2528results about "Force/torque/work measurement apparatus calibration/testing" patented technology

The invention discloses a method for correcting data of abnormal reading of a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: acquiring sensor readings, constructing a multi-sensor cross validation mechanism, establishing an anomaly detection threshold model, performing anomaly detection based on a time sequence mode, constructing a physical constraint feedback mechanism, performing dynamic drift compensation, processing the acquired readings, and performing self-correction feedback optimization. According to the method for correcting the abnormal reading data of the pressure sensor, through a physical constraint feedback mechanism, the output of the sensor is compared with a theoretical physical model for verification, and the recognition capability of nonlinear abnormality is enhanced; through dynamic drift compensation, linear and exponential drifts generated in long-term use of the sensor are separated and corrected, and accurate correction of the drifts is realized. According to the method, the reliability and the stability of pressure sensor data can be remarkably improved, a more accurate measurement basis is provided for a precise measurement scene, and meanwhile, the risk of system misinformation and failure is reduced.

The invention relates to the technical field of pressure transmitters, and discloses a high-precision self-adaptive pressure transmitter calibration method and system, and the method comprises the steps: carrying out the noise filtering processing of pressure signal data, obtaining standard signal data, carrying out the time-frequency domain joint analysis of the standard signal data, and obtaining a signal time-frequency joint representation; a dynamic compensation coefficient matrix of the target pressure transmitter is generated, and the response adaptation degree of the dynamic compensation coefficient matrix to the environment interference parameters is calculated; identifying a nonlinear error mode of the pressure transmitter, performing multi-dimensional parameter optimization on a calibration algorithm to obtain an optimal parameter, and extracting a key correction node parameter in a compensation function; adjusting factor distribution of each order of compensation item in the dynamic error correction model is analyzed, and a self-adaptive calibration instruction set of each order of compensation item in the dynamic error correction model is generated; and generating a multi-mode calibration scheme of the pressure transmitter. According to the invention, the calibration accuracy of the high-precision self-adaptive pressure transmitter can be improved.

The invention discloses an intelligent temperature compensation method for a pressure sensor, and relates to the technical field of sensor equipment. Comprising the following steps: constructing a temperature response characteristic model, collecting temperature data in real time, loading a temperature compensation algorithm on the temperature data collected in real time, updating the temperature response characteristic model in real time, performing real-time temperature compensation correction in output of a pressure sensor, and establishing an error prediction and early warning mechanism. And carrying out adaptive optimization and long-term calibration on the temperature compensation method. According to the intelligent temperature compensation method for the pressure sensor, through temperature response characteristic modeling, a temperature compensation algorithm, real-time model updating and other methods, the measurement precision and long-term stability of the sensor in a complex environment are remarkably improved, and rapid temperature difference changes and nonlinear errors can be adaptively coped with. Meanwhile, it is ensured that the temperature compensation algorithm can be continuously optimized along with environment changes and sensor aging, high applicability and expandability are achieved, and technical guarantee is provided for application of the sensor under severe working conditions.

A pressure sensor calibration method based on environmental interference compensation relates to the technical field of pressure sensor calibration, and comprises the following steps: acquiring an output value and environmental data of a target pressure sensor, marking acquisition time, and setting an acquisition period; performing statistical analysis on the environmental data of the target sensor in each historical acquisition period to obtain key values of each type of environmental parameters; a feature database is constructed, a simulation experiment is carried out based on the environment feature sets in the feature database, and pressure deviation values under different environment feature set conditions are obtained; and constructing a deviation output model, inputting the environmental data of the current acquisition period into the feature database for feature matching, and performing compensation adjustment on the target pressure sensor based on the deviation output model according to a feature matching result, or performing flexible compensation adjustment operation on the target pressure sensor. Therefore, the measurement precision, the stability and the adaptability of the pressure sensor are obviously improved.

The invention discloses a bolt pre-tightening force intelligent monitoring method and system based on acoustic characteristics, and relates to the technical field of structure monitoring, and the method comprises the steps: synchronously obtaining three types of acoustic characteristic data of resonance characteristics, sound velocity ratio and nonlinear characteristics, carrying out the interactive calibration, and generating a credibility evaluation result; according to a credibility evaluation result, dynamically adjusting weight coefficients of the three types of acoustic characteristic data in a pre-tightening force calculation weight distribution strategy; inputting the weighted three types of acoustic characteristic data into a staged fusion model by adopting a staged fusion strategy to carry out pretightening force joint calculation; and establishing a long-term monitoring database, and according to deviation analysis of historical monitoring data and a real-time resolving result, updating acquired acoustic characteristic data parameters and a weight distribution strategy to form a closed-loop calibration mechanism. The technical problems of poor dynamic adaptability of multi-source acoustic characteristic data and calculation error accumulation in bolt pre-tightening force monitoring under complex working conditions are solved.

The invention relates to the technical field of stress calibration, in particular to a real-time feedback target drone dynamic stress calibration method and system, and the method comprises the steps: 1, dividing a dynamic stress sensitive region based on a target drone three-dimensional dynamic model; 2, original stress signals output by the multi-axis stress sensor array are synchronously collected, and preprocessed stress signals are generated; 3, inputting the preprocessed stress signal into a dynamic stress compensation model; and 4, in a target aircraft controlled loading test, comparing calibrated stress data with measurement data of reference measurement equipment, reversely correcting neural network weight parameters of the dynamic stress compensation model according to a comparison difference value, updating a weight distribution strategy of a weighted least square method, and completing dynamic iteration of calibration parameters. Through combination of the multi-axis stress sensor array and the dynamic stress compensation model, accurate measurement and compensation of the dynamic stress of the target drone are realized.

The invention relates to the technical field of pressure sensing, and discloses a pressure distribution data acquisition method and device, and the method comprises the steps: carrying out the calibration and signal preprocessing of a pressure sensor array, and obtaining an initial state matrix, a preprocessing parameter set, and a response curve database; calculating a pressure change rate to obtain a pressure sampling dynamic trigger threshold value and a pressure sampling control matrix; performing partition control on the pressure data acquisition process to obtain a pressure acquisition partition control model; collecting target pressure data and inputting the target pressure data into the time weighted processing network model for feature extraction to obtain multi-modal pressure distribution feature data; the multi-modal pressure distribution characteristic data is input into a closed-loop control system, and the pressure sampling parameters and the pressure processing parameters are updated to obtain a pressure acquisition control parameter set, so that high-precision pressure distribution acquisition is realized, and the limitation of a traditional method in processing non-linear and time-varying pressure signals is solved.

The invention provides a packaging and sintering evaluation method and device for a power semiconductor device, and the method comprises the steps: carrying out the non-destructive scanning through a high-frequency acoustic microtechnique, carrying out the correction through combining with a temperature-sound velocity relation model, achieving the real-time monitoring of a sintering process, and solving a problem that the transient change cannot be captured. Through frequency domain conversion and an improved inverse Fourier transform algorithm, precise reconstruction of the microstructure of the sintering interface is realized, and the limitation that the microstructure is difficult to evaluate in the prior art is overcome. Based on the reconstructed microstructure, a detailed three-dimensional geometric model is established, and time domain finite element analysis is performed, so that comprehensive thermal stress evolution data is obtained, and the defect of surface information of a traditional method is overcome. And quantitative evaluation is carried out by using the multi-dimensional feature space and the preset discrimination model, so that comprehensive analysis of the sintering quality is realized, and the defect of lack of comprehensive quantitative evaluation capability is overcome. Through organic combination of the steps, the accuracy and comprehensiveness of packaging and sintering evaluation are remarkably improved.

The invention relates to the technical field of high-precision pressure sensing, and discloses a pressure response data calibration method and device and computer equipment, and the method comprises the steps: collecting the output response data of a pressure sensor array, and constructing a spatial feature matrix, a time feature matrix and an environment feature matrix based on the output response data; performing trilinear least square separation to obtain dynamic response data and time characteristic data; performing symmetric geometric mode decomposition to obtain linear response component data, periodic fluctuation component data and nonlinear characteristic component data; inputting a multi-branch gating circulation unit network model to carry out dynamic calibration strategy prediction to obtain a first dynamic calibration strategy; performing particle swarm iterative optimization to obtain a second dynamic calibration strategy, and generating a progressive calibration parameter set; static and dynamic pressure sensing calibration is performed on the pressure sensor array to obtain a comprehensive pressure sensing calibration result, so that the accuracy of pressure sensing calibration is improved.

The invention relates to the technical field of field data acquisition, in particular to an on-line temperature self-compensation calibration method and circuit for a force measurement transmitter. The calibration method comprises the steps that a working strain gauge and a temperature compensation gauge are synchronously excited through a two-way precision constant current source, the working strain gauge and the temperature compensation gauge are consistent in material parameter and are located in the same temperature field, and output current values of the two-way precision constant current source are equal and are controlled by a high-precision reference voltage source and a sampling resistor in a closed-loop mode. According to the invention, differential signals of the working strain gauge and the temperature compensation gauge are decoupled in real time through double-path precise constant-current source excitation and synchronous ADC acquisition technologies, gradient compensation is carried out on the temperature change rate in combination with a dynamic weighting algorithm, a lag error of traditional fixed coefficient compensation in a transient temperature field is effectively eliminated, and the accuracy of temperature compensation is improved. And online self-compensation of the temperature drift of the strain gauge is realized.

The invention provides an on-site rapid calibration combined device and method for a multi-dimensional force sensor, and the device comprises a workbench and a top plate, the top of the workbench and the bottom of the top plate are provided with connecting assemblies, the connecting assemblies are connected with standard force sensors, the standard force sensors are connected with steel wire ropes, and the two steel wire ropes are jointly connected with a loading assembly. When the tail end of the tested industrial robot moves in the X-axis direction or the Y-axis direction or the tail end of the tested industrial robot rotates, an included angle exists between the loaded steel wire rope and the steel wire rope before loading, and the device further comprises an angle recognition assembly. Through movement of the tail end of the tested industrial robot, loading and calibration of a multi-dimensional force sensor of the tested industrial robot are realized. Compared with an existing multi-dimensional force sensor calibration device, the device has the advantages that the multi-dimensional force sensor does not need to be dismounted from the tested industrial robot, and artificial installation errors are prevented from being introduced. Moreover, the calibration work of the multi-dimensional force sensor is simplified, and the influence on industrial production is reduced.

The invention relates to the technical field of sensor detection, in particular to a data acquisition and analysis system of a six-dimensional sensor, which comprises a data acquisition module used for acquiring standard output data and auxiliary data of the six-dimensional sensor, and fusing the standard output data and the auxiliary data to construct a state vector; pre-processing and labeling the state vector; and the data processing module is used for receiving the state vector in a period of fixed time as the input of the neural network model and outputting a compensation decoupling result of the state vector. According to the method, the uncertainty in a real environment is simulated by generating the dynamically changing simulation data, and rich training samples are provided for the neural network model; in combination with a reinforcement learning model, the system can adaptively adjust the behavior of the mechanical arm in a virtual environment, and the operation of the mechanical arm under different temperature and external force conditions is optimized; the dynamic simulation and optimization training mechanism not only enhances the adaptive capacity of the mechanical arm in a complex environment, but also improves the robustness and stability of a control system of the mechanical arm.

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.

The invention relates to the technical field of precision machining, in particular to a cutting force signal synchronous calibration method based on multi-sensor data fusion, which comprises the following steps of: 1, dynamically preprocessing acquired signals; step 2, constructing a reference time axis based on the spindle current signal, and realizing time domain alignment of multichannel signals by adopting a dynamic time warping algorithm; 3, establishing a feature incidence matrix containing a force-vibration-temperature coupling relation, and generating a fusion cutting force feature vector through frequency domain energy weight distribution; 4, a dynamic transfer function model is constructed, model parameters are updated in real time, and the fusion feature vector is mapped into a calibration cutting force signal; and 5, carrying out dynamic compensation on the calibration signal by adopting a nonlinear inverse compensation and residual modal screening strategy. According to the method, the accuracy of cutting force signals can be remarkably improved, errors and uncertainty in the machining process are reduced, and the production quality and efficiency are optimized.

The invention discloses a miniature piezoelectric driving type detection contact integrated laser module and a contact type calibration method, particularly relates to the technical field of precision measurement, is used for solving the problem of dynamic time-varying interference of high-voltage driving pulses on micro-force signals, and is an interference coupling path synchronous acquisition and dynamic resolving technology based on a distributed electromagnetic probe. Online identification of transient interference energy distribution characteristics is realized; a multi-dimensional parameter fusion mechanism of an interference-response dynamic model quantifies cross-domain characteristics of interference energy, system response and signal noise into a comprehensive suppression coefficient, and reverse compensation waveform injection and adaptive filter dynamic adjustment are performed, so that full life cycle offset of common-mode noise is realized, and damage of residual interference to closed-loop control is avoided; through cross-domain collaborative analysis of laser-mechanical data, a self-healing iteration mechanism of contact type calibration parameters is established, progressive pollution of dynamic interference to a calibration reference is eradicated, and long-term consistency of detection precision under complex working conditions is ensured.

An annular body includes a base, a first resistance wire, a second resistance wire, a first terminal, and a second terminal. The base surrounds a central axis and expands in a direction intersecting the central axis. Resistance values of the first and second resistance wires change according to strain of the base. The first terminal is electrically connected to the end of the first resistance wire. The second terminal is electrically connected to an end of the second resistance wire. The first terminal is at a first position in the circumferential direction. The second terminal is at a second position in the circumferential direction. When viewed in the axial direction, the central angle defined by the first position, the central axis, and the second position is equal to or greater than about 90°.

The invention provides a sensor dynamic zero point correction method based on deep Kalman filtering, and relates to the technical field of sensor zero point tracking. The method comprises the following steps: acquiring voltage signal zero point output time sequence data of a sensor; the obtained data serve as input of a deep learning network model based on Kalman filtering, the model processes the data, noise and zero drift interference are removed from the output data, dynamic zero correction is achieved, and a real measurement value of the sensor is obtained. The deep learning network model based on Kalman filtering comprises an RNN recurrent neural network module, a Pyro probability programming module and a variational inference module. According to the invention, the external interference is simulated by using the environmental vibration to carry out the experiment, and the result shows that the deep Kalman filter model provides a new idea for the dynamic zero correction of the force transducer.

The invention discloses an online calibration device and calibration method for a compatible torque sensor. Two calibration arm parts are symmetrically and detachably connected to the circumferential direction of a calibration arm mounting flange; the loading device is arranged corresponding to the lower end of one calibration arm pull rod; the first force sensor is connected to the upper surface of the output end of the loading device. The compatibility of the on-line calibration device for the compatible torque sensor is reflected in that when different torque sensors are calibrated, corresponding calibration arm mounting flanges are replaced. The invention further provides a compatible torque sensor online calibration method, different loading forces are applied through the loading device, and then the torque value T calibration value and the torque sensor output value T output value variation are calculated and calibrated, so that the linearity and the repetition precision of the torque sensor are calibrated.

The invention discloses a wireless transmission anchor rod multipoint stress sensor system, which relates to the technical field of intelligent monitoring of geotechnical engineering and comprises a data sensing and acquisition module, a data processing and transmission module, a data receiving and storage module, a data analysis and decision module and a user interaction and management module. The technical key points are as follows: multi-protocol adaptive communication and quantum encryption are carried out, a multi-protocol adaptive communication stack is based on an improved near-end strategy optimization algorithm, intelligent switching of communication protocols can be completed in an extremely short time according to an actual environment, and the intelligent switching of the communication protocols can be completed in scenes such as mountainous area tunnels with complex and changeable signals. The problem that transmission of a traditional single protocol is prone to being interrupted is effectively avoided, continuity of data transmission is guaranteed, meanwhile, the integrated quantum key distribution unit adopts a BB84 protocol and is combined with a two-factor encryption system, data transmission safety is remarkably improved, quantum attacks can be effectively resisted in a monitoring scene with the extremely high requirement for data safety in confidential work, and the security of data transmission is improved. And the data leakage risk is avoided.

The invention relates to the technical field of torque calibration, in particular to a torque calibration device which comprises a workbench, a calibration base is arranged at the top of the workbench, a calibration piece is installed in the calibration base, a waste collecting mechanism is arranged outside the calibration base, a side plate is arranged on one side of the workbench, and a transverse moving lead screw sliding table is arranged on the side plate. Two sliding tables are arranged on the transverse moving lead screw sliding table, a horizontally-arranged suspension frame is arranged on the sliding tables, lifting electric push rods are arranged on the suspension frame, a first mounting plate and a second mounting plate are mounted at the output ends of the two lifting electric push rods correspondingly, and four arc-shaped clamping blocks and a driving mechanism are mounted on the first mounting plate; a sweeping mechanism is mounted on the second mounting plate, transmission parts are arranged at the bottom of the first mounting plate and the bottom of the second mounting plate, and a control screen is further arranged on the workbench. The four arc-shaped clamping blocks are used for clamping the calibration piece, the stability of clamping the calibration piece is improved, and the accuracy of torque calibration is improved.

The invention discloses a vibrating wire sensor fault diagnosis and repair method, system and device, and a storage medium. The method comprises the steps of collecting an output signal of a vibrating wire sensor; amplitude values of all the collected output signals of the vibrating wire sensor are amplified, and a band-pass filter circuit is used for filtering; inputting the amplified and filtered data into a pre-constructed fault diagnosis and recognition model, and performing feature extraction and classification so as to recognize the vibrating wire sensor with the fault and the fault type of the vibrating wire sensor; according to the fault type, different modes are selected to repair the vibrating wire sensor with the fault; the diagnosis result and the working state of the fault diagnosis and recognition model and the real frequency of the repaired vibrating wire sensor are displayed; and storing all the data for subsequent analysis and iterative updating of a fault diagnosis and recognition model. According to the invention, fault diagnosis and online real-time monitoring can be accurately and effectively carried out on the vibrating wire sensor, and the vibrating wire sensor with faults can be rapidly repaired.

The invention belongs to the field of high-temperature sensing, and discloses a self-calibration high-temperature-resistant optical fiber temperature-vibration double-parameter sensor which comprises a packaging base, a packaging cover plate, a packaging sleeve, an optical fiber, a Bragg fiber grating and a sensitive diaphragm. The packaging cover plate is fixedly connected with the packaging base, a first diaphragm groove is formed in the top of the packaging base, a second diaphragm groove is formed in the bottom of the packaging cover plate, and the first diaphragm groove and the second diaphragm groove are oppositely arranged to form a vibration space of the sensitive diaphragm; the Bragg fiber grating is provided with a first Bragg grating and a second Bragg grating, the first Bragg grating is fixed on the packaging cover plate, and the second Bragg grating is fixedly arranged on the sensitive diaphragm; one end of the optical fiber is fixedly arranged in the central through hole and is packaged through a packaging sleeve fixed at the top of the packaging cover plate; the optical fiber end face and the sensitive diaphragm form a resonant cavity. According to the invention, temperature and vibration signals can be monitored synchronously under a high-temperature condition, self-calibration of the vibration signals is realized, and the measurement precision is improved.

The invention provides a self-diagnosis method and device for a dual-redundancy pressure sensor and electronic equipment, and the method comprises the steps: determining a stability criterion of the pressure sensor according to a data curve collected by the pressure sensor and a corresponding standard input data set; determining a data confidence criterion and a data weight of the pressure sensor according to the data curve; according to the sensitivity parameter of the pressure sensor, the environment temperature curve and the offset error parameter, the sensitivity weight, the temperature weight and the offset weight of the pressure sensor are correspondingly determined; integrating the data weight, the sensitivity weight, the temperature weight and the offset weight into an evidence weight of the pressure sensor; and determining a normal or abnormal state of the pressure sensor according to the stability criterion, the data confidence criterion and the evidence weight corresponding to the two paths of pressure sensors. The weight distribution can be dynamically adjusted by fusing various data evaluation indexes, so that the detection precision and stability of a pressure sensor system are improved.

The invention discloses a linkage differential measurement system and method suitable for micro thrust calibration. The system comprises a linkage differential measurement rack, a liquid metal radio frequency coaxial cable connector arranged on the rack, and a mass center precise regulation and control device arranged on the rack. The linkage differential measurement system provides micro-Newton-level micro thrust except environment common-mode noise; the method comprises the following steps: a torsional pendulum inclination angle control module controls a torsional pendulum inclination angle # imgabs0 #; the centroid position deviation calculation module is used for calculating centroid position deviation; judging whether the offset s of the current centroid position is less than 10 [mu] m or not; the mass center position adjusting module is used for adjusting the mass center position; the gravitational wave micro-thrust calibration torsional pendulum solves the problems that the gravitational wave thruster needs to calibrate thrust noise besides calibrating thrust, high rigidity of a coaxial cable can generate interference on motion of the gravitational wave micro-thrust calibration torsional pendulum, and the gravitational wave micro-thrust calibration torsional pendulum has extremely high requirements on sensing precision of mass center regulation and control.

The invention provides a dynamic calibration virtual system of a piezoelectric six-dimensional force sensor and a calibration method thereof, and the system collects and processes force and torque signals of the sensor in real time through integrating the six-dimensional force sensor, a data collection and processing module, a virtual simulation module, a virtual calibration module and a control module. The calibration matrix is automatically adjusted through a dynamic optimization algorithm, the virtual simulation module performs simulation calculation based on the physical characteristics of the sensor and data acquired in real time to generate optimized calibration data, so that the calibration precision is improved, the system can flexibly adjust calibration parameters in different working environments, and the calibration precision is improved. The sensor is ensured to maintain high-precision output under the change conditions of temperature, humidity and the like, the system effectively reduces coupling errors through an error correction unit and a dynamic calibration algorithm, and the stability and precision of the calibration process are improved.

The invention relates to the technical field of force sensors, and discloses a nonlinear decoupling method and system for a three-axis force sensor, and the method comprises the steps: collecting multi-dimensional original data, and carrying out the adaptive noise reduction, dynamic selection of a wavelet basis, and construction of a filtering matrix, and obtaining a noise reduction signal through calculation; constructing a dynamic PINN proxy model, fusing material parameters and environment parameters, outputting a stiffness matrix, establishing an online parameter updating mechanism, calculating the obtained optimization parameters, and constructing an enhanced dynamic compensation matrix; the output stiffness matrix and the enhanced dynamic compensation matrix are fused, and a main singular value is reserved; reconstructing to obtain a reconstructed strain field, establishing a multi-physics field coupling model to compensate quantum-thermal coupling errors, and compensating wire inductive coupling under high frequency; constructing a multi-scale graph structure, strengthening a loss function through physical constraint, performing crystal plasticity hysteresis compensation, fusing multi-scale features, and calculating and outputting a decoupling force value; and an intelligent calibration decision is established, the drift trend is predicted, and the calibration period is dynamically adjusted.

The present invention discloses a method for real-time monitoring and correction of an initial reference wavelength of a blade root load sensor of a power generation wind turbine, comprising the following steps: S1, mounting a grating optical fiber sensor on a blade, and calibrating the initial reference wavelength and sensitivity coefficient of the grating optical fiber sensor; S2, collecting the real-time wavelength of the grating optical fiber sensor; S3, calculating the real-time load of the blade based on the real-time wavelength, the initial reference wavelength and the sensitivity coefficient; S4, judging whether it is necessary to correct the initial reference wavelength of the grating optical fiber sensor based on load numerical logic; compared with the prior art, the present invention can reduce the problem of initial reference wavelength offset by correcting the initial reference wavelength of the grating optical fiber sensor, realize real-time correction, improve the load measurement accuracy, and reduce the time loss of repeated calibration.

The invention discloses a six-dimensional force / torque sensor decoupling system and method based on a width neural network, and the method comprises the steps: in a data collection and preprocessing unit, carrying out the null drift correction and normalization processing of a collected voltage signal through a weight type calibration platform and a data collection card, and forming a network model training data set; carrying out model training on the established six-dimensional force / torque sensor decoupling model of the width neural network, and solving a mapping matrix from a hidden layer to an output layer by adopting an importance score-based neuron pruning algorithm and a Moore-Pengos generalized inverse to obtain optimal model parameters; and finally, inputting the voltage signal subjected to data preprocessing to obtain a real-time output vector, and performing reverse normalization processing to obtain a decoupled six-dimensional force / torque vector. According to the method, precise decoupling of the six-dimensional force / torque sensor is achieved, and the inter-dimensional coupling error is remarkably reduced.

The invention belongs to the field of force sensor calibration, and particularly discloses a six-dimensional force sensor calibration method, system and equipment, and the method comprises the steps: obtaining the actual output data of a six-dimensional force sensor under different working conditions where calibration forces are applied: forces Fx, Fy and Fz and moments Mx, My and Mz in the X-axis direction, the Y-axis direction and the Z-axis direction; the working conditions comprise at least one of the conditions that the clamping jaw rotates to a plurality of preset angles on the XZ plane in the closed state, the clamping jaw rotates to a plurality of preset angles on the YZ plane in the closed state, and the clamping jaw is symmetrically opened to a plurality of preset angles along the Z axis on the XZ plane; based on different values and different working conditions of the calibration force, determining theoretical output data of the sensor when the calibration force of different values is applied; obtaining calibration parameters based on actual output data and theoretical output data of the sensor; the calibration parameters are used for calibrating actual output data when the six-dimensional force sensor works. According to the invention, the application effect of the six-dimensional force sensor on middle clamp instruments is improved.

The invention discloses a pressure sensor fault detection method and system and electronic equipment. The method comprises the steps of collecting historical working pressure data of a plurality of pressure sensors; constructing a prediction model based on the historical working pressure data of the plurality of pressure sensors, wherein the output of the prediction model is a predicted pressure value of the to-be-detected pressure sensor; comparing the actual pressure value of the to-be-detected pressure sensor with the corresponding predicted pressure value passing through the prediction model to generate a residual value; each statistical feature is compared with a corresponding threshold value to judge whether the to-be-detected pressure sensor breaks down or not, each statistical feature comprises a moving average value, a moving root-mean-square, a moving variance and moving energy, and each statistical feature is obtained through the residual value. According to the invention, accurate fault detection can be carried out on the pressure sensor, and the accuracy of fault detection of the pressure sensor is improved.