A hoist fault diagnosis system based on multi-sensor data fusion

Abstract

The application discloses a hoist fault diagnosis system based on multi-sensor data fusion, and belongs to the technical field of hoists. The system collects multiple multi-source sensor parameters such as bilateral synchronous deviation of a system acquisition platform, chain instantaneous slip rate, load centroid offset, start-stop acceleration kurtosis, current modulation depth and the like, constructs an operation smoothness index model, an electromechanical transmission quality factor model and a structure integrity coefficient model, and compresses multi-dimensional heterogeneous information into three normalized health coefficients; on this basis, a partial load transmission coordination degree model is established to quantize the dynamic matching degree between the slip rate and the partial load; finally, the partial load transmission coordination degree and the structure integrity coefficient are input into a brake leveling precision adjustment model to output an optimized brake parking distance overshoot, and a brake controller is driven to dynamically adjust the brake timing and the torque slope. The application significantly improves the accuracy and robustness of fault diagnosis under variable working conditions, simultaneously realizes online adaptive balance of the leveling precision and mechanical impact, and provides an effective closed-loop solution for intelligent operation and maintenance of a logistics hoist.

Description

Technical Field

[0001] This invention belongs to the field of hoist technology, and in particular relates to a hoist fault diagnosis system based on multi-sensor data fusion. Background Technology

[0002] Elevators are key equipment in logistics warehousing systems for vertical transport, widely used for the inter-floor transfer of goods such as wooden crates and pallets. The operation of logistics elevators is characterized by frequent starts and stops, variable loads, and a tight work rhythm. Once a malfunction occurs, it can easily lead to conveyor line paralysis, cargo damage, and even personal injury. Currently, fault diagnosis for elevators faces the following main technical limitations: The monitoring parameters are limited, and existing technologies mostly rely on single physical quantities such as vibration or temperature for threshold alarms. This makes it difficult to comprehensively reflect the multi-dimensional health status of the hoist's mechanical transmission, electrical drive, and load-bearing structure, resulting in low fault coverage and high false alarm rates. Furthermore, different sensor signals vary significantly in time scale, physical meaning, and fault sensitivity. Existing methods either perform simple threshold judgments or use fixed weighting, failing to fully consider the coupling relationships between multi-source signals and the issue of adaptive operation, leading to poor diagnostic robustness. Braking control is disconnected from health status. Existing leveling control strategies usually use fixed braking parameters, which do not consider the degree of deterioration of the transmission system and changes in the health status of the structure. It is difficult to achieve a dynamic balance between leveling accuracy and mechanical impact, which accelerates equipment aging. Furthermore, under non-steady-state conditions such as start-stop speed change and load change, traditional time-domain characteristics and fixed threshold methods are difficult to adapt to the fluctuations in operating conditions, which can easily lead to false alarms or missed alarms.

[0003] Therefore, there is an urgent need for a fault diagnosis system that can integrate information from multiple sensors, has adaptive working conditions, and can implement a closed-loop linkage braking control strategy to improve the safety and intelligence level of logistics elevator operation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a hoist fault diagnosis system based on multi-sensor data fusion, which solves the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a hoist fault diagnosis system based on multi-sensor data fusion, comprising; The operational stability module constructs an operational smoothness model based on the platform's bilateral synchronization deviation, acceleration kurtosis during start-up and shutdown, steady-state velocity ripple rate, and root mean square of the cargo platform's pitch angular velocity to obtain the motion smoothness index. The electromechanical transmission module constructs an electromechanical transmission quality factor model based on the motor current modulation depth, the sprocket meshing order amplitude variation coefficient, and the total distortion rate of the instantaneous torque pulsation harmonics of the motor to obtain the transmission electrical harmonic index. The main load-bearing structure module uses the vertical-horizontal vibration energy ratio, the dynamic gap change of the guide shoe, the vibration transmissibility of the base, and the drift of the structural characteristic frequency (main load-bearing structure system) to construct a structural integrity coefficient model to obtain the structural stability margin coefficient. The stability analysis module constructs a transmission matching degree model based on the instantaneous slip rate of chain transmission and the two-dimensional offset of the load centroid under the motion smoothness index and transmission electrical harmonic index to obtain the transmission matching degree. The braking stopping distance adjustment module constructs a braking leveling accuracy adjustment model based on transmission matching degree, structural stability margin coefficient and braking stopping distance overshoot, and outputs the optimized braking stopping distance overshoot.

[0006] A further technical solution: The motion smoothness index is obtained as follows: Real-time height difference between the left and right sides of the platform Import, To obtain the synchronization deviation ,in Indicates the height difference scale parameter; Acceleration kurtosis during start-stop process Import To obtain start-stop impact ,in Indicates the kurtosis increment scale parameter; Speed ​​steady-state ripple The velocity ripple is obtained by comparing it with its reference value. ; The root mean square of the loading platform's pitch angular velocity Import To obtain pitch and roll ,in Represents the pitch angular velocity scale parameter; Synchronization deviation, start-stop impact, velocity ripple, and pitch sway are imported into the running stability model to obtain the motion smoothness index. The operational stability model is expressed as follows: in, Indicates the degree of synchronization deviation. Indicates the start-stop impact. Indicates velocity ripple. Indicates pitch and roll. Indicates the synchronization deviation weight. Acceleration kurtosis weighting, Velocity ripple weighting, Pitch rate weighting The term represents the motion smoothness index, and the... ,in A higher value indicates smoother motion.

[0007] A further technical solution: The method for obtaining the transmission electrical harmonic index is as follows: The current modulation depth, sprocket meshing order amplitude variation coefficient, and total distortion rate of instantaneous torque pulsation harmonics of the motor are subjected to maximum-minimum normalization to obtain the current modulation degree, meshing variability, and torque distortion degree. The current modulation, meshing variability, and torque distortion are imported into the electromechanical transmission quality factor model to obtain the transmission electrical harmonic index. The electromechanical transmission quality factor model is expressed as follows: in, This indicates the power-mean order, and the... , Indicates the current modulation degree. Indicates the degree of meshing variability. Indicates torque distortion. This represents the current modulation weighting coefficient. This represents the weighting coefficient for meshing variability. This represents the torque distortion weighting coefficient. The term represents the harmonic index of the transmission electrical system, and the... ,in A larger value indicates that the motor current modulation, sprocket meshing variation, and torque pulsation harmonic distortion are all close to zero, the transmission chain has ideal electrical-mechanical coupling characteristics, and the conversion of electrical energy to mechanical energy is smooth and efficient.

[0008] Further technical solution: The structural stability margin coefficient is obtained as follows: The ratio of vertical to horizontal vibration energy Import To obtain the energy ratio degradation ,in Energy ratio baseline value (normal operating conditions). Indicates the energy-to-deterioration sensitivity index; The dynamic clearance change of the guide shoe Import To obtain dynamic gap degree ,in Indicates the maximum allowable dynamic gap variation; Vibration transmissivity of the base Import To obtain the degree of vibration transmission degradation ,in This indicates the maximum allowable transmission rate; Structural characteristic frequency drift Import To obtain frequency drift ,in This represents the frequency drift scale parameter; The energy ratio degradation, dynamic clearance, vibration transmission degradation, and frequency drift are imported into the structural integrity coefficient model to obtain the structural stability margin coefficient. The structural integrity coefficient model is expressed as follows: in, Indicates the degree of energy degradation. Indicates dynamic gap degree, Indicates the degree of vibration transmission degradation. Indicates frequency drift. Represents geometric weights, which are positive and sum to 1. Represents the structural stability margin coefficient, where And the A larger value indicates that the vibration energy of the main load-bearing structure is higher than normal, the guide shoe gap is stable, the vibration transmission rate of the base is low, and the modal frequency has no drift. This indicates that the frame, guide rail, connectors, etc. are in good condition and the structural stiffness is sufficient.

[0009] A further technical solution: The transmission matching degree is obtained by means of: Instantaneous slippage rate of chain drive Import To obtain the slippage index ,in Indicates the slippage limit; Import the two-dimensional offset of the load centroid. To obtain the off-center load index , Wherein represents the offset scale parameter; The slippage index is obtained by combining the off-center load index and based on the benchmark slippage model; The baseline slippage model is expressed as follows: in Indicates the off-center load index. This indicates the electrical harmonic index of the transmission. Indicates the linearity coefficient of the base eccentric load-slippage. This represents the DEHI correction factor; The tolerance bandwidth index is obtained by combining the motion smoothness index and the tolerance bandwidth model. The tolerance bandwidth model is expressed as follows: in, Indicates the smoothness index of motion. Indicates the baseline tolerance half bandwidth. This indicates the modulation slope of the MSI with respect to the bandwidth; The slip rate index, slip index, and tolerance bandwidth index are imported into the transmission matching degree model to obtain the transmission matching degree. The transmission matching degree model is expressed as follows: in, This indicates the slippage rate index. Indicates the slippage index. Tolerance bandwidth index Indicates the transmission matching degree, where And the A larger value indicates that the transmission system's response to off-center load is closer to the ideal state.

[0010] Further technical solution: The optimized method for obtaining braking stopping distance overshoot is as follows: The overshoot of braking stopping distance, transmission matching degree, and structural stability margin coefficient are imported into the compensation model to obtain the compensation index; The compensation model is expressed as follows: in Indicates the overshoot in braking stopping distance. Indicates the degree of transmission matching. Indicates the structural stability margin coefficient. Indicates the compensation index. Indicates the power decay coefficient; The transmission matching degree, structural stability margin coefficient, and compensation index are imported into the braking leveling accuracy adjustment model to obtain the optimized braking stopping distance overshoot. The braking leveling accuracy adjustment model is expressed as follows: in Indicates transmission matching degree, Indicates the structural stability margin coefficient. Indicates the compensation index. This indicates the optimized braking distance overshoot; the... , among which Determined by mechanical limits and safety strategies, it is usually taken as 0 to 2.5 times the original overshoot rating.

[0011] Beneficial effects This invention provides a hoist fault diagnosis system based on multi-sensor data fusion, which has the following advantages compared with the prior art: 1. Multi-level and multi-dimensional information fusion for comprehensive and accurate diagnosis. Fourteen quantifiable factors, including vibration, current, displacement, and temperature, are divided into three dimensions: motion smoothness, transmission electrical quality, and structural health. Operational stability index, electromechanical transmission quality factor, and structural integrity coefficient are constructed for each dimension. Then, the transmission matching status is comprehensively evaluated through the off-center load transmission coordination model. The system is clearly hierarchical and covers a complete health map of mechanical, electrical, and structural aspects.

[0012] 2. Adaptive to operating conditions and robust. Each index model adopts a dimensionless smooth mapping function, eliminating the need for fixed thresholds; the tolerance bandwidth dynamically expands and contracts with operational stability, tightening the criteria to capture early faults during stable conditions and widening the boundaries to suppress false alarms during periods of fluctuation, significantly improving diagnostic confidence under varying operating conditions.

[0013] 3. Closed-loop linkage between diagnosis and control enables online optimization of braking parameters. The off-center load transmission coordination degree and structural integrity coefficient are input into the braking leveling accuracy adjustment model. The target braking overshoot is dynamically adjusted according to the transmission matching state and structural load-bearing capacity. When the structure is healthy, the overshoot is tightened to pursue accuracy. When the structure is weakened, the overshoot is actively released to reduce impact, thus achieving an intelligent balance between leveling accuracy and equipment life.

[0014] 4. Diverse model structures avoid homogenization bias. The five core models adopt different function structures such as harmonic reciprocal, hyperbolic secant, weighted geometric mean, Gaussian kernel, and power exponential co-harmonic. Dimensionless mapping covers more than ten smooth forms such as exponential, arctangent, hyperbolic tangent, error function, and inverse proportional saturation, fully ensuring the diversity and complementarity of fused features.

[0015] 5. High feasibility of engineering implementation. All inputs can be directly quantified and acquired by general industrial sensors without the need for complex modeling; the output directly outputs a dimensional braking overshoot control target, which can be seamlessly integrated with existing PLCs or motion controllers, facilitating the retrofit and deployment on existing equipment. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0019] Please see Figure 1According to one embodiment of the present invention, a hoist fault diagnosis system based on multi-sensor data fusion includes: Based on the platform's bilateral synchronization deviation, acceleration kurtosis during start-stop process, velocity steady-state ripple rate, and root mean square of the loading platform's pitch angular velocity, an operational stability model is constructed to obtain the motion smoothness index. Based on the motor current modulation depth, the amplitude variation coefficient of the sprocket meshing order, and the total distortion rate of the instantaneous torque pulsation harmonics of the motor, an electromechanical transmission quality factor model is constructed to obtain the transmission electrical harmonic index. Based on the vertical-horizontal vibration energy ratio, the dynamic gap change of the guide shoe, the vibration transmissibility of the base, and the drift of the structural characteristic frequency (main load-bearing structural system), a structural integrity coefficient model is constructed to obtain the structural stability margin coefficient. Based on the instantaneous slip rate of chain drive and the two-dimensional offset of load centroid under the motion smoothness index and the transmission electrical harmonic index, a transmission matching degree model is constructed to obtain the transmission matching degree. A braking leveling accuracy adjustment model is constructed based on transmission matching degree, structural stability margin coefficient, and braking stopping distance overshoot, and the optimized braking stopping distance overshoot is output.

[0020] Imagine a hoist in a logistics center whose operating status may change after long-term operation. Without this system, these changes might be difficult to detect in a timely manner, or they might only be identified through threshold alarms for a single parameter, leading to false alarms or missed alarms.

[0021] The fault diagnosis system of this embodiment can achieve a comprehensive assessment and optimization of the hoist's operating status. Specifically, when the hoist is transporting goods: First, the operational stability module continuously monitors the smoothness of the hoist's operation. For example, it acquires real-time data on the synchronous deviation between the two sides of the platform using displacement sensors installed on both sides of the loading platform; it collects acceleration data during start-up and shutdown using acceleration sensors and calculates the acceleration kurtosis; it monitors the velocity ripple rate during steady-state operation using velocity sensors; and it measures the root mean square of the pitch angular velocity of the loading platform using angular velocity sensors. This raw data is input into the operational stability model, which comprehensively evaluates these parameters and generates a motion smoothness index. For example, if the synchronous deviation between the two sides of the platform increases, the acceleration kurtosis rises, and the velocity ripple rate and the root mean square of the pitch angular velocity also fluctuate, the motion smoothness index will decrease accordingly, indicating that the operational stability of the hoist has declined.

[0022] Meanwhile, the electromechanical transmission module monitors the electromechanical transmission system of the hoist. For example, it obtains the motor current modulation depth through a current sensor; monitors the vibration of the sprocket meshing area through a vibration sensor and calculates the sprocket meshing order amplitude variation coefficient; and measures the motor output torque through a torque sensor and calculates the total harmonic distortion rate of the instantaneous torque pulsation of the motor. These data are input into the electromechanical transmission quality factor model to generate a transmission electrical harmonic index. For example, if the motor current modulation depth, meshing variation coefficient, or torque distortion rate increases, the transmission electrical harmonic index will decrease accordingly, indicating that the electromechanical coupling characteristics of the electromechanical transmission system may be deteriorated.

[0023] In addition, the main load-bearing structure module assesses the health status of the hoist's main load-bearing structure. For example, it obtains the vertical-to-horizontal vibration energy ratio through multi-axial vibration sensors; monitors the dynamic gap change of the guide shoes through gap sensors; measures the vibration transmissibility of the base through vibration sensors; and monitors the drift of the structural characteristic frequency (main load-bearing structure system) through modal analysis. These data are input into the structural integrity coefficient model to generate a structural stability margin coefficient. For example, if the vertical-to-horizontal vibration energy ratio changes, the guide shoe gap fluctuation increases, the base vibration transmissibility increases, or the structural characteristic frequency drifts, the structural stability margin coefficient will decrease accordingly, indicating that the main load-bearing structure may have problems such as decreased stiffness or loose connections.

[0024] Subsequently, the stability analysis module integrates the above information. This module receives the motion smoothness index and the transmission electrical harmonic index, and combines them with the instantaneous slippage rate of the chain drive (obtained by a speed sensor or encoder) and the two-dimensional offset of the load center of gravity (obtained by a pressure sensor). These parameters are input into the transmission matching degree model to generate a transmission matching degree. For example, if both the motion smoothness index and the transmission electrical harmonic index are low, while the chain slippage rate increases and the load center of gravity offset is large, the transmission matching degree will decrease, indicating that the adaptability of the transmission system to the current load and operating conditions has deteriorated.

[0025] Finally, the braking distance adjustment module dynamically adjusts the braking strategy based on these comprehensive evaluation results. This module receives the transmission matching degree, structural stability margin coefficient, and current braking distance overshoot. These parameters are input into the braking leveling accuracy adjustment model, which calculates and outputs an optimized braking distance overshoot. For example, if the transmission matching degree is low and the structural stability margin coefficient is not ideal, it indicates that the overall health of the hoist is not ideal. The system may output a smaller optimized braking distance overshoot to ensure leveling and avoid further aggravation of equipment wear or safety risks due to excessive braking impact. In this way, the braking system can adaptively adjust according to the real-time health status of the hoist to achieve a dynamic balance between leveling accuracy and mechanical impact.

[0026] Thus, through the collaborative work of its various modules, the system achieves comprehensive perception, analysis, and decision-making regarding the multi-dimensional health status of the hoist, effectively overcoming the limitations of traditional single-parameter monitoring and fixed braking strategies.

[0027] Based on the above examples, the technical concept of this embodiment demonstrates a technical contribution.

[0028] Compared to the limitations of single-parameter monitoring in existing technologies, this system achieves a multi-dimensional and comprehensive evaluation of the hoist's operational stability, electromechanical transmission quality, and main load-bearing structure integrity through operational stability, electromechanical transmission, and main load-bearing structure modules. For example, in the above example, even if the abnormality of a single parameter such as platform bilateral synchronization deviation, motor current modulation depth, or guide shoe dynamic clearance change is insufficient to trigger a traditional threshold alarm, this system can still identify these deterioration trends through the comprehensive models of its respective modules, such as the operational stability model, electromechanical transmission quality factor model, and structural integrity coefficient model, and generate corresponding motion smoothness index, transmission electrical harmonic index, and structural stability margin coefficient. This multi-parameter fusion and model-based evaluation method improves the coverage and accuracy of fault diagnosis and reduces the risk of false alarms and missed alarms.

[0029] Furthermore, addressing the issue that existing technologies do not adequately consider the coupling relationships between multi-source signals and the adaptability to different operating conditions, this system uses a stability analysis module to correlate the motion smoothness index and the transmission electrical harmonic index with the instantaneous slippage rate of the chain drive and the two-dimensional offset of the load center of gravity, constructing a transmission matching degree model. This model can dynamically evaluate the adaptability of the transmission system to different operating conditions and loads, generating a transmission matching degree. In the example above, when the hoist experiences an increase in slippage rate and a shift in the center of gravity under a specific load, even if the motion smoothness index and the transmission electrical harmonic index only decrease, the transmission matching degree model can still identify a decrease in the matching degree between the transmission system and the current operating condition, thus providing robust diagnostic results. This deep integration and correlation analysis enables the system to better adapt to non-steady-state operating conditions such as frequent start-stop of the hoist and variable loads.

[0030] Furthermore, this system achieves closed-loop linkage between braking control and hoist health status through a braking stopping distance adjustment module. This module constructs a braking leveling accuracy adjustment model based on transmission matching degree, structural stability margin coefficient, and braking stopping distance overshoot, and outputs an optimized braking stopping distance overshoot. In the example above, when the system assesses that the transmission matching degree is low and the structural stability margin coefficient is not ideal, the braking leveling accuracy adjustment model can adjust the braking stopping distance overshoot to reduce braking impact while ensuring leveling accuracy, thus avoiding further damage to equipment that is already showing signs of deterioration. This adaptive braking strategy overcomes the drawbacks of existing fixed braking parameters, effectively extends equipment life, and improves operational safety.

[0031] In summary, the fault diagnosis system of this embodiment provides a comprehensive and adaptive solution through multi-sensor data fusion, modular comprehensive evaluation, and closed-loop linkage with braking control. It effectively solves the technical problems in existing hoist fault diagnosis technologies, such as single monitoring parameters, poor diagnostic robustness, and disconnect between braking control and health status, thereby improving the safety and intelligence level of logistics hoist operation.

[0032] Preferably, the motion smoothness index is obtained as follows: Real-time height difference between the left and right sides of the platform Import, To obtain the synchronization deviation degree ,in Indicates the height difference scale parameter; Acceleration kurtosis during start-stop process Import To obtain start-stop impact ,in Indicates the kurtosis increment scale parameter; Speed ​​steady-state ripple The velocity ripple is obtained by comparing it with its reference value. ; The root mean square of the loading platform's pitch angular velocity Import To obtain pitch and roll ,in Represents the pitch angular velocity scale parameter; Synchronization deviation, start-stop impact, velocity ripple, and pitch sway are imported into the running stability model to obtain the motion smoothness index. The operational stability model is expressed as follows: in, Indicates the degree of synchronization deviation. Indicates the start-stop impact. Indicates velocity ripple. Indicates pitch and roll. This represents the synchronization deviation weighting coefficient. Acceleration kurtosis weighting coefficient, Velocity ripple weighting coefficient, Pitch angular velocity weighting coefficient The term represents the motion smoothness index, and the... ,in A higher value indicates smoother motion.

[0033] The method for obtaining the motion smoothness index aims to quantify and synthesize multiple physical quantities that affect the smoothness of hoist operation, thereby providing a unified and comparable value. This method typically includes the acquisition, preprocessing, feature extraction, and final index fusion of raw sensor data. For example, a physical model-based approach can be used to predict and evaluate smoothness by establishing kinematic and dynamic models of the hoist; or a data-driven approach can be used to learn smoothness characteristics from a large amount of operational data through machine learning algorithms. Real-time height difference between the left and right sides of the platform This reflects the synchronicity of the hoist's loading platform in the vertical direction. If there is a height difference between the two sides when the hoist is running, it indicates a synchronization deviation, which may be caused by an unbalanced drive system, uneven wear of the guide rails, or load imbalance. This parameter can be monitored in real time by devices such as displacement sensors, laser rangefinders, or encoders installed on both sides of the platform. The synchronization deviation degree u1 is the real-time height difference between the left and right sides of the platform. The normalized and quantified metrics are used to measure the degree of platform synchronization. Their calculation formula ensures that when the height difference... As the value increases, the synchronization deviation u1 also increases, and its value ranges between 0 and 1. As a height difference scale parameter, it is used to adjust the sensitivity of the deviation to changes in height difference; acceleration kurtosis during start-stop process This is a statistical measure of the sharpness of the acceleration signal during the start-up and stop phases of the hoist. A higher kurtosis value indicates a more prominent peak in the acceleration signal, meaning a stronger impact during start-up and stop, which may cause passenger discomfort or accelerated wear on mechanical components. This parameter can be obtained by statistically calculating data collected from acceleration sensors installed on the loading platform. It is the acceleration kurtosis of the start-stop process. The quantified index is used to evaluate the smoothness of the start-stop process, and its calculation formula ensures that the acceleration kurtosis is within acceptable limits. When increased, start-stop impact It increases accordingly, and its value is between 0 and 1, where As a kurtosis increment scale parameter, it is used to adjust the impact intensity's response to kurtosis changes; Speed ​​steady-state ripple rate This reflects the magnitude of speed fluctuations during the stable operation phase of the hoist. Ideally, the steady-state operating speed should remain constant. However, due to factors such as motor control, transmission chain, or load changes, the actual speed will exhibit slight fluctuations. This parameter can be obtained by filtering and analyzing the data collected by speed sensors (such as encoders) and calculating the ratio of the speed fluctuation amplitude to the average speed. (Speed ​​ripple) It is the steady-state ripple rate of the speed The index obtained by comparing it with its reference value is used to quantify the degree of speed fluctuation, and its value is between 0 and 1. This processing method can standardize the speed ripple rate of different hoists or different working conditions, making them comparable, thereby more accurately assessing the smoothness of speed operation. Root mean square of the loading platform pitch rate The pitch and sway measure the degree of swaying of the loading platform around its horizontal axis during operation. Pitch and sway can be caused by factors such as uneven guide rails, worn guide shoes, load imbalance, or wind load, affecting operational comfort and safety. This parameter can be obtained by collecting data from an angular velocity sensor (such as a gyroscope) installed on the loading platform and calculating its root mean square value. The root mean square of the pitch angular velocity of the loading platform The quantified index is used to assess the degree of swaying of the loading platform. Its calculation formula is such that when the root mean square of the pitch angular velocity... When increased, pitch and sway It increases accordingly, and its value is between 0 and 1, where As a pitch angular velocity scale parameter, it is used to adjust the sensitivity of the sway to changes in angular velocity; The operational stability model is a mathematical model designed to integrate multiple independent stability indicators (such as synchronization deviation, start-stop shock, velocity ripple, and pitch sway) into a single motion smoothness index. By assigning weights to different indicators, the model reflects their different degrees of influence on overall stability, thereby providing a comprehensive and quantitative stability assessment result. In addition to the weighted inverse form used in this application, it can also be constructed using various methods such as fuzzy comprehensive evaluation models, neural network models, or support vector machine models. The solution proposed in this application constructs a comprehensive operational stability evaluation system by real-time monitoring and quantification of multiple key physical quantities during the operation of the hoist. Specifically, the system first collects the real-time height difference between the left and right sides of the platform. And combined with height difference scale parameters Convert it into synchronization deviation. This is to reflect the accuracy of the platform's vertical synchronization, and at the same time, the acceleration kurtosis during the start-up and shutdown process. The analysis was performed, and the kurtosis increment scaling parameter was used. The start-stop impact u2 is obtained, thereby quantifying the smoothness of the start-stop process. Furthermore, the steady-state ripple rate of the velocity is analyzed. The velocity ripple is obtained by comparing it with its reference value. To assess the speed fluctuations during steady-state operation, and further, to evaluate the root mean square of the pitch rate of the loading platform. Processing is performed, and the pitch angular velocity scale parameters are combined. Obtain pitch and roll These independent quantitative indicators are used to measure the lateral stability of the loading platform. , , , It was then imported into a stationarity model, which uses a weighted reciprocal form, where , , , These are the synchronization deviation weight, acceleration kurtosis weight, velocity ripple weight, and pitch angular velocity weight. These weight coefficients can be trained and optimized on experimental data using machine learning algorithms (such as regression analysis or neural networks) to maximize the model's fit to the actual propagation environment. Alternatively, they can be determined through expert experience assignment, analytic hierarchy process (AHP), or orthogonal experimental design. Through this weighted fusion method, the model can comprehensively consider the impact of various indicators on overall smoothness and output a motion smoothness index between 0 and 1. The higher the index value, the smoother the hoist runs. This detailed and quantitative acquisition method enables the operation stability module to provide an accurate and reliable assessment result of motion smoothness, providing a solid data foundation for subsequent stability analysis modules, braking and stopping distance adjustment modules, etc., thereby significantly improving the accuracy and effectiveness of the entire fault diagnosis system.

[0034] The following is a specific example illustrating how, in practical applications, the real-time height difference between the left and right sides of a platform can be obtained. A high-precision laser displacement sensor can be installed on the bottom of each of the left and right sides of the hoist's cargo platform to measure the distance between the platform and the bottom of the shaft in real time. The difference between the two distances can be used to determine the distance between the platform and the bottom of the shaft. For the acceleration kurtosis during the start-stop process A triaxial accelerometer can be installed at the center of the loading platform to collect acceleration data when the hoist starts and stops. The kurtosis and steady-state ripple rate can then be calculated using a digital signal processor (DSP) or embedded controller. Real-time speed data can be acquired using a high-resolution rotary encoder mounted on the hoist's main shaft. High-frequency noise is then removed using a digital filter, and the ratio of speed fluctuation to average speed during steady-state operation, as well as the root mean square of the loading platform's pitch angular velocity, are calculated. A MEMS gyroscope sensor can be installed on top of the loading platform to measure the pitch angular velocity in real time and calculate its root mean square value. All this sensor data can be transmitted to a central processing unit via industrial Ethernet or CAN bus. This central processing unit can be a high-performance industrial PC or PLC. The central processing unit processes data according to preset dimensional parameters. , , and weighting coefficients , , , The synchronization deviation is calculated sequentially according to the above formula. Start-stop impact Velocity ripple and pitch and sway Finally, the motion smoothness index is calculated using a motion stability model. The weighting coefficients can be determined through extensive experiments and calibrations of the operating data of the hoist under normal and different fault conditions.

[0035] Specifically, the method for obtaining the transmission electrical harmonic index is as follows: The current modulation depth, sprocket meshing order amplitude variation coefficient, and total distortion rate of instantaneous torque pulsation harmonics of the motor are subjected to maximum-minimum normalization to obtain the current modulation degree, meshing variability, and torque distortion degree. The current modulation, meshing variability, and torque distortion are imported into the electromechanical transmission quality factor model to obtain the transmission electrical harmonic index. The electromechanical transmission quality factor model is expressed as follows: in, This indicates the power-mean order, and the... , Indicates the current modulation degree. Indicates the degree of meshing variability. Indicates torque distortion. This represents the current modulation weighting coefficient. This represents the weighting coefficient for meshing variability. This represents the torque distortion weighting coefficient. The term represents the harmonic index of the transmission electrical system, and the... ,in A larger value indicates that the motor current modulation, sprocket meshing variation, and torque pulsation harmonic distortion are all close to zero, the transmission chain has ideal electrical-mechanical coupling characteristics, and the conversion of electrical energy to mechanical energy is smooth and efficient.

[0036] Among them, the current modulation depth refers to the degree of fluctuation of the current amplitude during motor operation, which reflects the stability of the motor power supply, the health of the motor windings and the performance of the control system. It can be obtained by performing Fourier transform or wavelet analysis on the motor phase current signal to extract the amplitude change of specific frequency components, or by calculating the envelope of the current signal and analyzing the degree of fluctuation of the envelope. The coefficient of variation of the meshing order amplitude refers to the degree of fluctuation of the meshing frequency and the amplitude of its harmonic vibration signals during the transmission process of the sprocket. This coefficient can effectively reflect the meshing state, wear condition, and whether there are tooth defects or installation deviations between the chain and the sprocket. It can be obtained by installing an accelerometer near the sprocket to collect vibration signals, performing spectrum analysis on the signals, extracting the amplitude of the meshing frequency and its harmonics, and then calculating the statistical variability of these amplitudes; or indirectly by monitoring the relative motion between the chain and the sprocket with a laser displacement sensor and analyzing its periodic fluctuations.

[0037] The total harmonic distortion rate of instantaneous torque ripple in a motor refers to the proportion of harmonic components other than the fundamental frequency component in the motor's output torque. It reflects the smoothness of the motor's torque output and the efficiency of converting electrical energy into mechanical energy. A high distortion rate usually indicates that the motor has electrical faults (such as winding imbalance or magnetic circuit saturation) or uneven mechanical load. It can be calculated by directly measuring the instantaneous torque by installing a high-precision torque sensor on the motor's output shaft and performing harmonic analysis on the measured signal; or by estimating it through motor voltage and current models and combining motor parameters and control strategies to infer the torque ripple situation.

[0038] Max-min normalization is a data preprocessing technique designed to transform data with different dimensions or numerical ranges into a unified interval, such as [0,1] or [-1,1]. This method eliminates the influence of differences in the original data's dimensions and numerical values ​​on subsequent model calculations, ensuring that each feature has the same weight or comparability in the model. This prevents certain features with larger values ​​from dominating the model output, improving the fairness and accuracy of the model. It can be achieved by subtracting the minimum value of the feature from each data point and then dividing by the difference between the maximum and minimum values ​​of the feature. Alternatively, in some cases, other normalization methods such as Z-score standardization (mean-standard deviation standardization) or decimal scaling can be used to adapt to different data distributions and model requirements.

[0039] Current modulation It is the motor current modulation depth after maximum-minimum normalization processing. It maps the original current fluctuation level to a standardized dimensionless range, so that the current fluctuation level between different operating conditions or different motors can be directly compared and evaluated.

[0040] Meshing variability It is the coefficient of variation of the sprocket meshing order amplitude after maximum-minimum normalization. It maps the original mechanical meshing state fluctuation to a standardized dimensionless range, which is convenient for comprehensive analysis with other indicators and reflects the mechanical health status of the chain drive system.

[0041] Torque distortion It is the total harmonic distortion rate of instantaneous torque ripple in a motor after maximum-minimum normalization. It maps the original torque output stability index to a standardized dimensionless range and is used as a standardized index to measure the motor's energy conversion efficiency and output torque quality.

[0042] The electromechanical transmission quality factor model is a mathematical model used to comprehensively evaluate the overall performance and health status of an electromechanical transmission system. This model generates a single, physically meaningful comprehensive index by weighted averaging and nonlinear transformation of multiple normalized key performance indicators (such as current modulation, meshing variability, and torque distortion). Its purpose is to compress complex, multi-dimensional electromechanical transmission status information into an easily understood and discernible numerical value, thereby providing a quantitative basis for fault diagnosis and maintenance decisions. This model can be constructed in various forms, such as based on fuzzy logic, neural networks, or statistical regression; or, as described in this application, using a power average (generalized average) combined with an exponential function, adjusting the order of the power average. and weighting coefficients It allows for flexible adjustment of the model's sensitivity to different indicators and its response to extreme situations, thereby more accurately reflecting the actual quality of the electromechanical transmission system.

[0043] The proposed solution first collects three types of raw data: motor current modulation depth, sprocket meshing order amplitude variation coefficient, and motor instantaneous torque pulsation harmonic total distortion rate. Since these data originate from different physical quantities with different dimensions and numerical ranges, to eliminate their differences and ensure fairness and comparability in subsequent model calculations, these raw data undergo max-min normalization. After normalization, dimensionless current modulation, meshing variability, and torque distortion are obtained. These standardized indicators can more objectively reflect their respective electromechanical transmission states. Subsequently, these normalized indicators are imported into the electromechanical transmission quality factor model, which adopts a power-mean combined with an exponential function, where the power-mean order... It can be adjusted according to actual needs to control the model's sensitivity to deviations in various indicators, for example, when When the value is large, the model will focus more on reflecting the indicator with the largest deviation. Additionally, by setting different weighting coefficients... , , This model can reflect the relative importance of each indicator in evaluating the quality of electromechanical transmissions. Ultimately, the model outputs a transmission electrical harmonic index between (0,1). , The closer the value is to 1, the closer the motor current modulation, sprocket meshing variation, and torque pulsation harmonic distortion are to zero. The more ideal the electromechanical coupling characteristics of the transmission chain, the smoother and more efficient the conversion process from electrical energy to mechanical energy. This systematic data processing and model building method effectively integrates multi-source heterogeneous electromechanical transmission data into a comprehensive quality index, providing accurate and reliable input for subsequent stability analysis and braking leveling precision adjustment, thereby improving the accuracy and decision-making efficiency of the entire hoist fault diagnosis system.

[0044] The following example illustrates how to obtain the drive electrical harmonic index. First, a high-precision current sensor installed on the motor power line is used to monitor the motor current in real time. Then, an embedded processor is used to perform Fast Fourier Transform (FFT) and envelope demodulation on the acquired current signal to calculate the motor current modulation depth.

[0045] Meanwhile, a piezoelectric accelerometer is installed near the sprocket bearing housing to collect vibration signals. The spectral analysis is performed by a digital signal processor (DSP) to identify the sprocket meshing frequency and its harmonic components, and to calculate its amplitude variation coefficient.

[0046] Furthermore, by integrating a torque sensor onto the motor output shaft, the instantaneous torque of the motor is directly measured, and its total harmonic distortion rate is calculated. After acquiring the aforementioned raw data, this data is transmitted to a central control unit (e.g., an industrial PC or a high-performance PLC). The software module running in this unit first performs max-min normalization processing on this data; for example, mapping the motor current modulation depth from a range of 0-10% to a current modulation depth of 0-1. The amplitude variation coefficient of sprocket meshing order is mapped from the range of 0-0.2 to the meshing variability of 0-1. The instantaneous torque ripple harmonic total distortion rate of the motor is mapped from a range of 0-15% to a torque distortion degree of 0-1. Then, these normalized ones , , and the preset power-mean order (For example, (represented by the average of the sum of squares) and weighting coefficients , , (For example, , , (This indicates that current modulation and torque distortion are relatively more important.) This information is input into the electromechanical transmission quality factor model to calculate the transmission electrical harmonic index. For example, when , 05, At 15:00, the model can calculate a DEHI value, for example, 0.92, indicating that the electromechanical transmission system is in a good operating condition.

[0047] Specifically, the structural stability margin coefficient is obtained as follows: The ratio of vertical to horizontal vibration energy Import To obtain the energy ratio degradation ,in Energy ratio baseline value (normal operating conditions). Indicates the energy ratio degradation sensitivity index. and These are parameters that have been pre-calibrated based on the hoist model and operating experience; Vertical-horizontal vibration energy ratio It is an important indicator for measuring the vibration characteristics of a hoist structure. It reflects the relative magnitude of the vibration energy of the structure in the vertical and horizontal directions. It can be obtained by collecting vibration signals from multi-axis accelerometers installed on the hoist structure and performing spectral analysis or energy calculation on the signals. The energy ratio is a key indicator of the structure's vibration characteristics. Based on the vertical-horizontal vibration energy ratio The energy ratio, obtained after transformation by a specific function, is used to quantify the degree to which the structural vibration energy ratio deviates from the normal state; the energy ratio reference value (normal operating condition). This refers to the reference value of the vertical-horizontal vibration energy ratio of the hoist under normal operating conditions. It can be obtained by taking multiple measurements and averaging them, or by statistical analysis, after the hoist has been installed and commissioned and is operating under stable conditions. The energy ratio degradation sensitivity index is also mentioned. It is used to adjust the energy ratio degradation. The ratio of vertical to horizontal vibration energy The sensitivity parameter can be adjusted according to the specific structural characteristics, material properties, and sensitivity requirements for fault diagnosis of the hoist. The dynamic clearance change of the guide shoe Import To obtain dynamic gap degree ,in Indicates the maximum allowable dynamic gap variation; Guide shoe dynamic clearance variation This refers to the real-time change in the gap between the guide shoe and the guide rail of the hoist during operation. It can be monitored in real time by displacement sensors, laser rangefinders, or eddy current sensors installed on the guide shoe or guide rail, and is known as dynamic gap tolerance. Based on the dynamic gap change of the guide shoe This value, obtained after a specific function transformation, is used to quantify the degree of degradation of the dynamic clearance between the guide shoe and the guide rail. It represents the maximum permissible variation range of the dynamic clearance. This refers to the safety threshold for the dynamic gap change between the guide shoe and the guide rail of the hoist, which can be set according to the design specifications, operating experience and safety standards of the hoist; Vibration transmissivity of the base Import To obtain the degree of vibration transmission degradation ,in Indicates the upper limit of permissible transmissibility; base vibration transmissibility This refers to the efficiency with which the hoist base transmits vibration from its supporting structure to the surrounding environment or the superstructure. It can be obtained by installing accelerometers on both the base and its supporting structure, measuring their vibration responses, and then calculating their transfer functions or power spectral density ratio. The degree of vibration transmission degradation is also considered. Based on the vibration transmissivity of the base Obtained after transformation by a specific function, this is used to quantify the degree of degradation of the vibration transmission characteristics of the base. Upper limit of transmissibility. This refers to the safe threshold for the vibration transmissibility of the base, which can be set according to the design requirements of the hoist, vibration control standards, and limitations on its impact on the surrounding environment.

[0048] Structural characteristic frequency drift Import To obtain frequency drift ,in Represents the frequency drift scale parameter; structural characteristic frequency drift amount This refers to the deviation of the natural frequency of the main load-bearing structure of the hoist from its normal state during operation. It can be obtained by performing modal analysis on the structural vibration signal to identify the natural frequency of the structure and comparing it with a reference frequency; this is known as the frequency drift. Based on structural characteristic frequency drift The frequency drift scale parameter, obtained after transformation by a specific function, is used to quantify the degree of inherent frequency drift of the structure. It is used to adjust the frequency drift. Structural characteristic frequency drift The sensitivity parameter can be adjusted according to the specific characteristics of the hoist structure, material properties, and sensitivity requirements for structural damage diagnosis.

[0049] The energy ratio degradation, dynamic clearance, vibration transmission degradation, and frequency drift are imported into the structural integrity coefficient model to obtain the structural stability margin coefficient. The structural integrity coefficient model is expressed as follows: in, Indicates the degree of energy degradation. Indicates dynamic gap degree, Indicates the degree of vibration transmission degradation. Indicates frequency drift. Represents geometric weights, which are positive and sum to 1. Represents the structural stability margin coefficient, where And the A higher value indicates that the vibration energy of the main load-bearing structure is higher than normal, the guide shoe clearance is stable, the vibration transmissibility of the base is low, and the modal frequency has no drift, indicating that the frame, guide rails, connectors, etc. are in good condition and the structural stiffness is sufficient; geometric weight These are parameters used to adjust the contribution of each degradation index in the structural integrity coefficient model to the final structural stability margin coefficient. They can be set based on expert experience or historical fault data. For example, a higher weight can be assigned to the frequency drift, which has a greater impact on structural stiffness.

[0050] As a specific implementation method, the structural stability margin coefficient can be obtained as follows: First, triaxial accelerometers and displacement sensors are installed on the main load-bearing structure of the hoist, such as at key locations like the car frame, guide rail supports, and base. The accelerometers can be used to collect vibration signals in the vertical and horizontal directions. By performing digital filtering and Fourier transform on these signals, the vertical-to-horizontal vibration energy ratio is calculated. Based on the structural characteristic frequency, a displacement sensor can be installed between the guide shoe and the guide rail to monitor the dynamic gap change of the guide shoe in real time. Meanwhile, acceleration sensors are installed at both the input and output ends of the hoist base to calculate the vibration transmissibility of the base. After acquiring this raw data, the data processing unit (e.g., an embedded controller or industrial PC) calculates the structural stability margin coefficient based on a preset mathematical model. It is used as an important input parameter for the hoist fault diagnosis system.

[0051] Specifically, the transmission matching degree is obtained as follows: Instantaneous slippage rate of chain drive Import To obtain the slippage index ,in Indicates the slippage limit; Import the two-dimensional offset of the load centroid. To obtain the off-center load index , Wherein represents the offset scale parameter; The slippage index is obtained by combining the off-center load index and based on the benchmark slippage model; The baseline slippage model is expressed as follows: in Indicates the off-center load index. This indicates the electrical harmonic index of the transmission. Indicates the linearity coefficient of the base eccentric load-slippage. This represents the DEHI correction factor; The tolerance bandwidth index is obtained by combining the motion smoothness index and the tolerance bandwidth model. The tolerance bandwidth model is expressed as follows: in, Indicates the smoothness index of motion. Indicates the baseline tolerance half bandwidth. This indicates the modulation slope of the MSI with respect to the bandwidth; The slip rate index, slip index, and tolerance bandwidth index are imported into the transmission matching degree model to obtain the transmission matching degree. The transmission matching degree model is expressed as follows: in, This indicates the slippage rate index. Indicates the slippage index. Tolerance bandwidth index Indicates the transmission matching degree, where And the A larger value indicates that the transmission system's response to off-center load is closer to the ideal state.

[0052] Among them, the instantaneous slip rate of chain drive The slippage index is the ratio of the instantaneous speed difference between the driving sprocket and the chain, or between the chain and the driven sprocket, to the speed of the driving sprocket during chain transmission. It reflects the degree of energy loss and wear in the transmission system. It can be calculated by measuring the sprocket speed and chain linear speed in real time using speed sensors installed on the sprockets and chain. It is by measuring the instantaneous slippage rate of the chain drive. Its limit value The normalization process aims to map the original slippage rate data to a unified, dimensionless range, facilitating input and comparison for subsequent models. This normalization process can be achieved through various methods such as linear scaling and logarithmic transformation. The two-dimensional offset of the load center of gravity refers to the offset distance of the load center of gravity on the hoist platform in the horizontal plane, reflecting the unevenness of the load distribution. This offset will cause uneven force on the transmission chain, exacerbating the slippage tendency. It can be obtained by installing multiple pressure sensors or weighing sensors at the bottom of the platform and combining them with geometric position information to calculate the position of the load center of gravity and thus obtain its two-dimensional offset relative to the ideal center.

[0053] Off-center load index It is obtained by normalizing the two-dimensional offset of the load centroid and is used to quantify the impact of load imbalance on the transmission system. This index converts the original offset data into a value between 0 and 1, which helps to eliminate the differences caused by different hoist sizes and load capacities.

[0054] The baseline slippage model is a mathematical function used to predict slippage under a given off-center load index. Harmonic index of transmission Below is a theoretical estimate of chain slippage in the transmission system. This model, established through empirical data or physical analysis, aims to capture the combined effects of off-center loading and electrical drive quality on chain slippage. The slippage index is... This is the output of the baseline slippage model, representing the theoretical slippage level that the drive system should have under current off-center load and electrical drive quality conditions. This index provides a benchmark reference for comparison with the actually measured slippage rate index s. In comparison, the tolerance bandwidth model is a function used to dynamically adjust the tolerance range of the transmission matching degree based on the motion smoothness index (MSI). This model assumes that the smoother the hoist operation (the larger the MSI), the lower the system's tolerance for slippage. This is the output of the tolerance bandwidth model, which defines a dynamic "tolerance interval" to measure the actual slippage exponent s. Compared with theoretical slip index Whether the deviation between them is within an acceptable range, the transmission matching degree model is a comprehensive mathematical model that takes the slippage rate index as an example. Slip Index and tolerance bandwidth index As input, the final output is the transmission matching degree. The core idea of ​​this model is to quantify the deviation between actual and theoretical slippage, and combine this with the system's tolerance to provide a comprehensive matching score, specifically the transmission matching degree. It is the final output of the transmission matching degree model. It is a value between 0 and 1, which is used to intuitively represent the matching degree of the hoist transmission system under the current operating conditions.

[0055] The solution in this application introduces the instantaneous slip rate s of the chain drive and the two-dimensional offset of the load center of mass, and combines them with the motion smoothness index. and drive electrical harmonic index A multi-dimensional transmission matching degree evaluation system was constructed. Specifically, firstly, the instantaneous slip rate s of the chain drive, which was monitored in real time, was normalized to obtain the slip rate index. This index directly reflects the actual degree of slippage in chain drive. Simultaneously, it normalizes the two-dimensional offset of the load center of gravity to obtain the off-center load index. This index quantifies the impact of load imbalance on the transmission system. Based on this, the system utilizes a benchmark slippage model to adjust the off-center load index. With the drive electrical harmonic index obtained from the electromechanical drive module Combined, the slip index is calculated. The slippage index represents the theoretically expected slippage level of the transmission system under current load bias and electrical drive quality, providing an important reference benchmark for actual slippage conditions.

[0056] To make the evaluation more adaptive, the system further utilizes the tolerance bandwidth model to obtain the motion smoothness index from the operational stability module. As input, the tolerance bandwidth index is calculated. This index dynamically defines the acceptable deviation range between actual slippage and theoretical slippage based on the overall operational stability of the hoist, so that the evaluation of transmission matching degree can fully consider the operating conditions of the system.

[0057] Ultimately, the actual slippage rate index will be... Theoretical slip index and dynamic tolerance bandwidth index Import the transmission matching degree model and calculate the transmission matching degree. This model quantifies the deviation between actual and theoretical slippage and, combined with dynamic tolerance range, comprehensively evaluates the transmission system's response to off-center loads and its overall health. In this way, the proposed solution can not only identify simple slippage phenomena but also deeply analyze the underlying causes, such as excessive off-center load, deterioration of electrical drive quality, or insufficient overall system stability. This solves the problem of relying solely on a single indicator to comprehensively evaluate the transmission system's matching degree. The acquisition of this information enables the system to more accurately diagnose potential faults in the drive train and provides a more reliable basis for subsequent braking leveling accuracy adjustments.

[0058] The following example illustrates the instantaneous slippage rate of chain drives. Real-time measurement can be achieved by installing a high-precision rotary encoder and a linear displacement sensor on the drive sprocket shaft and chain, respectively. The rotary encoder acquires the angular velocity of the sprocket, while the linear displacement sensor measures the instantaneous linear velocity of the chain. By converting the chain linear velocity into an equivalent sprocket linear velocity and comparing it with the actual sprocket linear velocity, the instantaneous slip rate can be calculated. ,For example, It can be set to 0.05, meaning that a slippage rate of 5% is considered the limit.

[0059] The two-dimensional offset of the load's center of mass can be achieved by installing piezoelectric or resistance strain gauge load cells at the four corners of the loading platform. These sensors collect pressure data at each point in real time. Then, using a preset geometric model and algorithm, the coordinates of the load's center of mass on the XY plane are calculated, thus obtaining its offset distance relative to the geometric center of the loading platform, such as the offset scale parameter. The baseline off-center load-slip linear coefficient in the benchmark slippage model can be set according to the dimensions of the loading platform and the maximum allowable off-center load. and Correction coefficient This can be determined by conducting a series of load and no-load tests during the hoist commissioning phase. Under different off-center loads and different electrical drive qualities (by adjusting motor parameters or simulating faults), the actual slippage rate is recorded, and then regression analysis is used to fit the result. and The value of the baseline tolerance half-bandwidth in the tolerance bandwidth model. and Modulation slope of bandwidth This setting can also be achieved through experiments and experience. For example, when the hoist is running normally and the Motion Smoothness Index (MSI) is high, It can be set to a smaller value, indicating a lower tolerance for slippage deviation; while when MSI decreases, This allows for an appropriate increase in the tolerance bandwidth. All these calculations and model processing can be implemented on a high-performance industrial controller that receives data from various sensors, runs pre-programmed algorithms and models, and outputs the transmission matching degree in real time. .

[0060] Specifically, the optimized method for obtaining the braking stopping distance overshoot is as follows: The overshoot of braking stopping distance, transmission matching degree, and structural stability margin coefficient are imported into the compensation model to obtain the compensation index; The compensation model is expressed as follows: in Indicates the overshoot in braking stopping distance. Indicates the degree of transmission matching. Indicates the structural stability margin coefficient. Indicates the compensation index; The transmission matching degree, structural stability margin coefficient, and compensation index are imported into the braking leveling accuracy adjustment model to obtain the optimized braking stopping distance overshoot. The braking leveling accuracy adjustment model is expressed as follows: in Indicates transmission matching degree, Indicates the structural stability margin coefficient. Indicates the compensation index. This indicates the optimized braking distance overshoot; the... , among which Determined by mechanical limits and safety strategies, it is usually taken as 0 to 2.5 times the original overshoot rating.

[0061] Among them, braking stopping distance overshoot Overshoot refers to the deviation between the actual stopping position and the target stopping position of the hoist during braking. It reflects the accuracy and responsiveness of the braking system. This parameter is usually measured in real time by sensors or estimated by the control system. For example, it can be monitored in real time by displacement sensors (such as laser rangefinders or encoders) installed on the hoist car or cargo platform, and compared with the target stopping position to calculate the overshoot; or it can be predicted and estimated by analyzing the speed curve and braking command during braking, combined with the dynamic model of the hoist.

[0062] Transmission matching degree This reflects the ideal degree of the hoist transmission system's response to off-center load. The larger the value, the better the performance of the transmission system. This parameter can be the output result calculated by the stability analysis module based on the instantaneous slip rate of the chain drive, the two-dimensional offset of the load center of mass, the transmission electrical harmonic index, and the motion smoothness index; or it can be obtained by real-time monitoring of the tension, vibration, and load distribution of the transmission chain, combined with a preset performance model for evaluation.

[0063] Structural stability margin coefficient This parameter characterizes the integrity and stiffness adequacy of the main load-bearing structure of the hoist. A higher value indicates a better structural condition. This parameter can be calculated by the main load-bearing structure module based on the vertical-horizontal vibration energy ratio, guide shoe dynamic clearance change, base vibration transmissibility, and structural characteristic frequency drift (main load-bearing structure system). Alternatively, it can be obtained by monitoring parameters such as stress, deformation, and vibration modes of key structural components like the hoist frame, guide rails, and connectors, and evaluating them using structural health monitoring algorithms. The compensation model comprehensively processes the original braking distance overshoot, transmission matching degree, and structural stability margin coefficient to generate a compensation index. The core of this model lies in basing the current state of the system (determined by the transmission matching degree) on the current state of the system. and structural stability margin coefficient The original overshoot is corrected (characterized by a lookup table or polynomial fitting function based on empirical data or simulation results). and As input, output a correction factor, then combine with... Multiplication; or through an adaptive control algorithm that can adjust based on real-time monitoring data. and The compensation model's parameters are dynamically adjusted to better adapt to changes in system state. The compensation index $AC$ is the model's output, representing the initially corrected braking distance overshoot. This index comprehensively considers the original overshoot as well as the current state of the transmission and structural systems. It can be used as a value directly calculated by the compensation model for subsequent braking leveling accuracy adjustment; or as an intermediate variable, its magnitude directly reflects the degree to which the braking distance overshoot needs adjustment under current transmission and structural conditions. The braking leveling accuracy adjustment model is the core for ultimately obtaining the optimized braking distance overshoot. It further utilizes transmission matching degree, structural stability margin coefficient, and compensation index, calculating through an exponential function to achieve fine-grained adjustment of the braking distance overshoot. This can be implemented, for example, through an intelligent control algorithm based on fuzzy logic or neural networks, which can learn and adapt to braking characteristics under different operating conditions, thereby outputting the optimal overshoot; or through a preset nonlinear function, which directly calculates the optimized braking distance overshoot based on the real-time values ​​of the input parameters. It is the final braking and stopping distance overshoot obtained after comprehensive system evaluation and adjustment. This value is used to guide the actual action of the braking system to ensure that the hoist can achieve higher leveling accuracy when stopping. It can be used as the set value of the brake controller and directly input to the brake actuator to adjust the timing and magnitude of the braking force; or as a feedback signal to adjust the control parameters of the braking system so that it can control the stopping position more accurately.

[0064] The solution in this application first obtains the initial braking distance overshoot. This value reflects the basic performance of the braking system. To improve the leveling accuracy, the solution in this application introduces a transmission matching degree. and structural stability margin coefficient These two key indicators, transmission matching degree It characterizes the degree of response of the transmission system to off-center load, while the structural stability margin coefficient This reflects the integrity of the main load-bearing structure. In the compensation model, the original braking distance overshoot... With transmission matching degree and structural stability margin coefficient The correction factor is multiplied to generate the compensation index. , The power-law decay coefficient can be determined through expert experience assignment, analytic hierarchy process (AHP), or orthogonal experimental design. Specifically, the correction factor... The design cleverly utilizes and The characteristics of the transmission matching degree Low (i.e., poor transmission system performance) or structural stability margin coefficient At a lower level (i.e., poor structural integrity), The value will increase, causing the exponent term to... Increase, thus making The value changes, thus affecting the original overshoot. This produces a more significant correction effect, indicating that the compensation model will more actively adjust the overshoot when the system state is not ideal. Subsequently, the braking leveling accuracy adjustment model further utilizes the transmission matching degree. Structural stability margin coefficient and compensation index To calculate the final optimized braking stopping distance overshoot. The model uses an exponential function. In the form of, where the exponent term The transmission matching degree was emphasized again. and structural stability margin coefficient The combined effects, when When either value is low, This increases the exponential term, thereby affecting the compensation exponent. This nonlinear adjustment mechanism allows the system to make more significant adjustments, enabling it to finely and adaptively optimize the braking distance overshoot based on subtle changes in the hoist's transmission and structural state. Through the synergistic effect of the two-level models, the proposed solution integrates the dynamic characteristics (transmission matching degree) and static characteristics (structural stability margin coefficient) of the hoist during operation into the braking strategy. This overcomes the limitations of traditional braking systems that rely solely on basic parameters such as speed and position for braking control. This adjustment method, which comprehensively considers multi-dimensional state information, allows the braking distance overshoot to more accurately reflect the current working condition, effectively avoiding braking inaccuracies caused by transmission or structural problems.

[0065] The following is a specific example to illustrate this: Braking stopping distance overshoot. The distance between the bottom of the hoist car and the shaft floor can be measured in real time using a laser rangefinder sensor installed at the bottom of the hoist car. This distance is then combined with the target floor leveling position for calculation. Transmission matching degree. and structural stability margin coefficient The results are output in real time by the stability analysis module and the main load-bearing structure module within the system. Parameters can be set in the compensation model. This is an empirical value, for example, 0.5. When the system receives the original braking distance overshoot... It is 10 mm. Transmission matching degree. The structural stability margin coefficient is 0.8. When it is 0.9. Compensation Index It is 9.89 mm. Subsequently, the compensation index... This information is input into the braking leveling accuracy adjustment model. Continue using the above... and Value. Optimized braking stopping distance overshoot. The result is 10.09 mm. The final optimized braking stopping distance overshoot is... For example, 10.09 mm. This value will be sent to the hoist's brake controller. Based on this optimized value, the brake controller adjusts the timing or force of the brake action to ensure the hoist stops more precisely at the target level when it stops. Simultaneously, the system will continuously... Is it within the preset safety range? Inside. Among them It can be set according to the mechanical limits and safety specifications of the hoist. For example, twice the original overshoot rating.

[0066] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A hoist fault diagnosis system based on multi-sensor data fusion, characterized in that, include; The operational stability module constructs an operational smoothness model based on the platform's bilateral synchronization deviation, acceleration kurtosis during start-up and shutdown, steady-state velocity ripple rate, and root mean square of the cargo platform's pitch angular velocity to obtain the motion smoothness index. The electromechanical transmission module constructs an electromechanical transmission quality factor model based on the motor current modulation depth, the sprocket meshing order amplitude variation coefficient, and the total distortion rate of the instantaneous torque pulsation harmonics of the motor to obtain the transmission electrical harmonic index. The main load-bearing structure module uses the vertical-horizontal vibration energy ratio, the dynamic gap change of the guide shoe, the vibration transmissibility of the base, and the drift of the structural characteristic frequency to construct a structural integrity coefficient model to obtain the structural stability margin coefficient. The stability analysis module constructs a transmission matching degree model based on the instantaneous slip rate of the chain drive and the two-dimensional offset of the load centroid under the motion smoothness index and the transmission electrical harmonic index to obtain the transmission matching degree. The braking stopping distance adjustment module constructs a braking leveling accuracy adjustment model based on transmission matching degree, structural stability margin coefficient and braking stopping distance overshoot, and outputs the optimized braking stopping distance overshoot.

2. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The braking leveling accuracy adjustment model is expressed as follows: in Indicates transmission matching degree, Indicates the structural stability margin coefficient. Indicates the compensation index. This indicates the overshoot of the optimized braking stopping distance.

3. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 2, characterized in that: The optimized method for obtaining braking distance overshoot is as follows: The overshoot of braking stopping distance, transmission matching degree, and structural stability margin coefficient are imported into the compensation model to obtain the compensation index; The compensation model is expressed as follows: in This indicates the overshoot in braking stopping distance. Indicates the degree of transmission matching. Indicates the structural stability margin coefficient. Indicates the compensation index. Indicates the power decay coefficient; The transmission matching degree, structural stability margin coefficient, and compensation index are imported into the braking leveling accuracy adjustment model to obtain the optimized braking stopping distance overshoot.

4. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The motion smoothness index is obtained as follows: Synchronization deviation, start-stop impact, speed ripple, and pitch sway are imported into the running stability model to obtain the motion smoothness index. The operational stability model is expressed as follows: in, Indicates the degree of synchronization deviation. Indicates the start-stop impact. Indicates velocity ripple. Indicates pitch and roll. Indicates the synchronization deviation weight. Acceleration kurtosis weighting, Velocity ripple weighting, Pitch angular velocity weighting The term represents the motion smoothness index, and the... ,in A higher value indicates smoother motion.

5. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 4, characterized in that: The synchronization deviation The method for obtaining this information is as follows: The real-time height difference between the left and right sides of the platform. Import, ,in Indicates the height difference scale parameter; The start-stop impact The method of obtaining it is: to obtain the acceleration kurtosis of the start-stop process. Import ,in Indicates the kurtosis increment scale parameter; The velocity ripple The method for obtaining it is: the steady-state ripple rate of the velocity. Compare it with its reference value; The pitch and sway The method of obtaining this is to calculate the root mean square velocity of the loading platform's pitch angle. Import .

6. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The method for obtaining the transmission electrical harmonic index is as follows: The current modulation depth, sprocket meshing order amplitude variation coefficient, and total distortion rate of instantaneous torque pulsation harmonics of the motor are subjected to maximum-minimum normalization to obtain the current modulation degree, meshing variability, and torque distortion degree. The current modulation, meshing variability, and torque distortion are imported into the electromechanical transmission quality factor model to obtain the transmission electrical harmonic index.

7. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The electromechanical transmission quality factor model is expressed as follows: in, This indicates the power-mean order, and the... , Indicates the current modulation degree. Indicates the degree of meshing variability. Indicates torque distortion. This represents the current modulation weighting coefficient. This represents the weighting coefficient for meshing variability. This represents the torque distortion weighting coefficient. The term represents the harmonic index of the transmission electrical system, and the... ,in A larger value indicates that the motor current modulation, sprocket meshing variation, and torque pulsation harmonic distortion are all close to zero, the transmission chain has ideal electrical-mechanical coupling characteristics, and the conversion of electrical energy to mechanical energy is smooth and efficient.

8. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The structural stability margin coefficient is obtained as follows: The energy ratio degradation, dynamic clearance, vibration transmission degradation, and frequency drift are imported into the structural integrity coefficient model to obtain the structural stability margin coefficient. The structural integrity coefficient model is expressed as follows: in, Indicates the degree of energy degradation. Indicates dynamic gap degree, Indicates the degree of vibration transmission degradation. Indicates frequency drift. Represents geometric weights, which are positive and sum to 1. Represents the structural stability margin coefficient, where And the A larger value indicates that the vibration energy of the main load-bearing structure is higher than normal, the guide shoe gap is stable, the vibration transmission rate of the base is low, and the modal frequency has no drift. This indicates that the frame, guide rail, connectors, etc. are in good condition and the structural stiffness is sufficient.

9. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 1, characterized in that: The transmission matching degree is obtained as follows: Combine the off-center load index and obtain the slippage index based on the benchmark slippage model; The tolerance bandwidth index is obtained by combining the motion smoothness index and the tolerance bandwidth model. The slip rate index, slip index, and tolerance bandwidth index are imported into the transmission matching degree model to obtain the transmission matching degree. The transmission matching degree model is expressed as follows: in, This indicates the slippage rate index. Indicates the slippage index. Tolerance bandwidth index Indicates the transmission matching degree, where And the A larger value indicates that the transmission system's response to off-center load is closer to the ideal state.

10. The hoist fault diagnosis system based on multi-sensor data fusion according to claim 9, characterized in that: The baseline slippage model is expressed as follows: in Indicates the off-center load index. This indicates the electrical harmonic index of the transmission. Indicates the linearity coefficient of the base eccentric load-slippage. This represents the DEHI correction factor; The tolerance bandwidth model is expressed as follows: in, Indicates the smoothness index of motion. Indicates the baseline tolerance half bandwidth. This represents the modulation slope of the bandwidth by the MSI.

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