Intelligent redundant brushless motor Hall position correction system

Abstract

The invention relates to the technical field of brushless motor control systems, and discloses an intelligent redundant brushless motor Hall position correction system, which comprises a multi-sensor redundant layout module used for arranging multiple types of sensors, and each type of sensor provides rotor position information; and the data processing and fusion module is used for receiving and processing the data from the plurality of sensors, removing noise through filtering and denoising algorithms, and outputting an accurate rotor position estimation value. According to the invention, the problem of rotor position error caused by external interference is solved through multi-sensor redundancy layout and a Kalman filtering fusion algorithm; an intelligent fault detection and switching mechanism is introduced, and the fault tolerance capability and the emergency response capability are improved through a standby sensor selection algorithm; and real-time monitoring, remote diagnosis, fault early warning and parameter adjustment of the equipment are realized through cloud platform data transmission and remote monitoring technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of brushless motor control systems, in particular to an intelligent redundant brushless motor Hall position correction system. Background Art

[0002] Brushless motors are widely used in modern industrial automation and precision control. Their high efficiency, stability, and reliability make them the preferred choice for many high-precision applications. However, with the increasing complexity of industrial environments and the increasing demand for control precision, existing technologies are becoming increasingly deficient in several areas, particularly in terms of accuracy, robustness, fault tolerance, and remote management.

[0003] First, traditional brushless motor control systems typically rely on a single sensor (such as a Hall effect sensor) to measure rotor position. While these sensors provide accurate position information under ideal conditions, they are susceptible to external interference in complex environments (such as electromagnetic interference and high temperatures), resulting in a deviation between the measured value and the actual position. This error significantly impacts the system's control accuracy, especially during dynamic conditions and long-term operation, where sensor accuracy may gradually degrade. While existing technologies use filtering and data processing to reduce interference, they often cannot cope with the dynamic changes encountered during long-term operation and complex environments, and the problem of unstable accuracy persists.

[0004] Secondly, existing motor control systems struggle to maintain long-term stability under many high-load or high-speed operating conditions. Traditionally, these systems rely on fixed algorithms for rotor position estimation. These static algorithms struggle to adapt to changing operating conditions. For example, changes in motor load, rapid speed fluctuations, or external environmental interference can all affect the system's positioning accuracy. Most existing systems can only adapt to these changes through manual calibration or adjustment of algorithm parameters. This is inefficient, increases the frequency and cost of manual intervention, and struggles to meet the demands of automation and intelligent development.

[0005] In terms of fault detection and fault tolerance, traditional brushless motor control systems typically rely on a single sensor for data collection. Consequently, any sensor failure can impact overall system performance. Existing fault detection mechanisms are mostly based on simple fault thresholds or periodic manual inspections, lacking real-time and intelligent capabilities. Consequently, when a sensor fails, the system often cannot respond immediately, potentially resulting in prolonged downtime and impacting production efficiency and equipment stability. Furthermore, traditional systems lack effective sensor redundancy and switching mechanisms. Once a sensor fails, there is typically no automatic switchover to a backup sensor, rendering the system inoperable.

[0006] Finally, most existing brushless motor control systems rely on local control and management. This approach proves inadequate when the number of devices is large and widely distributed. Technicians lack real-time access to the device's operating status, making it difficult to detect potential faults or operational anomalies. This not only reduces equipment maintenance efficiency but also increases downtime and repair costs. Consequently, existing systems lack sufficient intelligent support for remote monitoring, fault diagnosis, and remote operation, making comprehensive device management and optimization difficult. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides an intelligent redundant brushless motor Hall position correction system, which solves the problems of unstable accuracy, insufficient fault tolerance and lack of remote intelligent monitoring and management of brushless motor control systems under complex working conditions.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent redundant brushless motor Hall position correction system, comprising: Multi-sensor redundant layout module, used to arrange multiple types of sensors, and each sensor provides rotor position information; a data processing and fusion module, configured to receive and process data from the plurality of sensors, remove noise through filtering and denoising algorithms, and output an accurate rotor position estimate; Fault detection and switching module, used to monitor the working status of the sensor in real time, and automatically switch to the backup sensor when a sensor fails or data is abnormal; A Kalman filter and adaptive algorithm module is used to fuse the data of the multiple sensors and optimize the positioning accuracy of the system by adjusting the filter parameters in real time; A self-calibration and health management module, for automatically calibrating the sensor and performing health monitoring and fault warning based on device operation data; The communication and remote monitoring module is used to transmit equipment data, fault information, and health data to the remote control platform via the network.

[0009] Preferably, the sensor includes an optical encoder, a Hall sensor and a magnetic sensitive element.

[0010] Preferably, the data processing and fusion module includes: The data preprocessing unit is used to receive the raw data from each sensor and perform preliminary denoising to filter out noise caused by external environmental interference or sensor errors; The data fusion unit adopts weighted average or Kalman filter algorithm to fuse the processed data to obtain an accurate rotor position estimation value.

[0011] Preferably, the fault detection and switching module includes: A fault detection unit is used to monitor the deviation between the sensor output and the Kalman filter estimate in real time, determine whether the sensor has failed, and record the fault data; The switching decision unit decides whether to switch to the backup sensor based on the result of the fault detection unit. If a switch occurs, it sends a switching instruction to other modules and records the switching log; The backup sensor selection unit selects the most suitable backup sensor according to the current working environment and fault conditions to ensure that the system accuracy is not affected.

[0012] Preferably, the Kalman filter and adaptive algorithm module includes: The state prediction unit is used to predict the rotor position and speed at the current moment according to the control input by modeling the system dynamic equations; An updating unit, configured to calculate a Kalman gain and update a state estimation value by comparing a deviation between an actual measurement value of the sensor and a predicted value; The adaptive adjustment unit is used to dynamically adjust the process noise covariance matrix and the measurement noise covariance matrix according to the sensor data and the working environment to optimize the filtering results.

[0013] Preferably, the self-calibration and health management module includes: Automatic calibration unit, used to automatically calibrate the sensor regularly or on demand; Health monitoring unit, which detects potential equipment failures and provides maintenance recommendations based on historical data and current operating status of the equipment through big data analysis; The fault warning unit is used to issue fault warnings in advance based on the equipment status and the results of big data analysis.

[0014] Preferably, the communication and remote monitoring module includes: Remote monitoring unit, used to remotely monitor and manage device status, sensor data, and fault information via Wi-Fi or 4G / 5G communication modules; The data transmission unit is responsible for transmitting the real-time data and fault logs collected by the equipment to the cloud platform through the network for remote technicians to analyze and process; Remote operation unit, used to allow remote control system to adjust parameters, start calibration and diagnose faults.

[0015] Preferably, the multi-sensor redundant layout module optimizes the arrangement of sensors through a simulation algorithm, and the simulation algorithm includes: Based on the motor rotor motion trajectory and speed characteristics, simulate the rotor motion during motor operation to determine the optimal sensor position and angle distribution; Optimize the distribution and angle of sensors according to different working conditions, speeds, and load characteristics to ensure maximum coverage between sensors; The optimized sensor arrangement was verified through simulation analysis to ensure its stability and accuracy under various operating conditions, minimizing the measurement error of the motor rotor position.

[0016] The present invention provides an intelligent redundant brushless motor Hall position correction system. It has the following beneficial effects: 1. The present invention adopts the technical solution of multi-sensor redundant layout and Kalman filter fusion algorithm, which successfully solves the problem of inconsistency between the rotor position and the controller calculated position caused by external interference during the operation of the brushless motor. Through the layout of redundant sensors and real-time data processing, the present invention effectively avoids the measurement errors caused by single sensor failure or external noise interference in traditional solutions, and improves the robustness and accuracy of the system.

[0017] 2. The present invention ensures the high precision and robustness of the system under complex and changeable working conditions through adaptive Kalman filtering and dynamic adjustment of filtering parameters. Compared with the existing technology, the present invention can not only adapt to changes in working conditions such as motor speed and load, but also adjust the noise covariance matrix in real time, optimize the system's adaptability to dynamic changes, thereby ensuring stable operation in extreme environments such as strong electromagnetic interference, high and low temperatures.

[0018] 3. The present invention introduces an intelligent fault detection and switching mechanism, and realizes automatic fault detection and real-time switching through a backup sensor selection algorithm, thereby improving the system's emergency response capability in complex working conditions. Compared with traditional solutions that rely on manual or single backup, the present invention not only solves the problem of system shutdown caused by sensor failure through intelligent automatic switching, but also ensures the stable operation of the system to the greatest extent by dynamically assessing fault risks.

[0019] 4. The present invention realizes real-time monitoring and remote management of brushless motors through cloud platform data transmission and remote monitoring technology solutions, and transmits device status, sensor data and fault information to the cloud through wireless communication technology. The present invention breaks the limitations of traditional local control and realizes the functions of remote diagnosis, fault warning and parameter adjustment. This solution greatly improves the maintainability and management efficiency of the system, especially in scenarios with widely distributed multiple devices, ensuring timely maintenance and optimization of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system framework diagram of the present invention; Figure 2 This is a data processing and fusion module framework diagram of the present invention; Figure 3This is a framework diagram of the fault detection and switching module of the present invention; Figure 4 This is a framework diagram of the Kalman filter and adaptive algorithm module of the present invention; Figure 5 This is a framework diagram of the self-calibration and health management module of the present invention; Figure 6 This is a framework diagram of the communication and remote monitoring module of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 The embodiment of the present invention provides an intelligent redundant brushless motor Hall position correction system, comprising: Multi-sensor redundant layout module, used to arrange multiple types of sensors, and each sensor provides rotor position information; Sensors include optical encoders, Hall sensors and magnetic sensors.

[0023] The multi-sensor redundant layout module optimizes the sensor layout through simulation algorithms, including: Based on the motor rotor motion trajectory and speed characteristics, simulate the rotor motion during motor operation to determine the optimal sensor position and angle distribution; Optimize the distribution and angle of sensors according to different working conditions, speeds, and load characteristics to ensure maximum coverage between sensors; The optimized sensor arrangement was verified through simulation analysis to ensure its stability and accuracy under various operating conditions, minimizing the measurement error of the motor rotor position.

[0024] The multi-sensor redundant layout module in this embodiment is used to arrange different types of sensors and ensure that each sensor provides accurate rotor position information. This redundant multi-sensor layout ensures stable operation and high-precision positioning even when a sensor fails or experiences data anomalies. The redundant arrangement of sensors effectively reduces errors caused by environmental interference or sensor failure. The sensors include optical encoders, Hall sensors, and magnetic sensors. Each sensor works in conjunction with the others through a redundant arrangement, providing precise positioning while improving system reliability.

[0025] To further optimize system performance, this embodiment uses a simulation algorithm to optimize the sensor layout. This optimization not only applies to static operating conditions but also encompasses dynamic performance, ensuring that the sensor layout maintains high-precision measurements throughout the motor's operation. This embodiment describes in detail the various steps and calculation methods of the simulation optimization algorithm.

[0026] Generally speaking, sensor placement is crucial for accurate rotor position measurement in brushless motors. Incorrect sensor placement can lead to significant measurement errors, particularly when the motor is operating at high speed or under high load. Therefore, this embodiment employs a simulation algorithm to optimize sensor placement, maintaining low measurement errors under varying operating conditions.

[0027] In one possible implementation, the simulation algorithm takes into account factors such as the motor rotor's trajectory, speed, and load variations, and optimizes the sensor locations and angular distribution based on these factors. The goal is to maximize sensor coverage and ensure proper spacing and angular distribution between sensors to avoid data conflicts and improve position measurement accuracy.

[0028] In this embodiment, the simulation algorithm first considers the motion trajectory and speed characteristics of the motor rotor. Based on the rotor's motion during operation, the simulation algorithm simulates the motor's motion trajectory under different operating conditions. This trajectory information is used as input to further calculate the optimal sensor placement.

[0029] Specifically, to optimize the sensor position, the simulation algorithm performs the following calculations: Rotor motion trajectory simulation: Assume that the rotor position of the motor is determined by the time t and the speed v(t). The rotor position x( ) at time t can be described by the following dynamic equation: Among them, x( ) represents the position of the rotor at time t, is the rotor speed, is the speed. The speed is a function of time and is affected by the motor load, external environment and controller settings.

[0030] Sensor position optimization: Based on the rotor motion trajectory x( ) and speed v( ) simulation results, the sensor positions and angular distribution are optimized. The optimization goal is to ensure that each sensor can cover the entire range of rotor motion and avoid overlap and error accumulation between sensors. The optimization objective function can be expressed as: in, It is The measurement position of each sensor, is the target location, is the number of sensors and the goal is to minimize the measurement error.

[0031] Dynamic Optimization: Under complex operating conditions, such as high motor speeds and high loads, simulation algorithms adjust the sensor distribution and angles to ensure each sensor provides reliable position information. By adjusting the sensor layout in real time, the system minimizes errors under varying operating conditions.

[0032] To ensure that the simulation algorithm can efficiently optimize the sensor placement, the system uses a mathematical model that optimizes the objective function and constraints. First, assuming that the motion trajectory and speed of the motor rotor are known, the optimal sensor placement is obtained by minimizing the following objective function: in, Indicates the Sensors at time The measured value at is the target position, and n is the number of sensors. By minimizing this objective function, the sensor layout can be optimized to ensure that each sensor measurement is as close as possible to the true position of the motor rotor.

[0033] At the same time, the relative angles between sensors also need to be optimized to ensure that each sensor can effectively cover the entire area of ​​the rotor movement. The optimization can be performed using the following objective function: in, For the The installation angle of each sensor, To obtain the optimal angle, the optimization process adjusts the angle distribution of the sensor to reduce the error caused by unreasonable sensor layout.

[0034] The optimized sensor layout is validated through simulation analysis to ensure its stability and accuracy under various operating conditions. The simulation process uses multiple calculations to verify the performance of different sensor placements under various motor loads and speeds. By evaluating measurement errors, the optimization algorithm continuously adjusts the sensor layout to minimize measurement errors on the motor rotor under complex operating conditions.

[0035] Specifically, by simulating the motor's operating conditions, the system optimizes the system under various operating conditions, ensuring that the sensor layout not only meets the accuracy requirements under static conditions but also copes with dynamic conditions such as high speed and high load. Simulation results show that the optimized sensor layout minimizes measurement errors and provides stable rotor position output.

[0036] In general, through the aforementioned optimization simulation algorithm, the multi-sensor redundant layout module effectively optimizes sensor positions and angles, ensuring high-precision rotor position measurement in the brushless motor Hall-effect position correction system under various operating conditions. By fully considering factors such as the motor's rotor motion trajectory, speed, load characteristics, and electromagnetic interference, the system maintains highly accurate position output under complex operating conditions. This optimized sensor layout not only meets the motor's performance requirements but also verifies its stability under various operating conditions through simulation analysis, minimizing measurement errors and improving the robustness and reliability of the overall system.

[0037] Alternatively, the simulation algorithm can be further combined with actual measurement data to adjust and update the sensor layout in real time to adapt to performance changes during long-term motor operation. This not only improves the system's adaptability but also maintains high measurement accuracy over long-term operation.

[0038] Please see the attached Figure 2 ,Data processing and fusion module, which is used to receive and process data from multiple sensors, remove noise through filtering and denoising algorithms, and output accurate rotor position estimation; The data processing and fusion modules include: The data preprocessing unit is used to receive the raw data from each sensor and perform preliminary denoising to filter out noise caused by external environmental interference or sensor errors; The data fusion unit adopts weighted average or Kalman filter algorithm to fuse the processed data to obtain an accurate rotor position estimation value.

[0039] The data processing and fusion module in this embodiment receives position information from multiple sensors, performs denoising and filtering on it, and outputs an accurate rotor position estimate. This module's primary task is to ensure that the data from these multiple sensors accurately reflects the rotor position despite noise and interference, particularly under conditions of high motor speed, varying loads, or significant environmental interference. By leveraging advanced filtering algorithms and data fusion techniques, the system combines the strengths of multiple sensors, eliminates unnecessary errors, and provides more stable and accurate position information.

[0040] This module consists of two main units: a data preprocessing unit and a data fusion unit. The data preprocessing unit is responsible for initially removing noise caused by external environmental interference or sensor errors to ensure the quality of sensor data. The data fusion unit further fuses the processed data using a weighted average method or Kalman filter algorithm to obtain an accurate rotor position estimate.

[0041] Generally, raw sensor data may contain noise caused by factors such as the external environment, the sensor itself, or electromagnetic interference. In such cases, directly using the raw data can reduce the accuracy of position estimation. To ensure high-quality data in subsequent data fusion processing, this embodiment performs preliminary denoising on the sensor data using a data preprocessing unit.

[0042] Specifically, the data preprocessing unit employs a denoising algorithm to filter the raw data from each sensor. This unit uses classic filtering methods, such as low-pass and band-pass filtering, to remove high-frequency noise, resulting in cleaner and more accurate data. Systematic errors that may be introduced by the sensors themselves are also minimized by comparing and correcting them against standard models.

[0043] In some embodiments, the data preprocessing unit utilizes time-domain filtering or frequency-domain filtering techniques to locally smooth the data from each sensor, thereby suppressing errors caused by external environmental changes or sensor drift. This preprocessing allows the sensor output to more closely approximate the true position of the motor rotor.

[0044] As an option, after the data preprocessing unit completes data cleaning, the data fusion unit further fuses the processed data using a weighted average method or a Kalman filter algorithm. The weighted average method is very effective when processing simple sensor data. It assigns different weights to the output of each sensor based on its reliability and accuracy. The weighted average formula can be expressed as: in, is the fused position information, For the The output of each sensor, is the weight coefficient of the sensor, is the number of sensors. By considering the characteristics and accuracy of each sensor, the weighted average method can effectively fuse the data of multiple sensors and reduce the impact of a single sensor error on the overall system.

[0045] Specifically, the Kalman filter algorithm, as a more complex and accurate fusion method, is suitable for data processing in dynamic environments. In Kalman filtering, state estimation consists of two main components: prediction and update. In one possible implementation, the system uses the Kalman filter algorithm to estimate the position and speed of the motor rotor. The core of the Kalman filter is to use prediction and update steps to modify the current state based on sensor measurement data and previous estimates, thereby obtaining more accurate position information.

[0046] The Kalman filter algorithm consists of two main steps: prediction and update. The prediction step predicts the system state based on the system's dynamic model, while the update step modifies the prediction based on sensor data, thereby continuously optimizing the position information.

[0047] Specifically, in the prediction step, the current state of the system is estimated by the known dynamic equations : in, It is at the moment Based on control input and the state transition matrix The result of the prediction step is a preliminary estimate of the motor rotor position and speed.

[0048] Subsequently, the update step corrects the predicted value using the Kalman gain K(t) by comparing the deviation between the predicted value and the actual measured value z(t). The update formula is: in, is the updated state estimate, is the Kalman gain, is the forecast error covariance matrix, is the measurement matrix, is the measurement noise covariance matrix, is the actual sensor measurement. In this way, the system is able to combine the measurements of multiple sensors and continuously optimize its estimate of the motor rotor position.

[0049] In one possible implementation, the performance of the Kalman filter is highly dependent on the process noise covariance matrix Q ( ) and the measurement noise covariance matrix R( ). The values ​​of these matrices affect the calculation of the Kalman gain and ultimately determine the accuracy of the state estimation.

[0050] In this embodiment, the data fusion unit dynamically adjusts Q( ) and R( Specifically, by monitoring the quality and stability of sensor data in real time, the noise covariance matrix is ​​dynamically updated to ensure the stability and accuracy of the filtering process.

[0051] Generally speaking, when the sensor works in a relatively stable environment, the covariance matrix of process noise and measurement noise can be kept at a low value; when the sensor is subject to external interference or environmental changes, the value of the covariance matrix will increase appropriately, thereby enhancing the robustness of the filter.

[0052] Please see the attached Figure 3 ,Fault detection and switching module, is used to monitor the working status of the sensor in real time, and automatically switch to the backup sensor when a sensor fails or the data is abnormal; The fault detection and switching module includes: A fault detection unit is used to monitor the deviation between the sensor output and the Kalman filter estimate in real time, determine whether the sensor has failed, and record the fault data; The switching decision unit is used to decide whether to switch to the backup sensor based on the results of the fault detection unit. If a switch occurs, it sends a switching instruction to other modules and records the switching log; The backup sensor selection unit is used to select the most suitable backup sensor according to the current working environment and fault conditions.

[0053] In brushless motor control systems, sensor reliability directly impacts the accuracy of rotor position estimation. Sensor failure or abnormal output data can lead to position errors, making timely detection and switching measures crucial for system stability. The fault detection unit in this embodiment uses a comparison method with Kalman filter estimation results to determine whether a sensor fault has occurred.

[0054] Specifically, the fault detection unit monitors the real-time output data of the sensor and Kalman filter estimates When the deviation exceeds the preset tolerance threshold, the system will consider the sensor to be faulty.

[0055] The fault detection formula is as follows: in: represents the measurement value of the i-th sensor at time t; is the estimated value obtained by the Kalman filter algorithm; Is with sensor The associated measurement matrix represents the mapping from the system state vector to the measurement values.

[0056] when deviation When the value is greater than the preset threshold ϵ, the fault detection unit will consider that the sensor is faulty, and the system will record the fault data and pass the information to the switching decision unit.

[0057] Specifically, the switch decision unit determines whether to activate the backup sensor based on the fault detection unit's judgment. The core of the switch decision is to assess the impact of a sensor failure and ensure that the system switches to the backup sensor in the shortest possible time, thereby minimizing system interruption.

[0058] In one possible implementation, the handover decision unit determines whether to handover based on the following logic: When a sensor fault is detected, the impact of the fault on system performance is first evaluated; If the impact of the faulty sensor is within an acceptable range, the system can continue to use the sensor and wait for recovery; If the fault has a significant impact, or the system needs to recover accuracy immediately, the switch decision unit will activate the backup sensor.

[0059] To improve switching accuracy and responsiveness, the decision-making process of the switching decision unit can be dynamically adjusted based on the current system state (such as motor speed and load). Optionally, the switching decision unit can also evaluate the type and performance of backup sensors based on the system's real-time needs to ensure the selection of the most suitable backup sensor.

[0060] The switching decision unit also records the log of each switching, including the time of failure, failure type, switching sensor information, etc., to provide data support for subsequent maintenance and analysis.

[0061] Specifically, the backup sensor selection unit's primary task is to select the most suitable backup sensor based on the motor's current operating environment and fault conditions. This selection process not only depends on the sensor type but also takes into account the motor's current operating conditions (such as load, speed, and operating temperature), as well as the sensor's inherent stability and accuracy.

[0062] In a possible implementation, the backup sensor selection unit performs selection through the following steps: Environmental adaptability assessment: Select appropriate backup sensors based on the current operating environment (such as temperature, humidity, magnetic field, etc.). For example, in high-temperature or high-magnetic-field environments, select sensors with strong adaptability to high temperatures and high magnetic fields.

[0063] Sensor type matching: Based on the type of faulty sensor (such as Hall sensor, optical encoder, etc.), select a backup sensor that matches its performance. If the fault is a Hall sensor, the Hall sensor is preferred as the backup sensor.

[0064] Performance evaluation: Evaluate the accuracy, response speed, and stability of backup sensors, select the most suitable backup sensor, and ensure that its performance in the system does not affect the overall positioning accuracy.

[0065] In some embodiments, the backup sensor selection unit may also evaluate the load and speed of the motor. For example, when the motor is running at high speed, a sensor with faster response and higher accuracy may be selected to ensure the accuracy and response speed of the system.

[0066] Generally speaking, the workflow of the fault detection and switching module in this embodiment is as follows: Real-time monitoring of sensor status: The system acquires data from each sensor in real time and compares it with the rotor position estimated by the Kalman filter. By calculating the deviation between each sensor output and the estimated value, it determines whether a fault has occurred.

[0067] Fault diagnosis and recording: If the deviation If the set threshold is exceeded, the fault detection unit considers that the sensor has failed, the system records the relevant fault data, and transmits the information to the switching decision unit.

[0068] Switching decision: The switching decision unit evaluates the impact of the fault and decides whether to activate the backup sensor. If a switch is decided, the system will issue a switching instruction.

[0069] Backup sensor selection and activation: The backup sensor selection unit selects the most suitable backup sensor according to the working status of the motor and the type of fault sensor, and starts the sensor to work.

[0070] As an option, after the switch, the system will upload the switch information to the cloud platform or remote monitoring system through the remote monitoring module for technicians to conduct further analysis and fault diagnosis.

[0071] Kalman filter and adaptive algorithm module, used to fuse data from multiple sensors and optimize the system's positioning accuracy by adjusting filter parameters in real time; Please see the attached Figure 4 , Kalman filter and adaptive algorithm modules include: The state prediction unit is used to predict the rotor position and speed at the current moment according to the control input by modeling the system dynamic equations; An updating unit, configured to calculate a Kalman gain and update a state estimation value by comparing a deviation between an actual measurement value of the sensor and a predicted value; The adaptive adjustment unit is used to dynamically adjust the process noise covariance matrix and the measurement noise covariance matrix according to the sensor data and the working environment to optimize the filtering results.

[0072] In general, the state prediction unit is responsible for predicting the evolution of the system state based on the known system dynamic model and control inputs. Through accurate state prediction, the system can provide preliminary position information for subsequent update steps.

[0073] Specifically, the state prediction unit makes predictions based on the following system dynamic equations, assuming that the system state vector x( ) includes the rotor position x( ) and speed v( ), and control input u( ) (such as current or voltage) affects the state evolution of the system. The state update equation of the system is: in: It is at the moment +1 based on the moment The predicted state vector (i.e. rotor position and speed); A( ) is the state transition matrix, which describes the state change from the current moment to the next moment; B( ) is the control input matrix, which represents the influence of the control input on the system state; u( ) is the control input, such as the drive current or voltage of the motor.

[0074] This state prediction provides an estimate of the motor rotor's value at future moments, which serves as input to the Kalman filter update step.

[0075] Specifically, the update unit compares the predicted state The deviation between the actual sensor measurement value z(t) is used to calculate the Kalman gain and update the state estimate of the system. The update process uses the Kalman gain K( ) performs weighted fusion of measured and predicted values ​​to optimize state estimation.

[0076] The update process of Kalman filter is as follows: Kalman gain calculation: Where: K( ) is the Kalman gain, which determines the weighted ratio between the prediction and measurement results; is the forecast error covariance matrix, which represents the uncertainty in the forecast step; H( ) is the measurement matrix, which represents the mapping relationship from the system state vector to the sensor measurement value; R( ) is the measurement noise covariance matrix, which represents the noise level introduced during the measurement process; is H( ) is the inverse of the covariance matrix.

[0077] Update the state estimate: in: is the updated state estimate (i.e., the corrected rotor position and speed); z( ) is the actual measurement value from the sensor; is the predicted state estimate at the previous moment; is the measurement residual.

[0078] Through this updating process, the system can correct the predicted value according to the actual data of the sensor to obtain a more accurate rotor position estimate.

[0079] As an option, the role of the adaptive adjustment unit is to dynamically adjust the process noise covariance matrix Q ( ) and the measurement noise covariance matrix R( ), thereby optimizing the filtering effect. The noise level of the sensor in different working environments may vary. The system needs to adjust the filtering parameters according to these changes to ensure the robustness and accuracy of the filter.

[0080] Specifically, the adaptive adjustment unit dynamically adjusts the noise covariance matrix according to the following formula: Process noise covariance matrix adjustment: Measurement noise covariance matrix adjustment: in: is the process noise covariance matrix, which represents the uncertainty of the process model; R( ) is the measurement noise covariance matrix, which represents the uncertainty of the measurement error; is an adaptive factor that controls the update rate of the noise covariance matrix, usually between 0 and ≤1; R( −1) is the measurement noise covariance matrix at the previous moment; is the process noise covariance matrix at the previous moment.

[0081] ΔQ(t) and ΔR(t) are noise adjustments obtained by real-time monitoring of sensor data and environmental changes.

[0082] Specifically, in situations of significant environmental change, the system will appropriately increase the noise covariance matrix to enhance the robustness of the filter; whereas in stable operating environments, the noise covariance matrix will be gradually reduced to improve filtering accuracy. Through this dynamic adjustment, the Kalman filter algorithm can better adapt to environmental changes and provide more accurate rotor position estimates.

[0083] Typically, the Kalman filter and adaptive algorithm modules provide accurate rotor position estimates through real-time prediction and updates. The state prediction unit first predicts the rotor position and speed based on the system dynamic model. The update unit then modifies the predictions using the Kalman gain and updates them based on actual measurements. The adaptive adjustment unit dynamically adjusts the process noise covariance matrix and the measurement noise covariance matrix based on real-time noise levels and environmental changes to optimize the filtering effect.

[0084] In one possible implementation, the Kalman filter and adaptive algorithm modules iterate in real time to optimize the rotor position estimate. Each step works together through prediction, updating, and adjustment, ensuring the system provides highly accurate rotor position data in a variety of dynamic and complex environments.

[0085] Self-calibration and health management module, used to automatically calibrate sensors and perform health monitoring and fault warning based on equipment operation data; Please see the attached Figure 5 , the self-calibration and health management modules include: Automatic calibration unit, used to automatically calibrate the sensor regularly or on demand; Health monitoring unit, which detects potential equipment failures and provides maintenance recommendations based on historical data and current operating status of the equipment through big data analysis; The fault warning unit is used to issue fault warnings in advance based on the equipment status and the results of big data analysis.

[0086] Generally speaking, sensors may experience a decrease in accuracy after long-term operation or environmental changes (such as temperature and humidity), leading to measurement errors. Therefore, regular automatic calibration is necessary to ensure that sensors always maintain high accuracy.

[0087] Specifically, the automatic calibration unit automatically initiates the calibration process at predetermined time intervals or when an abnormal sensor output is detected. The process can be based on the following steps: Comparison of measured values ​​with standard values: First, the system compares the sensor output with a known standard reference value. The standard reference value can be a theoretical value, a pre-calibrated value, or data from another sensor.

[0088] Deviation calculation and correction: by comparing the deviation between the measured value and the standard value , the system adjusts the sensor according to the deviation. For example, suppose the output of a sensor is , the standard value is , then the deviation can be expressed as: if If the error exceeds the set allowable range, the system will calibrate the sensor and adjust its output value to make it as close to the standard value as possible.

[0089] Calibration strategy: Depending on the type of sensor, the system uses different calibration strategies. For example, for an optical encoder, the output may be corrected by adjusting the sensor's gain; for a Hall effect sensor, the output may be adjusted by resetting the reference voltage.

[0090] In one possible implementation, the calibration process can be connected to the cloud platform through the device control system to achieve remote calibration and real-time data upload for remote monitoring and diagnosis.

[0091] Specifically, the health monitoring unit analyzes the equipment's historical data, operating status, and real-time measurements, and combines big data analysis technology to assess the equipment's health status in real time and detect potential failure risks.

[0092] The work of the health monitoring unit includes the following aspects: Historical data analysis: The system regularly collects and stores equipment operating data, such as sensor output, speed, load, temperature, and other key parameters. This data is used to build a historical health record for the equipment.

[0093] Real-time status monitoring: The system acquires real-time data on the operating status of sensors and motors, monitoring whether sensor outputs are within normal ranges. If a sensor output value fluctuates abnormally, the system will immediately record and analyze it.

[0094] Big data analysis: By comparing and analyzing historical data and current operating status, combined with machine learning or statistical models, the system can identify potential health issues with equipment, such as gradual sensor failure, poor contact, and excessive temperature.

[0095] Health Score and Maintenance Recommendations: Based on the analysis results, the system calculates the health score of the device and provides corresponding maintenance recommendations. If the health status of the device falls below a certain threshold, the system will alert maintenance personnel and recommend repairs or component replacement.

[0096] The core of the health monitoring unit is to identify equipment problems early and predict the remaining life of the equipment through real-time data analysis and big data technology, so as to carry out maintenance in advance and avoid major failures.

[0097] As an option, the function of the fault warning unit is to issue fault warnings in advance based on the equipment status and the results of big data analysis to prevent equipment failures from being detected before they occur and avoid equipment shutdowns due to sudden failures.

[0098] Specifically, the work of the fault warning unit includes: Failure risk assessment: Based on the analysis results of the health monitoring unit, the system can assess the failure risk of each device component. For example, if a sensor has a low health score and increases in long-term output deviation, the system will identify it as a high-failure-risk component.

[0099] Real-time early warning: When a potential failure risk is detected, the system will issue an early warning signal based on the preset threshold. These early warning signals can include equipment status warnings, sensor failure alarms, high temperature alarms, etc.

[0100] Transmission and display of early warning information: Fault early warning information will be transmitted and displayed through the control system or remote monitoring platform, ensuring that technicians can understand the equipment status in a timely manner and take necessary maintenance measures.

[0101] In one possible implementation, the fault warning system could be connected to a cloud platform, leveraging cloud computing and big data analytics for data storage, calculations, and alarms. By integrating with other systems, the fault warning unit could predict the probability of equipment failure based on historical data and equipment status, and notify maintenance personnel in advance so they can take preventative measures.

[0102] Specifically, the workflow of the self-calibration and health management module in this embodiment is as follows: Automatic calibration process starts: At the specified time or when an abnormality occurs during equipment operation, the automatic calibration unit will start the sensor calibration process and adjust the sensor output value by comparing it with the standard value.

[0103] Health monitoring and data analysis: The health monitoring unit analyzes the equipment's historical operating data, monitors sensor output and motor status in real time, identifies potential faults based on big data analysis technology, and provides maintenance recommendations.

[0104] Fault warning triggering: If the health monitoring unit analyzes and finds that the equipment is in a high-risk state or potential failure, the fault warning unit will issue a warning signal and provide maintenance personnel with a detailed fault prediction.

[0105] Repair and maintenance: The system provides specific maintenance recommendations based on fault warning and health monitoring analysis results. Technicians can use this information to promptly perform repairs or replace parts.

[0106] As an option, all calibration, monitoring and warning data can be synchronized to a cloud platform in real time, facilitating remote viewing and intervention by technicians, thereby improving the maintainability of the system.

[0107] The communication and remote monitoring module is used to transmit equipment data, fault information, and health data to the remote control platform via the network.

[0108] Please see the attached Figure 6 , the communication and remote monitoring module includes: Remote monitoring unit, used to remotely monitor and manage device status, sensor data, and fault information via Wi-Fi or 4G / 5G communication modules; The data transmission unit is responsible for transmitting the real-time data and fault logs collected by the equipment to the cloud platform through the network for remote technicians to analyze and process; Remote operation unit, used to allow remote control system to adjust parameters, start calibration and diagnose faults.

[0109] Typically, the remote monitoring unit's primary task is to transmit the device's real-time status, sensor data, and fault information to a remote control platform via Wi-Fi or 4G / 5G communication modules, enabling technicians to monitor the device's operating status in real time. This unit provides real-time status feedback, including sensor data, device health, and fault logs, ensuring maintenance personnel can respond quickly and take necessary remedial measures.

[0110] Specifically, the remote monitoring unit connects to the cloud platform or remote control center based on wireless communication technologies (such as Wi-Fi, 4G, or 5G). The choice of wireless communication depends on the environmental conditions of the device and the required communication bandwidth: Wi-Fi communication: Suitable for devices installed in a relatively fixed environment and close to a network access point. Wi-Fi provides high data transmission rates and is suitable for real-time data monitoring.

[0111] 4G / 5G communication: Suitable for scenarios where devices are distributed over a wide geographical area and require connectivity via mobile networks. 4G and 5G communication provide a stable connection between devices and remote platforms, ensuring reliable remote control and data transmission.

[0112] As an option, the remote monitoring unit can also be configured with multiple network connection methods and automatically select the optimal communication method according to changes in network conditions, such as automatically switching to 4G / 5G network when the Wi-Fi network is unstable.

[0113] Specifically, the data transmission unit is responsible for transmitting real-time data, fault information, health data, etc. collected by the device to the cloud platform via the network. This unit is the foundation for remote data monitoring. It encapsulates the device's operating data and sends it to the cloud via secure and reliable communication protocols (such as TCP / IP and MQTT). The data transmission content includes: Real-time data: including rotor position information, temperature, load, speed, etc. collected by sensors.

[0114] Fault log: records fault information that occurs during equipment operation, including fault time, fault type, fault sensor, etc.

[0115] Health data: Based on the analysis results provided by the health monitoring unit, including equipment health score, potential failure prediction, remaining life and other information.

[0116] In some embodiments, the data transmission unit also features data compression and encryption to reduce network bandwidth consumption and ensure the security of the data transmission process. Data compression technology can reduce data transmission volume and improve network transmission efficiency; data encryption can protect device data from unauthorized access, ensuring data privacy and security.

[0117] As an option, the unit can also support batch transmission of data, dynamically adjusting the data transmission frequency according to the device status and network bandwidth to meet the needs of different working environments.

[0118] Specifically, the remote operation unit enables remote operation, allowing technicians to perform real-time operations on a cloud platform or remote control platform, including parameter adjustment, calibration startup, and fault diagnosis. This allows technicians to maintain and manage equipment without being on-site, improving operation and maintenance efficiency.

[0119] Specific features include: Parameter adjustment: Through remote operation, technicians can adjust the operating parameters of the equipment, such as sensor sensitivity, control strategy, load limit, etc. For example, adjust the motor speed, adjust the current input, or change the parameters of the control algorithm.

[0120] Calibration Initiation: If the system detects an abnormal sensor output, the remote operation unit can initiate an automatic calibration process. Through the remote platform, technicians can perform or manually trigger the sensor calibration process to ensure that the sensor output remains accurate.

[0121] Fault Diagnosis: The remote operation unit provides real-time access to device fault logs and health data, assisting technicians in troubleshooting. When the system detects a potential fault or performance degradation, technicians can diagnose the device through the remote platform, identify the cause, and perform repairs. Integrated fault analysis tools allow technicians to review historical fault records, sensor outputs, and health scores for data-driven fault analysis.

[0122] As an option, the remote operation unit can also integrate artificial intelligence or machine learning algorithms to automatically make adjustment recommendations based on historical data and operating patterns, further enhancing the system's intelligence. For example, the system can learn the equipment's operating patterns, automatically identify its status, and make optimization recommendations, helping technicians perform maintenance and management more efficiently.

[0123] Specifically, the workflow of the communication and remote monitoring module is as follows: Data collection and transmission: Sensors in the equipment continuously collect real-time data such as rotor position, temperature, and load, and the data transmission unit transmits this data to the cloud platform via the network.

[0124] Remote monitoring and analysis: The remote monitoring unit displays the real-time data, health status and fault information of the equipment on the remote platform, allowing technicians to view the equipment status in real time and conduct analysis.

[0125] Remote operation: When the equipment status is abnormal or needs adjustment, technicians can use the remote operation unit to adjust parameters, start calibration or diagnose faults.

[0126] Early warning and feedback: If the system detects a potential fault, the data transmission unit will promptly transmit the fault information to the remote platform. The remote operation unit allows technicians to respond and take necessary preventive measures.

[0127] As an option, by integrating the cloud platform and remote control system, the operating status of the entire equipment can be provided to technicians through a graphical interface, real-time data monitoring, fault diagnosis and maintenance suggestions, making equipment management more efficient and accurate.

[0128] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent redundant brushless motor Hall position correction system, characterized in that: include: Multi-sensor redundant layout module, used to arrange multiple types of sensors, and each sensor provides rotor position information; a data processing and fusion module, configured to receive and process data from the plurality of sensors, remove noise through filtering and denoising algorithms, and output an accurate rotor position estimate; Fault detection and switching module, used to monitor the working status of the sensor in real time, and automatically switch to the backup sensor when a sensor fails or data is abnormal; A Kalman filter and adaptive algorithm module is used to fuse the data of the multiple sensors and optimize the positioning accuracy of the system by adjusting the filter parameters in real time; A self-calibration and health management module, for automatically calibrating the sensor and performing health monitoring and fault warning based on device operation data; The communication and remote monitoring module is used to transmit equipment data, fault information, and health data to the remote control platform via the network.

2. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The sensor includes an optical encoder, a Hall sensor and a magnetic sensitive element.

3. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The data processing and fusion module includes: The data preprocessing unit is used to receive the raw data from each sensor and perform preliminary denoising to filter out noise caused by external environmental interference or sensor errors; The data fusion unit adopts weighted average or Kalman filter algorithm to fuse the processed data to obtain an accurate rotor position estimation value.

4. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The fault detection and switching module includes: A fault detection unit is used to monitor the deviation between the sensor output and the Kalman filter estimate in real time, determine whether the sensor has failed, and record the fault data; The switching decision unit is used to decide whether to switch to the backup sensor based on the result of the fault detection unit. If a switch occurs, it sends a switching instruction to other modules and records the switching log; The backup sensor selection unit is used to select the most suitable backup sensor according to the current working environment and fault conditions.

5. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The Kalman filter and adaptive algorithm module includes: The state prediction unit is used to predict the rotor position and speed at the current moment according to the control input by modeling the system dynamic equations; An updating unit, configured to calculate a Kalman gain and update a state estimation value by comparing a deviation between an actual measurement value of the sensor and a predicted value; The adaptive adjustment unit is used to dynamically adjust the process noise covariance matrix and the measurement noise covariance matrix according to the sensor data and the working environment to optimize the filtering results.

6. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The self-calibration and health management module includes: Automatic calibration unit, used to automatically calibrate the sensor regularly or on demand; Health monitoring unit, which detects potential equipment failures and provides maintenance recommendations based on historical data and current operating status of the equipment through big data analysis; The fault warning unit is used to issue fault warnings in advance based on the equipment status and the results of big data analysis.

7. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The communication and remote monitoring module includes: Remote monitoring unit, used to remotely monitor and manage device status, sensor data, and fault information via Wi-Fi or 4G / 5G communication modules; The data transmission unit is responsible for transmitting the real-time data and fault logs collected by the equipment to the cloud platform through the network for remote technicians to analyze and process; Remote operation unit, used to allow remote control system to adjust parameters, start calibration and diagnose faults.

8. The intelligent redundant brushless motor Hall position correction system according to claim 1, characterized in that: The multi-sensor redundant layout module optimizes the arrangement of sensors through a simulation algorithm, wherein the simulation algorithm includes: Based on the motor rotor motion trajectory and speed characteristics, simulate the rotor motion during motor operation to determine the optimal sensor position and angle distribution; Optimize the distribution and angle of sensors according to different working conditions, speeds, and load characteristics to ensure maximum coverage between sensors; The optimized sensor arrangement was verified through simulation analysis to ensure its stability and accuracy under various operating conditions, minimizing the measurement error of the motor rotor position.

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