Elevator braking performance intelligent detection method and system

Abstract

The invention belongs to the technical field of escalator braking performance detection, and particularly discloses an elevator braking performance intelligent detection method and system.The method comprises the steps that a parameterized simulation model is built based on mechanical topology and dynamic characteristics of an escalator, and firstly, model initial calibration is completed through a no-load braking test; then, a gradient simulation load scheme is designed in combination with actual operation load distribution, advanced calibration is performed on the model through physical test data under multi-stage loads, and finally, accurate prediction of the full-load braking performance is realized by relying on a high-fidelity calibration model, so that actual measurement of full-load weights is not needed, the detection safety and efficiency are remarkably improved, and the detection efficiency is improved. And the problem of model mismatching caused by factors such as equipment aging and abrasion can be effectively solved through a data-driven iterative calibration mechanism, the long-term prediction precision is ensured, and reliable technical support is provided for normalized and intelligent evaluation of the braking performance.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automatic escalator braking performance detection, and specifically discloses an elevator braking performance intelligent detection method and system. BACKGROUND

[0002] As a high-frequency and high-traffic transportation device in public places, the performance of the braking system of an automatic escalator is directly related to public safety. In order to ensure safety, the national standard compulsorily requires periodic detection of the full-load braking performance of an automatic escalator to verify its stopping ability under the most unfavorable working condition.

[0003] Traditional full-load braking performance detection generally relies on stacking solid weights on the steps to simulate the full-load working condition. This method has a series of defects such as complicated operation, interruption of normal operation of the device, high safety risk of high-altitude heavy lifting, and low detection efficiency. This leads to the fact that such detection can only be carried out in a discrete and long-periodic manner, and cannot realize the normalization and trend monitoring of the braking performance.

[0004] In order to overcome the above defects, some detection methods without full-load weights are proposed in the prior art. For example, a Chinese invention patent with the authorization announcement number CN112320550B discloses a method for measuring the stopping distance of an automatic escalator. The method measures multiple parameters such as the no-load running speed, no-load running power, and average deceleration under no load and once light load, and substitutes these parameters into a fixed conversion formula based on the derivation of dynamics theory, thereby calculating the predicted stopping distance under the full-load working condition.

[0005] Although this method avoids the use of full-load weights and significantly improves the detection convenience and efficiency, it relies on a pre-derived fixed physical formula for full-load braking calculation. The parameters in the formula are considered as constant values, and the dynamic characteristic drift caused by factors such as component wear, lubrication deterioration, and brake pad aging during long-term service of the device is not considered. Therefore, as the running time of the device accumulates, the deviation between the model and the actual system may continue to expand, resulting in difficulty in ensuring the accuracy of the full-load braking performance prediction result and limiting the long-term applicability. SUMMARY

[0006] Therefore, an object of an embodiment of the present application is to provide an elevator braking performance intelligent detection method and system, which builds a parameterized simulation model of automatic escalator braking, calibrates it through multi-working-condition physical test data, and finally uses the calibrated high-fidelity model for full-load braking prediction, effectively solving the problems existing in the prior art.

[0007] The object of the application can be realized by the following technical solutions: the first aspect of the application proposes an elevator braking performance intelligent detection method, comprising the following steps: S1, a parameterized simulation model of an escalator braking system is constructed, and initial parameter calibration is performed on the simulation model based on one-time empty load physical braking test data.

[0008] S2, load distribution is extracted based on historical operation data of the escalator, and a gradient simulation load scheme covering from empty load to the upper limit of safe load is designed accordingly.

[0009] S3, according to the gradient simulation load scheme, each simulation load is sequentially applied on the escalator.

[0010] S4, after each simulation load is applied, a braking instruction is triggered, multi-element signal time sequence data in the whole braking process are synchronously collected, key feature point identification and braking process stage decoupling are performed, and braking performance characteristics are extracted in each braking stage.

[0011] S5, the braking performance characteristics extracted in each braking stage by multiple gradient load points are used to perform advanced calibration on the initial calibrated parameterized simulation model.

[0012] S6, the standard full load condition is input into the advanced calibrated parameterized simulation model for simulation, full load braking performance prediction values are obtained, and braking safety judgment is performed accordingly.

[0013] The second aspect of the application proposes an elevator braking performance intelligent detection system, comprising the following modules: a simulation calibration module: a parameterized simulation model of an escalator braking system is constructed, and initial parameter calibration is performed on the simulation model based on one-time empty load physical braking test data.

[0014] A test planning module: based on historical operation data of the escalator, load distribution is extracted, and a gradient simulation load scheme covering from empty load to the upper limit of safe load is designed accordingly.

[0015] A simulation implementation module: according to the gradient simulation load scheme, each simulation load is sequentially applied on the escalator, after each simulation load is applied, a braking instruction is triggered, multi-element signal time sequence data including motor three-phase current, brake action displacement and main drive shaft speed in the whole braking process are synchronously collected, key feature point identification and braking process stage decoupling are performed, and braking performance characteristics are extracted in each braking stage.

[0016] An advanced calibration module: the braking performance characteristics extracted in each braking stage by multiple gradient load points are used to perform advanced calibration on the initial calibrated parameterized simulation model.

[0017] Simulation analysis module: standard full load conditions are input into the parameterized simulation model after advanced calibration to simulate, and full load braking performance prediction values are obtained, and braking safety is judged accordingly.

[0018] In combination with all the above technical solutions, the positive effects of the present application are: 1. The present application firstly completes initial calibration of the model by using the no-load brake test after constructing the parameterized simulation model of the escalator, then designs a gradient simulation load scheme in combination with the actual running load distribution, and finally realizes accurate prediction of the full load braking performance by relying on the calibrated model, which not only significantly improves the detection safety and efficiency without full load weight measurement, but also effectively overcomes the model mismatching problem caused by equipment aging, wear and other factors through data-driven iterative calibration, ensuring long-term prediction accuracy.

[0019] 2. The data collected when the model is calibrated by using the physical test data under multi-level load in the present application is not limited to terminal state indicators such as total braking time and total distance, but also decouples the braking process by stages, extracts performance characteristics with clear physical meaning in each stage, significantly enhances the richness and discriminability of calibration information, and expands the data for model calibration from single terminal state result to multi-dimensional whole process feature set, which is beneficial to improve the model fidelity and prediction accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0020] The present application will be further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 The embodiment steps of the elevator braking performance intelligent detection method in the present application are shown in the figure.

[0022] Figure 2 The implementation flowchart of S1 in the present application is shown in the figure.

[0023] Figure 3 The module connection diagram of the elevator braking performance intelligent detection system in the present application is shown in the figure. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely by combining the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] Embodiment 1

[0026] Referring to Figure 1 As shown in the specification, the application proposes an intelligent detection method for elevator braking performance, comprising the following steps: S1, constructing a parameterized simulation model of the escalator braking system, and based on the initial parameter calibration of the simulation model on the basis of the first empty load physical braking test data.

[0027] Since the escalator is an electromechanical system, its braking performance is affected by many time-varying and nonlinear factors, and frequent full-load physical tests cannot be carried out in actual operation, so it is necessary to construct a parameterized simulation model that can be calibrated and evolved to realize accurate mapping and performance prediction of the braking behavior.

[0028] Referring to Figure 2 As shown in the specification, in the specific implementation of the application, the parameterized simulation model is constructed in the following manner: based on the mechanical topological structure and dynamic characteristics of the escalator, a parameterized simulation model of the escalator braking system is established. The model is essentially a digital twin of the escalator braking system.

[0029] Specifically, modeling is first based on the mechanical topological structure of the escalator, i.e. the connection mode and mutual relationship between the drive chain, step chain, handrail belt, brake, tensioning device and other components. This structure determines how force is transmitted in the system and how each component moves in unison. Only by truly restoring this connection relationship can the simulation accurately reflect the stress and motion state of each part of the actual equipment during braking, avoiding distortion of the simulation results due to excessive simplification of the structure.

[0030] At the same time, the braking process involves speed reduction, friction, inertia effect and other physical behaviors. Therefore, the model also needs to incorporate basic dynamics laws and describe these behaviors through adjustable basic physical parameters such as system equivalent moment of inertia, friction coefficient, etc. In this way, the model can not only simulate the entire process from issuing the braking instruction to complete stop, but also reproduce key signals such as speed change, motor current, brake displacement, etc.

[0031] Compared with methods that rely only on empirical formulas or pure data fitting, this mechanism-based modeling approach has clear physical meaning for its internal parameters, which can be calibrated and corrected through actual measurement data, thereby continuously improving the accuracy of the model and effectively avoiding prediction bias caused by equipment aging, wear or changes in working conditions.

[0032] Therefore, the mechanical topological structure of the escalator provides geometric and connection constraints in the construction of the parameterized simulation model, and the dynamic characteristics provide the evolution law of motion and force, which together form the basis of the parameterized simulation model.

[0033] Although the parameterized simulation model of the escalator braking system is constructed based on mechanical structure and dynamics principles, the parameters used in the model can only be estimated according to drawings or experience in the design stage, and it is difficult to accurately reflect the actual physical state of a specific escalator.

[0034] Therefore, after the model is constructed, the initial parameter calibration is performed on the model by using the data of a no-load physical braking test. This step is equivalent to adjusting a general template to a digital twin body that is specific to the escalator. The no-load test is selected because the escalator has no passenger load at this time, the system is simplest, the collected signals such as speed, current, displacement have less interference, and can truly reflect the dynamic behavior of the driving system, the brake and the transmission mechanism, and therefore are suitable as a benchmark for initial calibration.

[0035] Only when the simulation results of the model under the no-load condition are basically consistent with the measured data, can it be ensured that the basic parameters in the model are within a reasonable range, and a reliable starting point is provided for subsequent more detailed calibration under different simulated loads, avoiding the failure or deviation from the true situation due to the too large initial deviation in the optimization process.

[0036] Applied to the above explanation, the initial parameter calibration implementation process is as follows: a sensor network is arranged on the output shaft of the driving motor, the brake actuator and the transmission node of the escalator.

[0037] When the escalator is in a no-load state, a braking instruction is triggered, and the time sequence data reflecting the motor current, the brake action displacement and the main shaft speed during the braking process are synchronously collected by the sensor network.

[0038] In the preferred implementation of the above scheme, the sensor network includes a speed sensor, a displacement sensor and a current sensor.

[0039] The speed sensor is installed on the output shaft of the driving motor, and is used to record the speed change of the main driving shaft from the running speed to the complete stop process. Since the braking effect is most directly reflected in the deceleration process, this data can directly reflect the braking time, the deceleration change trend and the final stop time, and is mainly used to calibrate the equivalent rotational inertia and friction coefficient and other parameters in the model.

[0040] The displacement sensor is installed on the actuator of the brake, and is used to monitor the displacement change when the brake is actuated. Because the brake needs a certain time from receiving the instruction to actually pressing the brake disc, its action has a delay and a gradual pressure building process. Through this signal, it can be accurately judged when the brake starts to act, how fast the brake acts and whether the brake is in place, so as to calibrate the parameters related to the mechanical response characteristics of the brake in the model, such as action delay, stroke resistance, etc.

[0041] The current sensor is installed on the driving motor power supply circuit composed of the transmission node to collect the change of three-phase current of the motor. When the brake instruction is issued, the control system will cut off the power supply or apply the reverse / energy consumption brake current, causing a significant change in current. This signal can reflect the response speed and brake intervention mode of the electric control system, and is used to calibrate the related parameters such as the electrical response delay and motor braking characteristics in the model.

[0042] Through the cooperative work of the three types of sensors, information of the electric control, machinery and motion in the braking process can be comprehensively obtained, providing reliable and multi-dimensional data support for the initial calibration of the simulation model.

[0043] The measured curve is drawn by using the time sequence data collected in the no-load braking test as a calibration reference, and the braking instruction is input to the simulation model to drive the simulation model to run.

[0044] By adjusting the basic physical parameters in the model, the no-load braking process curve simulated by the model is made to reach a preset degree of coincidence with the measured no-load braking process curve, and the initial calibration of the model is completed. The basic physical parameters at least include system rotational inertia and friction coefficient.

[0045] The no-load braking process curve simulated by the model in the above-mentioned refers to a set of time-varying physical quantity curves calculated and output by the simulation model after receiving the braking instruction, mainly including the time curve of the main driving shaft speed, the time curve of the motor current and the time curve of the brake displacement, which is just opposite to the measured curve collected in the no-load braking test.

[0046] The specific calibration process is as follows: first, the time sequence curves output by the parameterized simulation model under the no-load braking condition are aligned with the measured curves collected synchronously in the no-load physical test point by point, and the fitting errors such as root mean square error of each curve are calculated.

[0047] Subsequently, the corresponding to-be-calibrated basic physical parameters are assigned for different curves, for example: the rotational inertia and friction coefficient are mainly related to the speed curve, the brake action delay and damping characteristics are related to the displacement curve, and the electrical response time constant is related to the current curve, and the corresponding parameters are iteratively adjusted based on the error amount, such as appropriately increasing the rotational inertia or decreasing the friction coefficient when the simulation deceleration is too fast.

[0048] The above simulation, comparison and adjustment process is repeated until the errors of all curves converge to a preset calibration tolerance range.

[0049] The calibration tolerance range error can refer to the deviation requirement allowed for performance indicators in the relevant safety standards of escalator braking performance.

[0050] Finally, the parameter values at this time are locked, and at this time the parameterized simulation model has been personalized as the digital twin of the escalator.

[0051] S2, extract the load distribution based on the historical operation data of the escalator, and design a gradient simulation load scheme covering from no load to the upper limit of safe load according to the load distribution.

[0052] After building the digital twin of the escalator through initial parameter calibration, since the initial calibration only makes the model accurate under no load working condition, the braking performance of the escalator is highly dependent on the load. To ensure that the model also has high prediction accuracy under full load working condition, the model must be further calibrated through multi-stage simulation load test to master the mapping relationship between load and performance.

[0053] On the basis of the above, the multi-stage simulation load is not subjectively or uniformly set, but is designed based on the load distribution characteristics actually observed in the historical operation data of the escalator, which can ensure that the selected simulation load can reflect the real use scenario, thereby improving the information effectiveness of the calibration data.

[0054] Optionally, the gradient simulation load scheme is specifically designed as follows: the load time series is extracted from the historical monitoring data of the operation of the escalator.

[0055] It should be understood that during the operation of the escalator, the passengers stand on the steps, and their own weight will exert additional load moment on the drive system. The larger the load, the greater the downward gravity component that the escalator drive system needs to overcome, thereby directly affecting the braking performance.

[0056] Since the distribution position, number and stay time of passengers on the escalator dynamically change over time, in order to accurately obtain the actual running load, weight sensing units can be arranged on the steps of the escalator to sense the local load borne by each step in real time.

[0057] By synchronously collecting the signals of all sensing units, and adding up the instantaneous load values on all steps at each moment, the equivalent total load acting on the entire machine at that moment can be obtained.

[0058] The equivalent total load is continuously recorded in chronological order, that is, the load time series reflecting the real use intensity of the escalator is formed.

[0059] The extracted load time series is used to construct a load probability distribution graph reflecting the real running working condition by using a histogram.

[0060] Understandably, the histogram can clearly show the frequency distribution of the load value by dividing the data into several intervals and counting the number of data points in each interval.

[0061] The load probability distribution graph is divided into several load intervals through cluster analysis.

[0062] According to the escalator safety usage specification, the upper limit value of the safety load of the escalator is determined.

[0063] It should be noted that the upper limit value of the safety load of the escalator refers to the maximum weight limit that the escalator can safely bear, and this value does not refer to the load of the escalator in the full load state.

[0064] Between the empty load and the upper limit value of the safety load, a number of discrete load points are selected according to the proportion of the area of each load interval in the overall distribution.

[0065] In the load probability distribution, the width of each interval on the horizontal axis represents the load range span, and the height reflects the frequency of occurrence of the unit load segment in the interval. The area formed by the product of the two represents the total frequency of load occurrence in the interval, i.e. its statistical weight in the historical operation data.

[0066] Given that the larger area interval occupies a higher proportion in actual operation and has a stronger representativeness in the daily performance of the escalator braking performance, more load points should be allocated in the gradient simulation load scheme; while the smaller area interval, although with a lower frequency of occurrence, may involve safety critical conditions, and appropriate sampling should be retained to ensure complete coverage.

[0067] In specific implementation, first, the total number of discrete load points to be selected is determined, then the proportion is allocated according to the area proportion of each cluster interval, and the points are uniformly distributed within the corresponding load width range.

[0068] In a specific selection example, assuming that the total number of sampling points is 6, three load intervals A, B, and C are obtained by clustering, and their area proportions are 50%, 30%, and 20% respectively: interval A is allocated 3 load points; interval B is allocated 2 load points; and interval C is allocated 1 load point.

[0069] Each load point determines the specific value within the load range of its own interval using uniform distribution.

[0070] The selected discrete load points are arranged in order from small to large to form a gradient simulation load test scheme that covers the entire working condition range.

[0071] The above gradient simulation load test scheme determined based on the actual running load distribution of the escalator can make the selected load points consider both the running representativeness and the working condition coverage.

[0072] S3, according to the gradient simulation load scheme, each simulation load is sequentially applied to the escalator.

[0073] In determining the gradient simulation load scheme, load application needs to be performed on the escalator to obtain measured braking data under different loads, so as to perform further calibration on the parameterized simulation model.

[0074] As an optional implementation of the above step, the test mode is automatically activated when the escalator is in a non-operation period.

[0075] Since the brake performance test needs to actively trigger the emergency brake, the escalator will suddenly decelerate or stop during the process. If it is performed during the operation period, it is extremely easy to cause passengers to lose balance, fall down, and even trigger a stampede accident, so the test needs to be performed during the non-operation period.

[0076] According to the gradient simulation load scheme designed, a load simulation device which is non-contact coupled with the driving system of the escalator is used to sequentially apply gradient loads to the driving system of the escalator.

[0077] The above load simulation device can accurately output a constant equivalent torque according to a preset gradient. Specifically, it can be a magnetic powder brake or an eddy current brake, which is installed on the non-load side extension shaft of the driving host of the escalator or a special test tool. The excitation current of the load simulation device is adjusted by a controller to accurately simulate different load torques, and any operating condition from no load to the upper limit of the safe load can be flexibly reproduced. Compared with the traditional way of stacking physical weights on the steps, this non-contact loading method does not need physical counterweights, and fundamentally avoids the safety risks of heavy object handling and high-altitude falling.

[0078] It should be noted that although the load simulation device has the ability to simulate full load conditions, the equivalent torque applied by the load simulation device will actually trigger the mechanical braking process of the escalator. If such physical braking tests are frequently performed under full load conditions, the aging of the brake pads and the transmission chain will inevitably be accelerated, which not only shortens the service life of the equipment, but also may introduce unnecessary maintenance costs.

[0079] Therefore, the present application selects several gradient load points within the safe load upper limit range for the load simulation device to perform a limited number of real braking tests. The multi-condition measured data obtained is used to further calibrate the parameterized simulation model. On this basis, the braking performance under the full load condition is no longer dependent on physical tests, but is simulated and predicted by the calibrated model.

[0080] This method avoids repeated high-stress impact on the core braking components of the escalator, effectively protecting the health status of the equipment. On the other hand, it provides a feasible path for subsequent long-term and high-frequency braking performance monitoring.

[0081] S4, triggering a braking instruction after each application of the simulation load, synchronously collecting multi-element signal time sequence data of the whole braking process, wherein the multi-element signal time sequence data includes motor three-phase current, brake action displacement, and main drive shaft speed signal, and performing key feature point identification and braking process stage decoupling, and extracting braking performance representation in each braking stage.

[0082] The braking process of the escalator is not a single transient behavior after the application of the simulation load and the triggering of the braking instruction, but sequentially experiences multiple dynamic stages, each stage having its own braking performance. In order to comprehensively characterize the braking performance, the braking process needs to be decoupled in stages, and the performance indicators with physical significance need to be extracted in each sub-stage.

[0083] To achieve accurate stage division, the motor three-phase current, the brake action displacement, and the main drive shaft speed signal generated during the braking process are needed to identify the key feature points marking the start and end of each stage.

[0084] As one of the ways that the above-mentioned scheme can achieve, the key feature point identification refers to the following process: taking the time when the braking instruction is issued as the starting time of the electrical response.

[0085] Understandably, the braking instruction is an external triggering event of the entire braking process, has a clear timestamp and certainty, and is defined as the starting time of the electrical response, providing a synchronous time origin for all subsequent feature points, ensuring the alignment of the time sequence of multiple signals.

[0086] The first-order differential processing is performed on the synchronously collected motor three-phase current signal to obtain the current rate of change signal.

[0087] The first local peak point in the current rate of change signal is captured, and the time corresponding to the peak value is defined as the completion time of the electrical response.

[0088] Understandably, after the issuance of the braking instruction, the frequency converter or the contactor acts, causing the power supply state of the motor to change abruptly, triggering the rapid change of the current. This change is not completed instantaneously, but goes through a short electrical control response process. By performing first-order differential processing on the current signal, the rate of change can be amplified, and the steep slope of the response start can be highlighted. The first local peak point in the current rate of change signal corresponds to the time when the current changes most sharply, which is the critical point when the electrical control system completes the switching and the braking energy starts to be injected.

[0089] The first-order differential of the brake action displacement signal is obtained to get the brake movement speed signal, and the first peak time in the movement speed signal is detected to determine the mechanical braking start time.

[0090] Understandably, after receiving the electrical signal, the brake needs to overcome the spring pre-tightening force, mechanical clearance, and friction resistance to generate effective braking force, which has a mechanical delay. By taking the first-order derivative of the brake displacement signal, the movement speed is obtained. When the brake starts to actually move, the speed will rapidly rise from zero, and the first speed peak corresponds to the time when the brake actuator breaks through the static friction and starts to effectively compress the brake disc, marking the formal start of pure mechanical friction braking.

[0091] The moment when the main drive shaft speed first drops to and remains at zero speed without rising again is defined as the complete stopping moment.

[0092] Understandably, the main shaft speed continues to decay to zero under the action of friction torque. However, due to inertial rebound, sensor noise or slight vibration, the speed signal may oscillate slightly around zero. When the speed remains at zero and does not rise again, it represents the complete dissipation of system kinetic energy, and the braking process is completely over.

[0093] In a further realizable manner, decoupling the braking process stages using the identified key feature points includes the following process: the period from the start of the electrical response to the completion of the electrical response is taken as the electrical response stage.

[0094] The period from the completion of the electrical response to the start of the mechanical braking is taken as the mechanical pressure building stage.

[0095] The period from the start of the mechanical braking to the complete stopping moment is taken as the pure friction braking stage.

[0096] In a further realizable manner, the braking performance is extracted in each braking stage as follows: the duration of the electrical response stage is taken as the electrical response delay indicator, reflecting the response speed of the control system and the drive unit. The longer the duration, the more delayed the brake starts.

[0097] The ratio of the displacement amount of the brake in the mechanical pressure building stage to the stage duration is taken as the average pressure building rate of the brake, which characterizes the mechanical execution efficiency of the brake. The lower the average pressure building rate, the slower the pressure building, resulting in a delay in the establishment of braking force and causing insufficient deceleration.

[0098] In the pure friction braking stage, the initial speed and the stage duration are extracted from the time series data of the main drive shaft speed signal in the stage to calculate the average deceleration.

[0099] Specifically, since the main drive shaft speed monotonically decays from the initial value to zero in this stage, the average deceleration can be obtained from the ratio of the initial speed to the stage duration. A decrease in average deceleration usually means that the friction coefficient has decreased due to factors such as wear, contamination or aging, which will result in a weakening of the actual stopping ability, and thus weaken the safety margin of the braking system.

[0100] The main drive shaft speed signal in this stage is time-integrated to obtain the main shaft rotation angle, which is then multiplied by the step pitch circle radius to convert to linear running distance, i.e. pure friction braking distance, which represents the distance the escalator runs during the actual action of the effective braking force. It excludes the invalid sliding of the electrical and mechanical delay sections and is a pure indicator for evaluating the performance of the friction system.

[0101] The total braking time is obtained by summing the durations of the electrical response phase, the mechanical pressure build-up phase, and the pure friction braking phase, which comprehensively reflects the time consumed from issuing the braking command to complete stop.

[0102] The total braking distance is calculated by integrating the spindle speed signal and multiplying it by the step pitch circle radius during the entire braking process. This is the most critical safety compliance indicator, directly corresponding to the mandatory requirement in national standards that the braking distance must be between the minimum and maximum values. If the braking distance is too short, it will cause excessive deceleration, leading to passenger imbalance; if the braking distance is too long, it will cause the risk of brake failure, resulting in serious safety accidents.

[0103] The extracted braking performance characteristics not only reveal the functional states of each braking stage and achieve a multi-dimensional characterization of the braking process, but also comprehensively reflect the temporal response capability of the overall braking system. These quantitative indicators, combining stage-by-stage and global approaches, provide highly identifiable multi-source constraint data for the advanced calibration of subsequent parametric simulation models.

[0104] S5. Using the braking performance characteristics extracted from multiple gradient load points at each braking stage, the initially calibrated parametric simulation model is further calibrated.

[0105] Given the large amount of braking performance data under multi-gradient loads, performing simulation calibration point by point sequentially would be inefficient. Furthermore, the parameter sensitivity of escalator braking systems varies under different loads. In low-load or high-load regions, the system response tends to be stable, and parameter changes have little impact on the results; however, in medium-load or transitional regions, braking performance is more sensitive to parameter changes, and the load-performance relationship fluctuates significantly. Therefore, prioritizing the identification and focusing on these highly sensitive load ranges as the initial calibration targets can reduce the number of simulations while achieving more efficient parameter correction and model optimization.

[0106] Based on the above considerations, the advanced calibration process is as follows: the braking performance characteristics extracted at each gradient load point are plotted with the load level as the abscissa and the corresponding braking performance as the ordinate, and the load performance variation curve is drawn.

[0107] Numerical differentiation is performed on each load performance curve. By detecting the abrupt change in the sign of the first derivative, the inflection point in the original curve is identified. Thus, the load interval between adjacent inflection points is defined as the fluctuating load interval.

[0108] By performing an intersection operation on the fluctuating load ranges corresponding to all braking performance characteristics, the load range that is commonly sensitive to braking performance is obtained and defined as the first-round optimization range.

[0109] Perform a union operation on the fluctuating load intervals corresponding to all braking performance characteristics, and subtract the result from the intersection operation to obtain the secondary sensitive load interval, which is used as the optimization object in the second round.

[0110] The remaining gradient load points not included in the union are then taken as the third round of supplementary optimization objects.

[0111] According to the above three-stage optimization sequence, the corresponding gradient load points are sequentially input into the parameterized simulation model that has completed initial calibration, the simulation outputs of various braking performance indicators are obtained, and the simulation outputs are compared with the measured values to calculate the error amount.

[0112] By way of example, the error amount can be a relative error. In particular, for each load point, the errors of multiple braking performances are calculated to form an error vector.

[0113] The basic physical parameters in the parameterized simulation model that has completed initial calibration are cooperatively optimized based on the error amount through parameter optimization.

[0114] In an embodiment of the above scheme, the cooperative optimization process is as follows: first, for multiple load points in a round of optimization, the error vectors under each load point are combined to form a comprehensive error objective function such as the root mean square error.

[0115] Then, starting from the basic physical parameters after initial calibration, a numerical optimization algorithm such as the least squares method is used to automatically search for the parameter combination that minimizes the comprehensive error objective function.

[0116] Specifically, if the simulated braking distance under a certain load is longer, it indicates that the friction is insufficient, and the friction coefficient should be increased.

[0117] If the pressure building time is too short, the mechanical response may be too fast, and the brake damper or gap parameters should be adjusted.

[0118] All parameters are adjusted simultaneously to ensure that the overall multi-index error is reduced, rather than overfitting a single index.

[0119] Through cooperative optimization, the model is forced to simultaneously approach the real behavior under all performance indicators and load conditions, thereby obtaining a physically consistent and reliably generalized parameter set.

[0120] When the errors between the simulation values and the measured values of various braking performance indicators under all gradient load points converge within a preset calibration tolerance range after a round of optimization, it is determined that the model calibration is completed, and the current parameter set is locked as the final calibrated model parameters.

[0121] The calibration tolerance range in the above scheme still refers to the allowable deviation requirement for braking performance in the relevant safety standards for escalator braking performance.

[0122] It should be noted that although the parameterized simulation model after initial calibration can well reproduce the no-load braking behavior, when different loads are introduced for simulation, there may still be a large deviation from the measured data, and therefore advanced calibration is required.

[0123] Since the calibration process is divided into multiple rounds of optimization according to load sensitivity, the first round focuses on the load interval most sensitive to parameters, and if the errors between the simulation values and the measured values of each braking performance index at all gradient load points have met the preset tolerance requirements after a round of optimization, there is no need to continue the optimization of subsequent rounds, so as to improve the calibration efficiency.

[0124] S6, input the standard full load condition into the parameterized simulation model after the advanced calibration to perform simulation, obtain the full load braking performance prediction value, and perform braking safety judgment accordingly.

[0125] The specific implementation is as follows: input the standard full load condition into the parameterized simulation model after the advanced calibration to obtain the predicted braking distance under the working condition.

[0126] The standard full load condition is the most unfavorable braking working condition composed of the rated load, the rated speed and the running direction which is usually downward according to the current national standard.

[0127] The predicted braking distance is compared with the full load braking distance limit value specified in the current escalator safety standard.

[0128] If the predicted braking distance exceeds the full load braking distance limit value, it is determined that the braking performance is not up to standard.

[0129] It should be noted that in the full load working condition, the current safety standard only takes the braking distance as the only mandatory compliance index for determining whether the braking performance is up to standard, and other performance parameters are used for process analysis but do not directly determine the pass / fail result, so in the present application, only the braking distance is used for braking safety judgment in the full load braking simulation.

[0130] Embodiment 2

[0131] Referring to Figure 3 The present application proposes an intelligent elevator braking performance detection system, which comprises the following modules: a simulation calibration module: a parameterized simulation model of the escalator braking system is constructed, and initial parameter calibration of the simulation model is performed based on the once empty load physical braking test data.

[0132] A test planning module: based on the historical running data of the escalator, the load distribution is extracted, and a gradient simulation load scheme covering from empty load to the upper limit of safe load is designed.

[0133] A simulation implementation module connected with the test planning module: according to the gradient simulation load scheme, each simulation load is sequentially applied on the escalator, a braking instruction is triggered after each simulation load is applied, multi-element signal time sequence data including motor three-phase current, brake action displacement and main drive shaft speed during the whole braking process are synchronously collected, key feature point recognition and braking process stage decoupling are performed, and braking performance representation is extracted in each braking stage.

[0134] The advanced calibration module is connected with the simulation implementation module and the simulation calibration module, and performs advanced calibration on the parameterized simulation model of initial calibration by using the braking performance characteristics extracted from the plurality of gradient load points in each braking stage.

[0135] The simulation analysis module is connected with the advanced calibration module, and inputs the standard full load condition into the parameterized simulation model after the advanced calibration to obtain a full load braking performance prediction value, and performs braking safety judgment based on the full load braking performance prediction value.

[0136] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part.

[0137] Those skilled in the art can realize that the algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0139] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0140] Finally, the above is only a preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligent detection of elevator braking performance, characterized in that, Includes the following steps: S1. Construct a parameterized simulation model of the escalator braking system, and calibrate the initial parameters of the simulation model based on a single no-load physical braking test. S2. Extract load distribution based on historical operating data of escalators, and design a gradient simulation load scheme covering the load from no load to the upper limit of safe load. S3. According to the gradient simulation load scheme, apply each simulated load sequentially on the escalator; S4. Trigger a braking command after each simulated load is applied, synchronously collect multi-signal time data of the entire braking process, identify key feature points and decouple the braking process stages, and extract braking performance characteristics in each braking stage. S5. Using the braking performance characteristics extracted from multiple gradient load points at each braking stage, perform advanced calibration on the initially calibrated parametric simulation model. S6. Input the standard full-load conditions into the parameterized simulation model after advanced calibration to perform simulation, obtain the predicted value of full-load braking performance, and make a braking safety judgment based on it.

2. The intelligent detection method for elevator braking performance as described in claim 1, characterized in that: S1 includes the following: Based on the mechanical topology and dynamic characteristics of escalators, a parametric simulation model of the escalator braking system is established. A sensor network is deployed on the output shaft of the drive motor, the brake actuator, and the transmission nodes of the escalator. When the escalator is in an unloaded state, a braking command is triggered, and the timing data of motor current, brake displacement and spindle speed reflecting the braking process are collected synchronously using a sensor network. The measured curves were plotted using the time-series data collected during the no-load braking test as a calibration reference, and braking commands were input into the simulation model to drive the simulation model to run. By adjusting the basic physical parameters in the model, the errors between the simulation output curve of the no-load braking process and the measured curve of the no-load braking process are brought to within the preset calibration tolerance range, thus completing the initial calibration of the model. The basic physical parameters include at least the system's equivalent moment of inertia and friction coefficient.

3. The intelligent detection method for elevator braking performance as described in claim 1, characterized in that: S2 is implemented as follows: Extract load time series from historical monitoring data of escalator operation; Histograms were used to construct a load probability distribution map reflecting the actual operating conditions from the extracted load time series. The load probability distribution map is divided into several load intervals through cluster analysis; According to the safety regulations for the use of escalators, the upper limit of the safe load for escalators shall be determined. Between the no-load and the upper limit of the safe load, a number of discrete load points are selected proportionally based on the area of ​​each load interval relative to the overall distribution. Arrange the selected discrete load points in ascending order to form a gradient simulation load scheme that covers the entire operating condition range.

4. The intelligent detection method for elevator braking performance as described in claim 3, characterized in that: S3 includes the following: The test mode is automatically activated when the escalator is not in operation. According to the designed gradient simulation load scheme, gradient loads are applied to the escalator drive system in sequence through a load simulation device that is non-contactly coupled with the escalator drive system.

5. The intelligent detection method for elevator braking performance as described in claim 1, characterized in that: The identification of key feature points follows the process described below: The multi-signal timing data collected during braking include the three-phase current of the motor, the displacement of the brake action, and the speed signal of the main drive shaft; The moment the braking command is issued is taken as the start moment of the electrical response; The three-phase current signals of the motor acquired synchronously are processed by first-order differentiation to obtain the current change rate signal; The first local peak point is captured in the current rate of change signal, and the time corresponding to the peak point is defined as the electrical response completion time. The first derivative of the brake action displacement signal is used to obtain the brake motion speed signal. The mechanical braking start time is determined by detecting the first peak time in the motion speed signal. The main drive shaft speed signal is continuously monitored for its numerical decay process. The moment when the speed first drops to zero and remains at zero without rising again is defined as the moment of complete stop.

6. The intelligent detection method for elevator braking performance as described in claim 5, characterized in that: The decoupling of the braking process phase includes the following processes: The time period from the start time of the electrical response to the completion time of the electrical response is defined as the electrical response phase. The period from the completion of the electrical response to the start of mechanical braking is defined as the mechanical pressure build-up phase. The period from the start of mechanical braking to the moment of complete stopping is defined as the pure friction braking phase.

7. The intelligent detection method for elevator braking performance as described in claim 6, characterized in that: The braking performance characterization extracted during each braking stage includes the following: The duration of the electrical response phase is used as an indicator of electrical response delay. The ratio of the brake's displacement during the mechanical pressure build-up phase to the phase duration is taken as the average pressure build-up rate of the brake. During the pure friction braking phase, the initial velocity and phase duration are extracted from the timing data of the main drive shaft speed signal during this phase to calculate the average deceleration. The main drive shaft speed signal is integrated over time during this stage to obtain the main shaft rotation angle, which is then multiplied by the step pitch circle radius to convert it into a linear running distance, i.e., pure friction braking distance. The total braking time is obtained by summing the durations of the electrical response phase, the mechanical pressure build-up phase, and the pure friction braking phase. The total braking distance is obtained by integrating the spindle speed signal and converting it throughout the braking process.

8. The intelligent detection method for elevator braking performance as described in claim 1, characterized in that: S5 includes the following: The braking performance extracted at each gradient load point is characterized by plotting the load level as the abscissa and the corresponding braking performance as the ordinate, and the load performance variation curve is plotted. Numerical differentiation is performed on each load performance curve. By detecting the abrupt change in the sign of the first derivative, the inflection point in the original curve is identified. Thus, the load interval between adjacent inflection points is defined as the fluctuating load interval. The intersection of all the fluctuating load ranges corresponding to the braking performance characteristics is calculated to obtain the load range that the braking performance is commonly sensitive to, which is defined as the first-round optimization range. Perform a union operation on all the fluctuating load intervals corresponding to the braking performance characteristics, and subtract the result from the intersection operation to obtain the secondary sensitive load interval, which is used as the object of the second round of optimization. The remaining gradient load points not included in the union are then used as the objects for the third round of supplementary optimization. Based on the above three-stage optimization sequence, the corresponding gradient load points are sequentially input into the parameterized simulation model that has completed initial calibration, the simulation outputs of various braking performance characteristics are obtained, and compared with the measured values ​​to calculate the error. Based on the error, the basic physical parameters in the initially calibrated parameterized simulation model are optimized collaboratively through parameter optimization. When the error between the simulated and measured values ​​of various braking performance characteristics under all gradient load points converges to the preset calibration tolerance range after a certain round of optimization, the advanced calibration of the model is determined to be completed, and the current parameter set is locked as the final calibrated model parameters.

9. The intelligent detection method for elevator braking performance as described in claim 1, characterized in that: S6 includes the following: By inputting the standard full-load conditions into the parameterized simulation model after advanced calibration, the predicted braking distance under this working condition can be obtained. The predicted braking distance will be compared with the full-load braking distance limit specified in the current escalator safety standards. If the predicted braking distance exceeds the limit for braking distance under full load, the braking performance is deemed substandard.

10. An intelligent detection system for elevator braking performance, characterized in that, include: Simulation calibration module: Constructs a parameterized simulation model of the escalator braking system and performs initial parameter calibration of the simulation model based on a single no-load physical braking test. Test planning module: Extract load distribution based on historical operation data of escalators, and design a gradient simulation load scheme covering from no load to the upper limit of safe load. Simulation Implementation Module: Based on the gradient simulation load scheme, simulated loads are applied sequentially on the escalator. After each simulated load is applied, a braking command is triggered. Simultaneously, multi-dimensional signal time-series data including motor three-phase current, brake action displacement, and main drive shaft speed are collected throughout the braking process. Key feature points are identified and braking process stages are decoupled. Braking performance characteristics are extracted in each braking stage. Advanced calibration module: Utilizes braking performance characteristics extracted from multiple gradient load points at each braking stage to perform advanced calibration on the initially calibrated parametric simulation model; Simulation analysis module: Input the standard full-load conditions into the parameterized simulation model after advanced calibration to perform simulation, obtain the predicted value of full-load braking performance, and make a braking safety judgment based on it.

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