incorporate wings to prevent the solar table from rotating during transportation. The wings may be adjustable such that the solar table may be maneuvered in height and pitch / roll / yaw angle into an installation instance before unloading. Such a capability of sideshft in combination of instance adjustment facilitates an installation process of the solar table.

The invention discloses a robot reinforcement learning control method and system based on a gating circulation unit, and relates to the technical field of artificial intelligence. Based on a reinforcement learning state machine, a trained student network is deployed to a real quadruped robot in a zero mapping manner, and movement control of the quadruped robot is realized; the training process of the student network is as follows: building a simulation training terrain environment of the robot, and setting different levels of terrain difficulty for each terrain; based on the distillation learning thought, establishing a teacher network and a student network based on a gating circulation unit, constructing a total loss function, and based on the constructed total loss function, fitting a motor joint angle output by the teacher network through an omnibearing simulation training terrain environment, a large-scale parallel training method and the student network; according to the robot reinforcement learning control method and system, robust motion of the robot in an unstructured terrain is realized, and the robot reinforcement learning control method and system have good anti-interference capability and self-recovery capability for a dynamic environment in which traditional control cannot be competent.

The present invention provides an intravascular blood pump system with an external motor and comprising an impeller housing and / or impeller blade(s) that may be expandable and collapsible. The blade(s) and / or impeller housing may be biased to expand or may be expanded by centrifugal forces generated during rotation of the impeller and blades an operatively connected rotational external motor.

Inspection robots with a payload engagement device are described. An example inspection robot may have a housing, a drive module, having at least one wheel and a motor, where the drive module is operatively coupled to the housing. The example inspection robot may also have a payload coupled to the drive module, where the payload includes a sensor mounted to the payload, and a payload engagement device operationally coupled to the drive module and the payload, where the payload engagement device applies a selected downward force on the payload.

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 provides a servo system optimization method and system based on algorithm control, and relates to a motor control technology, and the method comprises the steps: firstly obtaining a real-time dynamic response parameter set during the dynamic operation of a servo device, covering inertia delay, torque fluctuation and position deviation parameters, and a corresponding optimization demand set, including optimization demands for each parameter; performing multi-level control signal generation on the real-time dynamic response parameter set according to the optimization demand priority, generating a control signal sequence set matched with a first compensation signal, a second compensation signal, a third compensation signal and the like, inputting the control signal sequence set into an execution mechanism, and driving the execution mechanism to dynamically respond according to an updating track, and finally, the control signal sequence set is optimized according to a feedback signal set generated by an execution mechanism, so that operation parameters of the servo device can be comprehensively considered, control signals are generated in a targeted manner, dynamic adjustment and optimization are realized, and the performance of the servo system is effectively improved.