Elevator multi-caliper disc brake diagnostic method, system, device, medium, and product

Abstract

The application provides an elevator multi-caliper disc brake diagnosis method, system, device, medium and product, and relates to the fault diagnosis field of the elevator multi-caliper disc brake. The diagnosis method comprises the following steps: acquiring a state transition matrix between each preset state in a Markov jump system state transition model; acquiring first operation data of the multi-caliper disc brake and preprocessing; acquiring a current brake system state estimation value and a corresponding operation data range according to the preprocessed first operation data; acquiring second operation data of the multi-caliper disc brake in real time and preprocessing; when the preprocessed second operation data exceeds the operation data range, determining a fault type of the multi-caliper disc brake according to the current brake system state estimation value, the Markov jump system state transition model and the state transition matrix. The application can realize fault diagnosis for the elevator multi-caliper disc brake and provide support for daily maintenance and repair of the elevator brake system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fault diagnosis of elevator multi-caliper disc brakes, in particular to an elevator multi-caliper disc brake diagnosis method, system, device, medium and product. BACKGROUND

[0002] The elevator relies on the elevator brake system during the process of running to stopping or emergency stopping, and therefore the elevator brake system is a core component for ensuring the safe operation of the elevator. The brake system is configured with many safety protection devices to ensure the safe operation of the elevator, and the failure or malfunction of any protection device will pose a serious threat to the safety of passengers. As the actuator of the braking process of most high-speed elevators, the multi-caliper disc brake is an important component for ensuring the safety of the elevator, and the braking performance thereof is directly related to the reliable stopping of the traction machine and the safe stopping of the car. The multi-caliper disc brake of the elevator is usually composed of multiple brake calipers and a brake disc, and can apply a braking torque at multiple contact points to form a large braking torque in a short time, thereby improving the emergency braking effect of the elevator. Compared with ordinary low-speed elevators, high-speed elevators are used in high-speed variable load conditions for a long time, and have problems such as high speed, long braking distance during emergency braking, high temperature of friction elements, and serious wear. The risk of failure of key components of the brake increases with the use time, which is much higher than that of ordinary elevators. Therefore, on the basis of studying the performance characteristics of the multi-caliper disc brake of the high-speed elevator, it is necessary to analyze the influence of the failure of a certain brake caliper of the multi-caliper disc brake on the comprehensive braking ability under high-speed and variable load conditions, so as to effectively detect and warn the elevator brake with faults.

[0003] A large number of researchers have proposed many methods for elevator brake fault detection, such as an elevator brake intelligent monitoring system with a host computer and an embedded controller, which monitors the working data of the elevator brake in real time to determine the working state of the brake, and realizes real-time monitoring and real-time feedback of brake faults; an abnormal feature detection strategy based on a support vector machine is designed to realize elevator brake fault detection based on a deep clustering method. However, the above methods are all aimed at common drum brakes, ordinary disc brakes, and belt brakes, and lack research on multi-caliper disc brakes, and are difficult to be used for the fault diagnosis process of multi-caliper disc brakes. SUMMARY

[0004] The present application provides an elevator multi-caliper disc brake diagnosis method, system, device, storage medium and computer program product, which is based on the evaluation results of the comprehensive braking ability in the actual braking process of the elevator, and analyzes whether there is a fault of one or more brake calipers that leads to an unsatisfactory braking effect of the elevator according to the reliability index of the multi-caliper disc brake and the single-caliper fault-braking performance relationship model, thereby providing support for the daily maintenance and repair of the elevator brake system with a multi-caliper disc brake.

[0005] According to a first aspect of the present application, the embodiments of the present application provide a diagnostic method for an elevator multi-caliper disc brake, a brake system of an elevator comprising a multi-caliper disc brake, the multi-caliper disc brake comprising a plurality of brake calipers, the diagnostic method comprising: obtaining a state transition matrix between each preset state in a Markov jump system state transition model; obtaining first operation data of the multi-caliper disc brake and preprocessing; obtaining a current brake system state estimate value according to the preprocessed first operation data, and obtaining an operation data range corresponding to the current brake system state estimate value; collecting second operation data of the multi-caliper disc brake in real time and preprocessing; when the preprocessed second operation data exceeds the operation data range, determining a fault type of the multi-caliper disc brake according to the current brake system state estimate value, the Markov jump system state transition model and the state transition matrix.

[0006] The above-mentioned embodiments of the present application are based on the Markov jump system state transition model, and obtain a brake system state estimate value through operation data of the multi-caliper disc brake, and accurately detect a fault type of the multi-caliper disc brake when there is a difference between the operation data of the multi-caliper disc brake and the state estimate value, thereby providing support for daily maintenance and repair of the brake system of the elevator using the multi-caliper disc brake.

[0007] In some embodiments of the present application, the diagnostic method further comprises: establishing a multi-caliper disc brake physical model based on a physical structure of the multi-caliper disc brake and a geometric relationship between the calipers of the multi-caliper disc brake; and obtaining a multi-caliper disc brake comprehensive reliability index according to the multi-caliper disc brake physical model.

[0008] In some embodiments of the present application, the diagnostic method further comprises: establishing the Markov jump system state transition model describing a feature mapping between a single brake caliper fault and a brake performance of the multi-caliper disc brake based on the multi-caliper disc brake comprehensive reliability index, a brake caliper fault and a high-speed elevator comprehensive brake capacity evaluation result.

[0009] In some embodiments of the present application, the diagnostic method further comprises: updating the state transition matrix according to the first operation data and the second operation data of the multi-caliper disc brake, and maintenance information of the elevator.

[0010] According to the above-mentioned embodiments of the present application, the robustness of the diagnostic method can be improved by continuously optimizing the state transition matrix.

[0011] In some embodiments of the present application, the first operation data and the second operation data comprise: a temperature of the multi-caliper disc brake, a vibration signal of the multi-caliper disc brake, and a pressure signal of the multi-caliper disc brake.

[0012] In some embodiments of the present application, an observer technique is adopted to obtain a current braking system state estimation value according to the preprocessed first operation data and a dynamic equation, wherein the dynamic equation is a linear dynamic equation used to describe dynamic characteristics of the braking system.

[0013] In some embodiments of the present application, the linear dynamic equation is:

[0014]

[0015]

[0016] wherein, is a state vector, the state vector comprising state information of the braking system; is an input vector; is an output vector, the output vector comprising readings of various types of acquisition sensors; 、 、 is a Markov jump system dynamic characteristic matrix, the Markov jump system dynamic characteristic matrix being related to a preset state in a Markov jump system state transition model; is process noise; is measurement noise.

[0017] In some embodiments of the present application, the state transition probability in the state transition matrix is updated through the input vector.

[0018] In some embodiments of the present application, the current braking system state estimation value is calculated by using a Kalman filter or an extended Kalman filter technique.

[0019] In some embodiments of the present application, the current braking system state estimation value is calculated by using a Kalman filter technique, which comprises: using a Kalman filter technique to calculate a brake drag gap and a braking time according to the preprocessed first operation data; generating fusion data according to the brake drag gap and the braking time and a temperature of the multi-caliper disc brake; and calculating the current braking system state estimation value according to the fusion data and a Markov jump system state transition model.

[0020] In some embodiments of the present application, determining the fault type of the multi-caliper disc brake according to the current braking system state estimation value, the Markov jump system state transition model and the state transition matrix comprises: comparing the current braking system state estimation value with each preset state in the Markov jump system state transition model, and calculating a probability value of each Markov state in the Markov jump system state transition model in combination with the state transition matrix, and determining the fault type of the multi-caliper disc brake according to the probability value.

[0021] In some embodiments of the present application, the diagnostic method further comprises: after determining the fault type, locating the faulty brake caliper according to the following steps: extracting the fault features of the vibration signal of each brake caliper in the pre-processed first operating data and pre-processed second operating data, the fault features including time domain fault features and frequency domain fault features; inputting the time domain fault features and frequency domain fault features of the vibration signal into a fault pattern recognition model, the fault pattern recognition model outputting a fault pattern of the brake caliper; and locating one or more brake calipers with faults according to the abnormality degree values of the fault features of the vibration signal of each brake caliper.

[0022] In some embodiments of the present application, the time domain fault features of the vibration signal include: peak value, mean value, variance, kurtosis of the vibration signal; and the frequency domain fault features of the vibration signal include: main frequency, frequency spectrum energy distribution.

[0023] In some embodiments of the present application, the diagnostic method further comprises: after locating the faulty brake caliper, generating an operation suggestion according to the one or more brake calipers with faults.

[0024] In some embodiments of the present application, the diagnostic method further comprises: displaying the state of the brake system, the faulty brake caliper and the operation suggestion through a graphical interface.

[0025] According to the above-mentioned embodiments of the present application, by visually displaying the real-time state of the brake system, the fault details and the operation suggestion, it is convenient for maintenance personnel to overhaul and maintain the elevator brake system.

[0026] According to the second aspect of the present application, the embodiments of the present application provide a diagnostic system for a multi-caliper disc brake of an elevator, the diagnostic system being used to implement the above-mentioned diagnostic method for the multi-caliper disc brake of the elevator, the diagnostic system comprising: a model parameter acquisition module configured to acquire a state transition matrix between each preset state in a Markov jump system state transition model; a data acquisition module configured to acquire first operating data of the multi-caliper disc brake, and acquire second operating data of the multi-caliper disc brake in real time, and pre-process the first operating data and the second operating data; a state estimation module configured to acquire a current brake system state estimation value according to the pre-processed first operating data, and acquire an operating data range corresponding to the current brake system state estimation value; and a fault diagnosis module configured to, when the pre-processed second operating data exceeds the operating data range, determine a fault type of the multi-caliper disc brake according to the current brake system state estimation value, the Markov jump system state transition model and the state transition matrix.

[0027] The above-mentioned embodiments of the present application are based on a Markov jump system state transition model, and obtain a braking system state estimation value through operation data of a multi-caliper brake, and accurately detect a fault type of the multi-caliper brake when there is a difference between the operation data of the multi-caliper brake and the state estimation value, thereby providing support for daily maintenance and repair of an elevator braking system using the multi-caliper brake.

[0028] In some embodiments of the present application, the model parameter acquisition module establishes a multi-caliper brake physical model based on a physical structure of the multi-caliper brake and a geometric relationship between the calipers of the multi-caliper brake, and acquires a multi-caliper brake comprehensive reliability index according to the multi-caliper brake physical model.

[0029] In some embodiments of the present application, the model parameter acquisition module establishes the Markov jump system state transition model describing a characteristic mapping between a single brake caliper fault and braking performance of the multi-caliper brake based on the multi-caliper brake comprehensive reliability index, brake caliper fault, and high-speed elevator comprehensive braking capacity evaluation result.

[0030] In some embodiments of the present application, the model parameter acquisition module is further configured to update the state transition matrix according to first operation data and second operation data of the multi-caliper brake, and maintenance information of the elevator.

[0031] According to the above-mentioned embodiments of the present application, the robustness of the diagnosis system can be improved by continuously optimizing the state transition matrix.

[0032] In some embodiments of the present application, the first operation data and the second operation data include temperature of the multi-caliper brake, vibration signals of the multi-caliper brake, and pressure signals of the multi-caliper brake.

[0033] In some embodiments of the present application, the state estimation module uses an observer technology to obtain a current braking system state estimation value according to the preprocessed first operation data and a dynamic equation, wherein the dynamic equation is a linear dynamic equation used to describe dynamic characteristics of the braking system.

[0034] In some embodiments of the present application, the linear dynamic equation is as follows:

[0035]

[0036]

[0037] wherein, is a state vector, and the state vector includes state information of the braking system; is an input vector; is an output vector, the output vector comprising readings of various types of acquisition sensors; 、 、 is a Markov jump system dynamic characteristic matrix, the Markov jump system dynamic characteristic matrix being related to a preset state in a Markov jump system state transition model; is process noise; is measurement noise.

[0038] In some embodiments of the present application, the model parameter acquisition module updates the state transition probability in the state transition matrix through the input vector.

[0039] In some embodiments of the present application, the state estimation module calculates the current brake system state estimation value by using Kalman filter or extended Kalman filter technology.

[0040] In some embodiments of the present application, the state estimation module calculates the current brake system state estimation value by using Kalman filter technology, including: calculating the brake drag gap and brake time according to the preprocessed first operation data by using Kalman filter technology; generating fusion data according to the brake drag gap and brake time and the temperature of the multi-caliper disc brake; and calculating the current brake system state estimation value according to the fusion data and the Markov jump system state transition model.

[0041] In some embodiments of the present application, determining the fault type of the multi-caliper disc brake according to the current brake system state estimation value, the Markov jump system state transition model and the state transition matrix includes: comparing the current brake system state estimation value with each preset state in the Markov jump system state transition model, and calculating the probability value of each Markov state in the Markov jump system state transition model in combination with the state transition matrix, and determining the fault type of the multi-caliper disc brake according to the probability value.

[0042] In some embodiments of the present application, the diagnostic system further comprises a fault location module, configured to locate the fault brake according to the following steps after determining the fault type: extracting the fault features of the vibration signal of each brake caliper in the preprocessed first operation data and the preprocessed second operation data, the fault features including time domain fault features and frequency domain fault features; inputting the time domain fault features and the frequency domain fault features of the vibration signal into a fault pattern recognition model, the fault pattern recognition model outputting the fault pattern of the brake caliper; and locating one or more brake calipers with faults according to the abnormality degree value of the fault features of the vibration signal of each brake caliper.

[0043] In some embodiments of the present application, the time-domain fault features of the vibration signal include: peak value, mean value, variance, kurtosis of the vibration signal; and the frequency-domain fault features of the vibration signal include: main frequency, frequency spectrum energy distribution.

[0044] In some embodiments of the present application, the diagnostic system further comprises a suggestion generation module configured to generate an operation suggestion according to the one or more faulty brake calipers.

[0045] In some embodiments of the present application, the diagnostic system further comprises a display module configured to display the state of the brake system, the faulty brake caliper, and the operation suggestion through a graphical interface.

[0046] According to the above embodiments of the present application, the real-time state of the brake system, the fault details, and the operation suggestion are displayed visually, which facilitates the maintenance and repair of the elevator brake system by the maintenance personnel.

[0047] According to a third aspect of the present application, an embodiment of the present application provides a computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, cause a computer to perform the steps of the elevator multi-caliper disc brake diagnostic method based on a Markov jump system model according to any one of the above embodiments.

[0048] According to a fourth aspect of the present application, an embodiment of the present application provides a computer device comprising a memory and a processor, wherein the memory is configured to store one or more computer-readable instructions, and wherein the one or more computer-readable instructions, when executed by the processor, can implement the elevator multi-caliper disc brake diagnostic method based on a Markov jump system model according to any one of the above embodiments.

[0049] According to a fifth aspect of the present application, an embodiment of the present application provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the elevator multi-caliper disc brake diagnostic method based on a Markov jump system model according to any one of the above embodiments.

[0050] As described above, the elevator multi-caliper disc brake diagnostic method, system, device, medium, and computer program product provided by the embodiments of the present application are based on the comprehensive braking capability evaluation results in the actual braking process of the elevator, and analyze whether there is one or more faulty brake calipers that cause the elevator braking effect to be unsatisfactory according to the multi-caliper disc brake reliability index and the single-caliper fault-braking performance relationship model, thereby providing support for the daily maintenance and repair of the elevator brake system using the multi-caliper disc brake. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1is a flowchart of an elevator multi-dog brake diagnosis method based on a Markov jump system model according to Embodiment 1 of the present application;

[0052] Figure 2 is a flowchart of a method for implementing elevator multi-dog brake diagnosis and positioning based on a Markov jump system model according to Embodiment 3 of the present application;

[0053] Figure 3 is a flowchart of a method for implementing elevator multi-dog brake diagnosis and positioning based on a Markov jump system model according to Embodiment 4 of the present application;

[0054] Figure 4 is a schematic diagram of an architecture of an elevator multi-dog brake diagnosis system based on a Markov jump system model according to Embodiment 5 of the present application;

[0055] Figure 5 is a schematic diagram of a layer framework of an elevator multi-dog brake fault diagnosis system based on a Markov jump system model according to Embodiment 6 of the present application;

[0056] Figure 6 is Figure 5 is a schematic diagram of processing operations performed by each layer architecture component in the fault diagnosis system shown in

[0057] Figure 7 is a schematic diagram of a disc brake measurement structure according to Embodiment 7 of the present application;

[0058] Figure 8 is Figure 7 is a schematic diagram of a structure of a touch terminal in

[0059] The reference signs are explained as follows: 10-vibration sensor, 20-touch terminal, 30-temperature sensor, 40-coil voltage detection module, 1-housing, 2-acquisition module circuit board, 3-lithium battery, 4-rubber sleeve, 5-touch screen fixing plate, 6-touch screen, 7-upper housing, 8-panel. DETAILED DESCRIPTION

[0060] Various aspects of the present application are described in detail below in conjunction with the attached drawings and specific embodiments. Well-known modules, units, and their interconnections, links, communications, or operations are not shown or described in detail. Moreover, the described features, architectures, or functions can be combined in any manner in one or more embodiments. Those skilled in the art will understand that the various embodiments described below are only for illustration and not for limiting the scope of protection of the present application. It can also be readily understood that the modules or units or processing methods in the embodiments described herein and shown in the drawings can be combined and designed in various different configurations.

[0061] The following briefly describes the terms used in the text below.

[0062] MJS: Markov Jump System, a Markov jump system.

[0063] FMEA: Failure Mode and Effects Analysis, a systematic risk assessment tool aimed at identifying potential failure modes in a system, design, process or service, assessing their impact on system performance, and determining improvement priorities through structured analysis.

[0064] EKF: Extended Kalman Filter, an extended version of the Kalman Filter (KF) for nonlinear systems.

[0065] ABS: Acrylonitrile Butadiene Styrene, a thermoplastic polymer material, also known as acrylonitrile-butadiene-styrene copolymer.

[0066] PBT: Polybutylene Terephthalate, a thermoplastic engineering plastic, also known as polybutylene terephthalate.

[0067] Effective contact area of caliper disc: the sum of the area of the friction material surface of the caliper and the friction surface of the brake disc that actually contacts and transmits friction during braking.

[0068]

Example 1

[0069] Figure 1 is a flowchart of the elevator multi-caliper disc brake diagnosis method based on the Markov jump system model according to Example 1 of the present application. The brake system of the elevator includes the multi-caliper disc brake, and the multi-caliper disc brake includes multiple brake calipers.

[0070] As Figure 1 shown, in Example 1 of the present application, the elevator multi-caliper disc brake diagnosis method based on the Markov jump system model can include at least the following steps S11, S12, S13, S14 and S15, which are described in detail below.

[0071] In step S11, the state transition matrix between each preset state in the Markov jump system state transition model is obtained.

[0072] In some embodiments, the Markov jump system state transition model describing the characteristic mapping between single brake caliper failure and braking performance of the multi-caliper disc brake is established based on the multi-caliper disc brake comprehensive reliability index, brake caliper failure and high-speed elevator comprehensive braking capacity evaluation results. Wherein, the multi-caliper disc brake physical model is established based on the physical structure of the multi-caliper disc brake and the geometric relationship between the calipers of the multi-caliper disc brake; and the multi-caliper disc brake comprehensive reliability index is obtained according to the multi-caliper disc brake physical model.

[0073] In further embodiments, the jump probability matrix and other model parameters are constantly updated with newly collected data, and the effectiveness of the model is periodically verified and necessary corrections are made.

[0074] Wherein, the reliability index includes usage reliability and inherent reliability, and the inherent reliability includes but is not limited to one or more of the following: brake spring pressure reliability, braking torque reliability, friction plate wear reliability, brake action time reliability, brake disc gap reliability, hinge point structure performance reliability. Specifically, the usage reliability is embodied in the periodic maintenance of the special equipment on-site inspection and maintenance company, and the inherent reliability is determined by the relevant factors in the design and production of the brake model to be diagnosed. The usage reliability and the inherent reliability are consistent to a certain extent, and their mathematical relationship is represented as: Wherein, is the overall reliability of the brake; is the inherent reliability; is the usage reliability.

[0075] For example, for a single-caliper disc brake, its The index is determined by the input index of each component in the design stage, including but not limited to the upgate force, the upgate and release gap and time, the braking torque, etc., among which the braking torque is the most important index. The upgate force and the friction coefficient value are the key factors affecting the braking torque and also the key factors determining the reliability of the disc brake. Using high-quality spring materials and taking various measures to ensure the stability of the brake friction factor can output a sustained and stable braking torque, which is the guarantee for the accurate and safe braking of the elevator at each floor. It can be concluded from a large number of failure sites that another main index affecting the safety and reliability of the brake is the hinge point failure rate, because the severity of its occurrence directly determines the brake opening and closing action and the suspended holding braking torque. Therefore, the mathematical relationship of the inherent reliability of the disc brake is represented as: Wherein, is the reliability of the spring upgate and release; is the braking torque reliability of the friction disc; is the reliability of the hinge point structure.

[0076] Wherein, , The calculation method is as follows: (1) Calculation: The normally closed brake is generally used in the elevator, and the butterfly spring group is used in the space field to shorten the space. For the ordinary hydraulic disc brake, the service life approximately presents an exponential curve distribution, and the failure rate is obtained through a large amount of statistical data, and then the reliability of the brake spring is calculated ; (2) Calculation: The friction reliability, i.e. the stable reliability of the braking torque, can be determined through the single jaw disc brake test experiment. In the experiment process, the friction coefficient and the torque distribution on the brake disc are determined, and the mean value and the standard deviation data of the final braking torque and the friction coefficient are obtained to calculate the reliability index of the friction coefficient ; (3) Calculation: The hinge point structure belongs to a series type of system logic control, and when each hinge point can normally complete the specified action, the reliability of the disc brake can be guaranteed. The hinge point reliability index is obtained by assuming the average failure rate of the mechanical hinge point, and then the reliability of the series system is obtained according to the specific number of hinge points of the brake . Finally, according to the reliability index analysis of the single jaw disc brake, the specific structure of the multi-jaw disc brake and the geometric relationship between different jaw discs are considered to obtain the comprehensive reliability index of the multi-jaw disc brake.

[0077] Specifically, the reliability of each jaw disc unit of the multi-jaw disc brake is calculated by the following formula:

[0078] ,

[0079] wherein, represents the th jaw disc;

[0080] The Weibull life distribution model of the disc spring group is calculated as follows:

[0081] ,

[0082] wherein, t is a time variable, represents the time experienced by the multi-jaw disc brake from the start of operation to the occurrence of failure or reaching a specific state (such as a wear threshold), is a characteristic life, is a shape parameter;

[0083] According to the brake bench experiment data, the following is derived from the tolerance range probability of the normal distribution of the friction coefficient:

[0084] ,

[0085] wherein, is a standard normal cumulative function, z is a normalized friction coefficient, used in the standard normal cumulative function Φ(z) to quantify the amplitude distribution characteristics of the friction coefficient;

[0086] According to the series system model, the following is calculated:

[0087] ,

[0088] wherein, is the number of hinge points of the single-caliper disc brake, is the average reliability of the hinge point.

[0089] And, considering the redundancy structure of the parallel caliper disc and the actual load imbalance, a load factor is introduced to correct the comprehensive reliability index of the multi-caliper disc brake :

[0090] ,

[0091] wherein, represents the actual load borne by the i-th caliper disc unit, represents the total load borne by all the units of all the caliper discs in the system.

[0092] Finally, the correctness of the braking performance reliability of the multi-caliper disc brake can be verified through experiments.

[0093] In step S12, first operation data of the multi-caliper disc brake is acquired and preprocessed.

[0094] In some embodiments, the first operation data and the second operation data described below include, but are not limited to, one or more of the following: temperature of the multi-caliper disc brake, vibration signal of the multi-caliper disc brake, pressure signal of the multi-caliper disc brake, displacement signal of the brake caliper.

[0095] In this embodiment, sensors are installed to monitor the working parameters of the brake caliper, such as pressure, temperature, displacement, etc., and continuously record the data during the operation of the brake system. Optionally, the vibration sensor, temperature sensor, coil voltage detection module, etc. shown in the disc brake measurement structure of Embodiment 7 are used to acquire the vibration signal, temperature, pressure signal, etc. of the multi-caliper disc brake.

[0096] In step S13, a current brake system state estimation value is acquired according to the preprocessed first operation data, and an operation data range corresponding to the current brake system state estimation value is acquired.

[0097] ​​In some embodiments, an observer technique is employed to obtain the current brake system state estimation value according to the preprocessed first operation data and a dynamic equation, wherein the dynamic equation is a linear dynamic equation used to describe dynamic characteristics of the brake system.

[0098] wherein the linear dynamic equation is shown in the following formula (1):

[0099]

[0100] (1)

[0101] wherein, is a state vector, the state vector including state information of the brake system; is an input vector; is an output vector, the output vector including readings of various types of acquisition sensors; , , is a Markov jump system dynamic characteristic matrix, the Markov jump system dynamic characteristic matrix being related to a preset state in a Markov jump system state transition model; is process noise; is measurement noise.

[0102] In further embodiments, the state transition probability in the state transition matrix is updated by the input vector.

[0103] In an alternative embodiment, a Kalman filter or extended Kalman filter technique is employed to calculate the current brake system state estimation value. Illustratively, employing the Kalman filter technique to calculate the current brake system state estimation value includes employing the Kalman filter technique to calculate a brake drag gap and a brake time according to the preprocessed first operation data; generating fusion data according to the brake drag gap and the brake time and a temperature of the multi-caliper disc brake; and calculating the current brake system state estimation value according to the fusion data and a Markov jump system state transition model.

[0104] In step S14, the second operation data of the multi-caliper disc brake is acquired in real time and preprocessed.

[0105] In further embodiments, the diagnostic method further includes updating the state transition matrix according to the first operation data and the second operation data of the multi-caliper disc brake and maintenance information of the elevator.

[0106] In step S15, when the pre-processed second operation data is out of the operation data range, the fault type of the multi-caliper disc brake is determined according to the current braking system state estimation value, the Markov jump system state transition model and the state transition matrix.

[0107] In some embodiments, determining the fault type of the multi-caliper disc brake according to the current braking system state estimation value, the Markov jump system state transition model and the state transition matrix can include: comparing the current braking system state estimation value with each preset state in the Markov jump system state transition model, and calculating the probability value of each Markov state in the Markov jump system state transition model in combination with the state transition matrix, and determining the fault type of the multi-caliper disc brake according to the probability value.

[0108] By using the above diagnostic method of embodiment 1 of the present application, on the basis of the Markov jump system state transition model, the braking system state estimation value is obtained through the operation data of the multi-caliper disc brake, and when there is a difference between the operation data of the multi-caliper disc brake and the state estimation value, the fault type / potential fault of the multi-caliper disc brake can be accurately detected, thereby providing support for the daily maintenance and repair of the elevator braking system using the multi-caliper disc brake.

[0109] In further embodiments, the diagnostic method further includes: after determining the fault type, locating the faulty brake caliper according to the following steps: extracting the fault features of the vibration signal of each brake caliper in the pre-processed first operation data and the pre-processed second operation data, the fault features including time domain fault features and frequency domain fault features; inputting the time domain fault features and the frequency domain fault features of the vibration signal into a fault pattern recognition model, the fault pattern recognition model outputting the fault pattern of the brake caliper; and locating one or more brake calipers with faults according to the abnormality degree value of the fault features of the vibration signal of each brake caliper. The time domain fault features of the vibration signal include but are not limited to one or more of the following: peak value, mean value, variance, kurtosis of the vibration signal; the frequency domain fault features of the vibration signal include but are not limited to: dominant frequency, frequency spectrum energy distribution. In this way, the location of the fault can be accurately located, and the problem that the fault detection method in the prior art only focuses on the overall operation state of the brake and can only diagnose whether the elevator brake fails, the type of the fault, etc., but cannot accurately locate the fault, is solved.

[0110] In still further embodiments, the diagnostic method further includes: after locating the faulty brake caliper, generating an operation suggestion according to the one or more brake calipers with faults. Still further, the diagnostic method further includes: displaying the state of the braking system, the brake caliper with faults and the operation suggestion through a graphical interface.

[0111] Using the diagnostic method described above, the reliability of the spring application and release, as well as the hinge structure reliability, of a single-caliper disc brake are analyzed. A comprehensive reliability index for the multi-caliper disc brake is established based on the specific structure of the multi-caliper disc brake and the geometric relationships between the different calipers. Secondly, through experiments and testing, comprehensive braking capacity evaluation results for high-speed elevators under different caliper failure and performance conditions are collected. Using feature selection and regression analysis, a multi-state transition model is developed to describe the qualitative relationship between individual caliper failures and braking performance. Finally, based on the comprehensive braking capacity evaluation results during actual elevator braking, the multi-caliper disc brake reliability index and the single-caliper failure-brake performance relationship model are used to analyze whether one or more caliper failures are causing unsatisfactory elevator braking performance. Furthermore, if an abnormality occurs during braking or exceeds a set maintenance threshold, the system automatically issues an alarm and performs fault analysis, accurately indicating the specific fault location, providing support for routine inspection and maintenance of elevator braking systems using multi-caliper disc brakes.

[0112] [Example 2]

[0113] Embodiment 2 of the present invention provides a method for diagnosing and locating faults of an elevator multi-caliper brake based on a Markov jump system model, wherein the elevator braking system includes the multi-caliper brake, which includes multiple brake calipers.

[0114] In a second embodiment of the present invention, the method may include at least: Step 1: Establishing a Markov Jump System (MJS) model for different brake caliper faults; and Step 2: Fault diagnosis and location. These steps are described in detail below.

[0115] Step 1: Establishing the Markov jump system model includes the following steps:

[0116] Step S211: define the possible states of different brake calipers in the brake system, that is, define the state space. For example, through a finite set Indicates the possible states of different brake calipers in the brake system. It is in normal operating state; A minor fault in a single brake caliper (for example, minor wear of the friction pad); Several brake calipers had minor faults; A single brake caliper has a moderate fault (for example, moderate wear of the friction pad); It is a moderate failure of multiple brake calipers; A single brake caliper has a serious fault (for example, the brake caliper is stuck); There was a serious failure of multiple brake calipers.

[0117] In the above example, all possible states in the defined braking system comprehensively cover the normal working state of the brake and various fault states, for example, including the following states: .

[0118] Among them, the normal operation state ( ) indicates that all components of the brake are working normally, without any fault occurring, and the system is in the best working state.

[0119] Single brake caliper slight fault state ( ) indicates that a brake caliper in the braking system has slight wear or performance has slightly decreased, but the overall braking performance is still within the safe range and does not affect the normal operation of the elevator.

[0120] Multiple brake caliper slight fault state ( ) indicates that multiple brake calipers in the braking system have slight faults, which may have a certain cumulative impact on braking performance and need to be closely monitored.

[0121] Single brake caliper moderate fault state ( ) indicates that a brake caliper in the braking system has moderate wear or performance has significantly decreased, and the braking performance is greatly affected, which may cause abnormal operation of the elevator.

[0122] Multiple brake caliper moderate fault state ( ) indicates that multiple brake calipers in the braking system have moderate faults, and the braking performance has severely decreased, which poses a safety hazard to the operation of the elevator.

[0123] Single brake caliper severe fault state ( ) indicates that a brake caliper in the braking system has severe wear or is completely failed, and the braking performance is basically lost, making the elevator unable to brake safely.

[0124] Multiple brake caliper severe fault state ( ) indicates that multiple brake calipers in the braking system have severe faults, and the braking system is completely failed, making the elevator in an extremely dangerous state.

[0125] Step S212, constructing a state transition matrix. The state transition matrix describes the probability of the system transitioning from state to state , which can be estimated based on historical data, expert knowledge, or fault tree analysis, etc. For example, based on the state set S in step S211, assume that the state transition matrix is as follows:

[0126]

[0127] wherein, represents the probability of the system transitioning from state to state , for example, represents the probability of transitioning from the normal operating state to the single-caliper-mild-failure state . Meanwhile, and the sum of the elements of each row is equal to 1, i.e., .

[0128] For example, may represent the probability of a single-caliper mild failure due to some reason (such as normal wear of the friction plate) in the normal operating state. Similarly, may represent the probability of multiple-caliper severe failure due to failure propagation or failure to repair in time in the single-caliper severe failure state.

[0129] By collecting a large amount of historical operation data and combining with industry expert knowledge, the probabilities of jumping from one state to another are scientifically estimated. Based on these probability values, a state transition matrix is constructed, and each element P( | ) in the matrix represents the probability of the system transitioning from state to state , providing a quantitative basis for state prediction and failure mode identification.

[0130] In actual situations, the transition probabilities between different states in the state transition matrix may be influenced by various factors, such as the usage environment of the brake, the maintenance status, the driving habits of the driver, etc. For example, when the elevator multi-caliper disc brake is regularly maintained, it may increase the probability of recovering from the failure state to the normal state; if the elevator is in a high-load operating state for a long time, it may increase the probability of transitioning from the normal state to the failure state. Alternatively, accurate state transition matrices can be obtained by performing detailed system analysis and failure mode effect analysis (FMEA).

[0131] Step S213, define the system dynamic equation. For each state , define a dynamic equation to describe the system's operating behavior in that state.

[0132] In an exemplary embodiment, the dynamic characteristics of the brake system are described using a linear dynamic equation shown in the following formula (2):

[0133]

[0134] (2)

[0135] wherein, is a state vector containing state information of the brake system; is an input vector; is an output vector, which includes readings from various types of acquisition sensors; , , is a Markov jump system dynamic matrix, which is related to the state transition model in the Markov jump system state transition model; is process noise; is measurement noise.

[0136] wherein the input vector may be the action of external forces / external events on the brake or other external inputs. For example, regular maintenance may increase (the value from failure to normal), while long-term high-load operation may increase (the value from normal to failure).

[0137] In this embodiment, the application of the dynamic equation in fault diagnosis and localization includes: (1) state estimation: during the fault diagnosis process, the system state is estimated using the dynamic equation. By comparing the difference between the estimated state and the actual measurement value, potential faults are detected. (2) Fault isolation: when a fault is detected, the dynamic equation and the state transition matrix work together in the fault isolation algorithm. By analyzing the path and probability of system state transition, it can help to determine which specific brake caliper is faulty. (3) Model updating: by continuously updating the model parameters (such as the state transition matrix and the system matrix), the model can adapt to the changes of the system over time, improve the efficiency and accuracy of fault diagnosis and localization, and thus realize real-time analysis of elevator safety risks, rapid early warning, and risk avoidance.

[0138] In another exemplary embodiment, the dynamic equation is as shown in the following formula (3):

[0139] (3)

[0140] wherein, is the state vector at time k; is an input vector; is the state vector at time k+1; , is a system matrix and an input matrix related to the state transition model in the Markov jump system state transition model; is process noise. Wherein, depicts the state transition relationship (such as brake caliper failure changing the decay law of braking torque, making wear-related parameters change in Reflects the effect of input on the state (e.g. when the brake caliper fails, the same control input The generated braking torque may be reduced, resulting in matrix elements change). In other words, 、 According to the status ( Therefore, combined with the state transfer matrix, the model can describe the dynamic behavior of the brake under different brake caliper fault states and provide a basis for fault diagnosis and location.

[0141] Step 2, fault diagnosis and location, specifically includes the following steps S221 to S224:

[0142] Step S221: Data collection: High-precision sensors are used to collect real-time brake operation data, such as braking time, braking force, brake temperature, vibration signals, etc., and the collected data is pre-processed to improve data quality.

[0143] In this embodiment, vibration sensors are used to accurately locate faults and assess their severity. Specifically, a triaxial vibration accelerometer is placed on the brake shoe corresponding to each brake caliper. It continuously collects vibration signals during braking at a high sampling frequency, monitoring and recording the operating status of each brake caliper in real time. The collected data is then pre-processed by denoising and normalizing it to eliminate environmental noise and other interfering factors, improve data quality, and unify data of different dimensions to a common scale for subsequent analysis. Furthermore, by deeply extracting the time and frequency domain features of the vibration signal and using machine learning algorithms for intelligent pattern recognition, the fault mode of the brake caliper can be accurately determined.

[0144] Step S222: State estimation. Using Kalman filtering and other observer techniques, the system state is accurately estimated based on the data collected in step S221. , get the estimated value of the current system state. Specifically including the following steps 2.1~2.3:

[0145] Step 2.1: Calculate the brake clearance.

[0146] Based on the principle of Newtonian mechanics, displacement can be obtained by the quadratic integration of acceleration over time. That is, the vibration acceleration signal during the operation of the brake can be collected in real time by the vibration acceleration sensor. In theory, Performing an integration can get the speed signal , the integral formula is: . Then Perform secondary integration to obtain the displacement signal, i.e. the brake clearance data. , the integral formula is: .

[0147] However, direct integration will introduce error accumulation effect. Therefore, in this embodiment, the extended Kalman filter (EKF) is introduced to combine the double integration process with state estimation to avoid the error accumulation caused by direct integration. Specifically, the EKF converts the acceleration signal As input, through the nonlinear state transfer function Predict the state at the next moment and use the Kalman gain Correct the predicted values.

[0148] The state prediction equation is shown in the following formula (4):

[0149] (4)

[0150] The covariance prediction equation is shown in the following formula (5):

[0151] (5)

[0152] The calculation formula of Kalman gain is shown in the following formula (6):

[0153] (6)

[0154] In the above formulas (4) to (6), represents the prior state estimate at time k, express The posterior state estimate at time t, express System input at any time, represents the nonlinear state transfer function of the system, express The prior covariance estimate of the time, express The posterior covariance estimate at time , express exist The Jacobian matrix at , express The process noise covariance matrix at time , represents the observation matrix, represents the measurement noise covariance matrix. is the observation matrix of the kth time step, and its mathematical expression is:

[0155]

[0156] in, is the observation vector of the kth time step, which contains the data collected by the sensor in real time (such as temperature, vibration, and pressure signals). is the system state vector at the kth time step, which represents the key state parameters of the braking system (e.g., brake clearance, brake time, friction coefficient). is the observation matrix, which defines the linear mapping relationship from the state vector to the observation vector, i.e., represents the sensor reading predicted based on the current state. is the measurement noise at the kth time step, which represents the random error in the sensor measurement process.

[0157] In this embodiment, the EKF takes the acceleration signal as the observation value and continuously optimizes the state estimation through iterative calculation. By adjusting the process noise covariance matrix Q and the measurement noise covariance matrix R, the filter can adapt to the noise characteristics of the actual system, thereby effectively suppressing the integral error and finally outputting an accurate brake clearance estimation value.

[0158] Step 2.2: Calculate the brake time.

[0159] The brake time is calculated based on the smoother and more accurate speed data obtained through Kalman filtering processing. The brake time is defined as the time required for the speed to rise from 0 to the maximum value and then drop from the maximum value to 0. The specific calculation steps include:

[0160] (1) Determine the speed threshold: Set the speed threshold to 10% of the maximum speed, i.e., where is the maximum speed during braking.

[0161] (2) Identify the speed change points: Identify the time point when the speed first reaches , the time point when the speed reaches , and the time point when the speed last drops to .

[0162] (3) Calculate the brake time: The brake time is calculated by the following formula (7):

[0163] ( (7)

[0164] That is, the brake time is calculated based on the total time of the acceleration phase duration ( ) and the deceleration phase duration ( ), and then divided by the coefficient b, where the value of the coefficient b is obtained based on a large amount of actual data analysis, for example, the value of the coefficient b is 0.8.

[0165] Through the above method, the brake time of the brake can be accurately calculated, thereby providing an important basis for brake performance evaluation and fault diagnosis.

[0166] Step 2.3: Brake state estimation based on data fusion

[0167] In this embodiment, the method of data fusion is adopted, the brake gap value, the brake time and the synchronously measured temperature value are combined, and a Markov Jump System (MJS) model is introduced for comprehensive judgment, so that the state of the brake can be comprehensively evaluated. The specific steps include:

[0168] (1) Markov Jump System (MJS) model construction: different states of the brake (such as normal, stuck, insufficient braking force, overheating failure, etc.) are modeled as different states of Markov chain. Define the state transition probability matrix to describe the possibility of random jump of the brake state between different modes.

[0169] (2) Data fusion and state estimation: in the process of data fusion, not only the fusion of sensor data is considered, but also the possibility of each Markov state is calculated based on the MJS model. Use weighted average method, Kalman filter or neural network method to fuse multi-sensor data, and consider the jump possibility of the brake state. The fused data contains the comprehensive information of the brake gap, the brake time and the temperature value, and the jump possibility of the brake state, which is used to quantitatively evaluate the overall state of the brake.

[0170] (3) Brake state possibility calculation: based on the MJS model, the possibility of the brake in each state is calculated. Specifically, the sensor data and the state transition probability matrix are combined, and the state possibility is updated using Bayes theorem or similar method.

[0171] Among them, the jump possibility of the brake state is the prior probability based on historical data and physical laws (such as brake failure mechanism, historical fault statistical data), which describes the randomness of state transition (such as ), which can predict the potential failure evolution path and build a risk warning baseline; the brake state possibility is the posterior probability of fused real-time sensor data, which quantifies the probability of being in each state, which can locate the current fault state in real time and provide accurate diagnosis basis.

[0172] The jump possibility of the brake state provides a basis for the calculation of the brake state possibility, and reflects the probability information of the transition of the brake between different states. The brake state possibility is quantitatively evaluated on the basis of the jump possibility and in combination with the real-time sensor data, so as to evaluate the probability that the brake is currently in each state. The two affect each other and jointly act on the fault diagnosis process. The jump possibility is used to predict the potential fault risk, and the state possibility is used to accurately evaluate the current state, thereby providing support for fault diagnosis and decision-making.

[0173] (4) Brake state estimation and decision-making: The brake state is estimated according to the fused data and the state possibility. A threshold or decision rule is set. When the possibility of a certain state exceeds the threshold, it is judged that the brake is in the state. According to the state of the brake, corresponding maintenance measures or alarms are taken.

[0174] (5) Judgment of possible problems of the brake: In an exemplary embodiment, a double-index fault criterion is constructed through the combination analysis of the brake drag gap and the braking time, for example: a. When the brake drag gap is too small and the braking time is short, and the possibility of the stuck state is high, it is judged that the brake has a stuck phenomenon; b. When the brake drag gap is too large and the braking time is long, and the possibility of the brake force deficiency state is high, it is judged that the brake has a brake force deficiency.

[0175] In another exemplary embodiment, the possible problems of the brake can be analyzed based on the combination of temperature and braking time, for example: when the temperature is too high and the braking time is prolonged during braking, and the possibility of the overheating failure state is high, it is judged that the brake has an overheating failure risk.

[0176] In some embodiments, the possible problems of the brake can be analyzed comprehensively in combination with multiple indexes, for example: multiple indexes such as the brake drag gap, the braking time and the temperature value, and the possibility of the brake state are comprehensively analyzed to judge other possible problems of the brake.

[0177] Step S223, fault mode identification. According to the estimated brake state in step S222, the possibility of each Markov state is calculated, and the fault mode in which the current system is located is identified.

[0178] In this embodiment, the specific method of mode identification includes: comparing the estimated system state with each preset state in the Markov jump system model in detail, combining the state transition matrix, and calculating the possibility of each Markov state. By deeply analyzing the difference between the estimated state and the actual measurement value, the fault mode in which the current system is located is accurately identified.

[0179] The estimated value of the current system state obtained in step S222 provides the basis for calculating the probability of a Markov state. The estimated value of the current system state is a preliminary estimate / approximation of the system's current state based on sensor data and techniques such as Kalman filtering. The Markov state probability, based on this estimate, combines the Markov jump system model and the state transition matrix to further refine and quantify the probability of the system being in each preset state. The two are interdependent, completing the process from preliminary state estimation to accurate fault mode identification.

[0180] In an exemplary embodiment, assuming that the system is in a normal operating state ( ) The corresponding brake temperature should be within the normal range. In the data acquisition step, the sensor collects the brake temperature data in real time as the actual measurement value. Based on the collected data, the Kalman filter and other observer technologies are used to estimate the current system state. Assume that (Normal operating state). However, if the actual measured value shows that the brake temperature is abnormally high and exceeds the normal range, the estimated state ( ) and the actual measured value. By deeply analyzing this difference and combining the state transfer matrix and Markov jump system model, it can be determined that the system may have shifted from a normal operating state to a fault state, such as a minor fault state of a single brake caliper ( Furthermore, according to the identified failure mode, corresponding maintenance measures are taken, such as inspecting and replacing the faulty brake caliper.

[0181] Step S224: Fault location: Based on the identified fault mode, determine which brake caliper has failed and the severity of the fault.

[0182] In some implementations, fault features are extracted from pre-processed collected data (e.g., vibration signals) to comprehensively capture vibration information during braking, laying the foundation for subsequent intelligent identification and precise location of fault modes. Fault feature extraction includes deep mining of time-domain features, precise analysis of frequency-domain features, and the fusion of time-frequency analysis.

[0183] Specifically, deep mining of time-domain features involves extracting key parameters of the vibration signal, such as peak value, mean, variance, and kurtosis (i.e., vibration fault signatures). These parameters comprehensively reflect the intensity and variation patterns of the vibration signal during the braking process. In-depth analysis of these time-domain features provides a crucial basis for fault identification, helping to accurately determine the operating status of the brake caliper.

[0184] Further, in order to more accurately analyze the fault characteristics, the following frequency domain characteristics are accurately analyzed in this embodiment: through fast Fourier transform (FFT), the time domain signal is converted into a frequency domain signal, thereby extracting the main frequency, frequency spectrum energy distribution and other frequency domain characteristics. These characteristics help to accurately identify whether there is an abnormal frequency component in the braking process, and further accurately judge whether the brake caliper is faulty, thereby providing strong support for fault positioning.

[0185] Further, based on the deep extraction of fault characteristics, intelligent fault mode recognition and accurate positioning are realized. Specifically, advanced machine learning algorithms such as support vector machines, neural networks, etc. are used to deeply train and learn the extracted fault characteristics, and a high-precision fault mode recognition model is established. The vibration signal characteristics of each brake caliper are input into the fault mode recognition model, and the model can automatically and accurately identify the fault mode of the brake caliper. After identifying the fault mode, the abnormal degree of each brake caliper vibration signal (i.e. the deviation degree of the fault characteristics from the normal state) is further determined by comparing and analyzing the vibration sensor data corresponding to each brake caliper, to determine which brake caliper has a fault.

[0186] For example, during the braking process of the elevator, the system detects that the energy of the vibration signal of a certain brake caliper suddenly increases significantly in a certain frequency band, while the vibration signals of other brake calipers remain normal. The abnormal vibration signal characteristics are input into the fault mode recognition model, and the model analyzes that the abnormal signal matches the fault mode of "brake caliper jamming". Therefore, it can be judged that the brake caliper with abnormal vibration signal has a jamming fault, and the corresponding alarm device is triggered immediately to prompt the maintenance personnel to check and maintain the brake caliper. Further, the controller can be adjusted to compensate for the fault impact.

[0187] In this embodiment, the built model is applied to the monitoring of the braking system, which can predict the occurrence of faults by real-time detection of the system state and take corresponding measures for prevention and maintenance.

[0188] The above method for diagnosing and positioning the elevator multi-caliper disc brake fault according to embodiment 2 of the application is adopted, a Markov jump system model is constructed, different states of the brake are accurately estimated, and the current fault mode of the system is identified. Further, by comparing and analyzing the abnormal degree of the vibration signal of each brake caliper, it can be determined which brake caliper has a fault, and accurate fault positioning is realized.

[0189]

Embodiment 3

[0190] Figure 2 is a flowchart of the method for diagnosing and positioning the elevator multi-caliper disc brake based on the Markov jump system model according to embodiment 3 of the application.

[0191] like Figure 2 As shown, in embodiment 3 of the present invention, the method for diagnosing and positioning an elevator multi-caliper brake may include at least the following steps:

[0192] Firstly, a physical model is established based on the physical structure of the multi-caliper disc brake and the geometric relationship between different calipers to evaluate the comprehensive reliability index of the brake.

[0193] Secondly, information such as brake pad friction, brake disc torque, and brake disc clearance is obtained for each caliper in a multi-disc brake (caliper C1, C2, ..., CN) as individual caliper data. This data is then integrated. Through fault experiments and tests, comprehensive high-speed elevator braking capacity evaluation results are collected under different caliper fault and actual conditions. A Markov jump system state transition model is used to explore the qualitative relationship between individual caliper faults and braking performance, achieving a "fault-performance" feature mapping. The overall performance of the multi-disc brake (such as braking force, stability, heat dissipation, durability, friction coefficient, wear rate, and noise) is determined based on parameters such as caliper shape, area, distribution, effective contact area, number of calipers, location, and material.

[0194] Finally, by designing a relationship mining and decision-making model based on causal reasoning, self-attention mechanisms, and decision trees, we were able to mine the reliability indicators of multi-caliper disc brakes and the relationship between single-caliper faults and brake performance. This allowed us to analyze the correlation between evaluation results and faults, determine whether a fault has occurred, and assess the fault risk level. In particular, the model simultaneously considers both fault type and location, providing effective data support for efficient inspection and maintenance of elevator brake systems.

[0195] The method for diagnosing and locating a multi-caliper brake for an elevator, as described in Example 3 of the present invention, uses comprehensive braking capacity assessment results from actual elevator braking, along with multi-caliper brake reliability indicators and a single-caliper fault-brake performance relationship model, to analyze whether one or more brake caliper faults are causing unsatisfactory braking performance. When an abnormality occurs during braking or exceeds a set maintenance threshold, an automatic alarm is generated and a fault analysis is performed, accurately indicating the specific fault location. This provides support for routine inspection and maintenance of elevator braking systems using multi-caliper brakes.

[0196] [Example 4]

[0197] Figure 3 1 is a flow chart of a method for diagnosing and positioning an elevator multi-caliper brake based on a Markov jump system model according to embodiment 4 of the present invention.

[0198] like Figure 3As shown, in Embodiment 4 of the present application, the implementation of elevator multi-caliper brake diagnosis and positioning can at least include the following steps: step S31, step S32, step S33, step S34, step S35, step S36 and step S37, which will be described in detail below.

[0199] In step S31, the fault diagnosis system state and parameters are initialized. The core of the fault diagnosis system is a Markov jump model, which is established according to the methods in Embodiments 1 to 3 to describe the characteristic mapping between single brake caliper faults and brake performance of the multi-caliper brake. Thus, the performance evaluation result of the multi-caliper brake can be determined based on the collected sensor data, and the correlation between the performance evaluation result and the fault can be analyzed to determine whether a fault occurs, evaluate the fault risk level, and further determine the fault type and fault location, thereby providing effective data support for efficient maintenance and repair of the elevator brake system.

[0200] In step S32, real-time sensor data is collected and the collected sensor data is preprocessed. The sensors collect working parameters of the multi-caliper brake, including but not limited to one or more of the following: braking torque, friction amount, gap, response time.

[0201] In step S33, based on the preprocessed sensor data, a state estimator is used for state estimation.

[0202] In step S34, it is determined whether there is a significant difference between the estimated state and the actual measurement. When there is no significant difference between the estimated state and the actual measurement, return to step S32; when there is a significant difference between the estimated state and the actual measurement, perform preliminary fault detection in step S35.

[0203] In step S35, preliminary fault detection is performed to determine the fault type.

[0204] In step S36, a fault isolation algorithm is executed to obtain a specific fault diagnosis result. In this embodiment, when a fault is detected, the dynamic equation and the state transition matrix are jointly applied to the fault isolation algorithm to determine which brake caliper is faulty by analyzing the path and probability of system state transition. The fault isolation algorithm accurately locates the faulty brake caliper by analyzing the system state transition probability and sensor features, and its core steps include: 1. Extracting time / frequency domain features of temperature, vibration and pressure signals; 2. Inputting a fault pattern recognition model; 3. Determining the faulty component according to the abnormal value. This fault isolation algorithm relies on the Markov state transition matrix and Kalman filter state estimation.

[0205] In step S37, corresponding measures are taken according to the fault diagnosis and positioning results. In the present embodiment, the fault position is accurately positioned by a pattern recognition algorithm in combination with time domain and frequency domain feature extraction. Further, a decision suggestion is generated according to the fault positioning results, and corresponding control measures are executed, such as starting an alarm system, adjusting elevator operation parameters, or performing maintenance, etc.

[0206] In further embodiments, the model parameters (such as state transition matrix and system matrix) are updated according to the fault diagnosis and positioning results and the process returns to step S32 for continued fault diagnosis and positioning. By continuously updating the model parameters, the model can adapt to changes in the system over time, improving the efficiency and accuracy of fault diagnosis and positioning, and thus realizing real-time analysis, rapid warning, and risk avoidance of elevator safety risks.

[0207]

Embodiment 5

[0208] Figure 4 is a schematic diagram of the architecture of a multi-caliper brake diagnostic system for an elevator based on a Markov jump system model according to Embodiment 5 of the present application. The brake system of the elevator includes the multi-caliper brake, which includes a plurality of brake calipers.

[0209] As shown in Figure 4 , the diagnostic system includes a model parameter acquisition module 410, a data acquisition module 420, a state estimation module 430, a fault diagnosis module 440, a fault positioning module 450, a suggestion generation module 460, and a display module 470.

[0210] The model parameter acquisition module 410 is configured to acquire a state transition matrix between each of the preset states in the Markov jump system state transition model.

[0211] In some embodiments, the Markov jump system state transition model describing the feature mapping between a single brake caliper fault and the braking performance of the multi-caliper brake is established based on the multi-caliper brake comprehensive reliability index, the brake caliper fault, and the high-speed elevator comprehensive braking capacity evaluation results. The multi-caliper brake physical model is established based on the physical structure of the multi-caliper brake and the geometric relationship between the calipers of the multi-caliper brake; and the multi-caliper brake comprehensive reliability index is acquired according to the multi-caliper brake physical model.

[0212] In further embodiments, the state transition matrix is updated according to the first and second operation data of the multi-caliper brake, and the maintenance information of the elevator.

[0213] The data acquisition module 420 is used to acquire first operating data of the multi-caliper brake, acquire second operating data of the multi-caliper brake in real time, and pre-process the first operating data and the second operating data.

[0214] In some embodiments, the first operating data and the second operating data described later include but are not limited to one or more of the following: temperature of the multi-caliper brake, vibration signal of the multi-caliper brake, pressure signal of the multi-caliper brake, and displacement signal of the brake caliper.

[0215] In this embodiment, sensors are installed to monitor the working parameters of the brake caliper, such as pressure, temperature, displacement, etc., and continuously record data during the operation of the brake system.

[0216] The state estimation module 430 is configured to obtain a current brake system state estimation value based on the pre-processed first operating data, and obtain an operating data range corresponding to the current brake system state estimation value.

[0217] In some embodiments, an observer technique is used to obtain an estimated value of the current braking system state based on the preprocessed first operating data and a dynamic equation, wherein the dynamic equation is a linear dynamic equation for describing the dynamic characteristics of the braking system.

[0218] In an optional embodiment, a Kalman filter or an extended Kalman filter technique is used to calculate the current brake system state estimate. Exemplarily, using the Kalman filter technique to calculate the current brake system state estimate includes: using the Kalman filter technique to calculate brake clearance and braking time based on the preprocessed first operating data; generating fused data based on the brake clearance and braking time and the temperature of the multi-caliper brake; and calculating the current brake system state estimate based on the fused data and a Markov jump system state transition model.

[0219] The fault diagnosis module 440 is configured to determine a fault type of the multi-caliper brake according to the current brake system state estimation value, the Markov jump system state transition model, and the state transition matrix when the preprocessed second operating data exceeds the operating data range.

[0220] In some embodiments, determining the fault type of the multi-caliper brake based on the current brake system state estimate, the Markov jump system state transition model, and the state transition matrix may include: comparing the current brake system state estimate with each preset state in the Markov jump system state transition model, and calculating the probability value of each Markov state in the Markov jump system state transition model in combination with the state transition matrix, and determining the fault type of the multi-caliper brake based on the probability value.

[0221] The fault location module 450 is used to locate the faulty brake caliper according to the following steps: extracting the fault characteristics of the vibration signal of each brake caliper in the preprocessed first operating data and the preprocessed second operating data, the fault characteristics including time domain fault characteristics and frequency domain fault characteristics; inputting the time domain fault characteristics and frequency domain fault characteristics of the vibration signal into a fault mode recognition model, and the fault mode recognition model outputs the fault mode of the brake caliper; and locating one or more faulty brake calipers according to the abnormality degree value of the fault characteristics of the vibration signal of each brake caliper.

[0222] The suggestion generating module 460 is configured to generate an operation suggestion based on the faulty one or more brake calipers.

[0223] The display module 470 is used to display the status of the brake system, faulty brake calipers, and operation suggestions through a graphical interface. By visually displaying the real-time status of the brake system, fault details, and operation suggestions, it is convenient for operation and maintenance personnel to inspect and maintain the elevator brake system.

[0224] The diagnostic system of Example 5 of the present invention, based on a Markov jump system state transition model, uses the operating data of the multi-caliper brake to obtain an estimated brake system state. When there is a discrepancy between the operating data and the estimated state, the system accurately detects the fault type or potential fault of the multi-caliper brake, providing support for the routine inspection and maintenance of elevator brake systems using multi-caliper brakes. Furthermore, the faulty brake caliper can be precisely located.

[0225] [Example 6]

[0226] Figure 5 1 is a schematic diagram of a layer framework of an elevator multi-caliper brake fault diagnosis system based on a Markov jump system model according to a sixth embodiment of the present invention; Figure 6 yes Figure 5 Schematic diagram of the processing operations performed by each hierarchical architectural component in the fault diagnosis system shown.

[0227] like Figure 5 As shown, the fault diagnosis system includes: a sensor layer 510, a data acquisition layer 520, a state estimation layer 530, a fault diagnosis and positioning layer 540, a decision and control layer 550, a user interface layer 560 and a model update and optimization module 570.

[0228] The sensor layer 510 deploys multiple types of sensors (such as vibration, temperature, and pressure) to collect real-time physical parameters of the brake caliper. This sensor layout is optimized using a multi-point layout strategy, as well as sensor calibration and maintenance strategies.

[0229] The data acquisition layer 520 is used to pre-process the collected original signals (denoising, normalization), extract time domain / frequency domain features (peak value, spectrum energy, etc.) and store them.

[0230] The state estimation layer 530 uses an extended Kalman filter (EKF) to calculate key state parameters (brake clearance, braking time); defines the Markov discrete state space (normal, minor fault, moderate fault, major fault, etc.) and the state transition probability matrix, and converts the EKF parameter estimation results into the likelihood of each discrete state through feature mapping; based on the Bayesian recursive framework, it integrates the Markov state transition prior and the EKF likelihood information to calculate the posterior probability of the brake being in each discrete state, and finally uses the state corresponding to the maximum posterior probability as the system state estimate.

[0231] The fault diagnosis and location layer 540 uses advanced machine learning algorithms, such as support vector machines and neural networks, to conduct in-depth training and learning on the extracted fault features and establish a high-precision fault pattern recognition model; the vibration signal characteristics of each brake caliper are input into the fault pattern recognition model, and the model can automatically and accurately identify the fault mode of the brake caliper; after identifying the fault mode, combined with the vibration sensor data corresponding to each brake caliper, the abnormality of each brake caliper vibration signal (that is, the degree of deviation between the fault characteristics and the normal state) is compared and analyzed to determine the brake caliper with a specific fault.

[0232] Furthermore, based on the MJS model, the state space is defined ( ~ ) and the transition probability matrix, and dynamically adjust the transition probability matrix in combination with historical data.

[0233] The decision-making and control layer 550 triggers alarms, adjusts braking force distribution, and provides maintenance recommendations based on the diagnostic results. The alarm module in the decision-making and control layer uses a threshold-triggered mechanism to generate alarms and associates corresponding control strategies based on the alarm level (fault risk level). These control strategies include, but are not limited to, adaptive force distribution (for example, adjusting the braking force of other calipers in the event of a jam) and fault-tolerant control (for example, switching to a degraded control mode in the event of a fault to ensure basic braking). Maintenance recommendations include fault location and severity (for example, "Brake caliper C2 is moderately worn; replacement recommended within 72 hours").

[0234] The user interface layer 560 is used to visually display real-time brake status, fault details (fault type and location, risk assessment level), and operation logs, and supports human-computer interaction. The operation logs can be used for historical data query and maintenance record management.

[0235] The model update and optimization module 570 continuously optimizes the state transfer matrix and algorithm parameters through online learning to improve the robustness of the system.

[0236] As Figure 6 shown, Figure 5 The hierarchical architecture components in the fault diagnosis system shown are implemented by performing the following processing operations to achieve elevator multi-caliper brake fault diagnosis and positioning:

[0237] Operation S61: The sensor layer monitors the working parameters of the brake caliper in real time, such as pressure, temperature, displacement, vibration, etc., to provide basic data for subsequent fault diagnosis.

[0238] Operation S62: The data acquisition layer collects the data transmitted by the sensor layer and performs preprocessing such as denoising, normalization, etc. to improve data quality.

[0239] Operation S63: The state estimation layer uses observer techniques such as Kalman filter to estimate the state based on the data transmitted by the data acquisition layer, obtaining the estimated value of the current system state.

[0240] Operation S64: The fault diagnosis and positioning layer is based on the Markov jump system model, compares the difference between the estimated state and the actual measured value to detect potential faults; once a fault is detected, further analysis is performed to determine which brake caliper is faulty (fault isolation); through pattern recognition algorithms, combined with time domain and frequency domain feature extraction, the fault location is accurately located (fault location).

[0241] Operation S65: The decision and control layer generates decision suggestions based on the results transmitted by the fault diagnosis and positioning layer, such as alarms, adjustment of operating parameters or maintenance, and executes corresponding control measures such as starting the alarm system, adjusting the elevator operating parameters, etc.

[0242] Operation S66: The user interface layer provides a graphical interface to display system status, fault information and operation suggestions. Allows the operator to view the system status, receive alarm information, and perform corresponding processing according to the operation suggestions.

[0243]

Example 7

[0244] Figure 7 is a schematic diagram of a disc brake measurement structure according to Example 7 of the present application.

[0245] As Figure 7As shown, the vibration sensor 10 is fixed on the brake by magnetic attraction, 8 sensors are installed on each brake block at four corners for block brake, corresponding number of sensors are directly attracted on the brake for disc brake, 4 sensors are installed on the brake arm and brake spring for drum brake, and vibration signals are transmitted to the touch terminal 20 through wired mode; the temperature sensor 30 is measured by non-contact method, directly aiming at the brake shoe, and transmits temperature data by wireless mode; the coil voltage detection module 40 is connected in parallel on the voltage line between the two ends of the brake coil to obtain the voltage, and transmits data by wired mode. During testing, it is necessary to check whether each sensor is fixed firmly and whether the signal transmission is normal, and to verify whether the touch terminal can accurately receive and display vibration, temperature and voltage data through the methods of simulating vibration, heating brake shoe and supplying power to the coil. At the same time, those skilled in the art should understand that the specific measurement installation needs to select sensors and fix them according to the type of brake.

[0246] In some embodiments, the vibration sensor, the temperature sensor and the coil voltage detection module are used as a sensor layer to monitor and collect the working parameters of the brake in real time, and the collected working parameters are uploaded to the host computer of the data collection layer for collection and preprocessing. Further, the diagnosis and positioning methods in the foregoing embodiments 1 to 4 are realized based on the preprocessed data, and the processing operations performed by the components of each level architecture in the foregoing embodiment 6 are performed based on the preprocessed data.

[0247] The structure of the touch terminal 20 is as shown in Figure 8 The touch terminal 20 mainly consists of a lower shell 1, a collection module circuit board 2, a lithium battery 3, a rubber sleeve 4, a touch screen fixing plate 5, a touch screen 6, an upper shell 7 and a panel 8.

[0248] The lower shell 1 is used as the basic support structure of the device, and bears all the internal components. The material of the lower shell 1 is ABS, which is a thermoplastic engineering plastic with excellent wear resistance, impact resistance and chemical corrosion resistance, so that the lower shell is both light and durable.

[0249] The collection module circuit board 2 is the functional core of the device, which is used to collect and process vibration data, temperature data and coil voltage data, and transmit the data and results to the touch screen for user operation.

[0250] The lithium battery 3 provides the required power support for the device to ensure its independent working ability. For example, a 12V lithium battery is used, which has the advantages of high energy density, long service life, fast charging and environmental protection without pollution, and can stably power the collection module circuit board and the touch screen.

[0251] The rubber sleeve 4 is made of PBT, which can play a buffering and protection role to prevent the internal components from being impacted.

[0252] The material of the touch screen fixing plate 5 is aluminum-6061, which is used for fixing the touch screen.

[0253] The touch screen 6 is used for providing a user interaction interface, facilitating operation and display of information. A user can input instructions through the touch screen, and meanwhile, the device can display data or state information through the touch screen.

[0254] The upper shell 7 cooperates with the lower shell 1 to form a complete device shell, protecting internal components. Like the lower shell 1, the upper shell 7 is also made of ABS material, having the characteristics of lightness, beauty, durability, etc.

[0255] The panel 8 is used for covering the touch screen, protecting the screen and providing clear display effect. The material of the panel 8 is transparent acrylic, which has the characteristics of high transparency, wear resistance, easy processing, etc. It can not only protect the touch screen from scratching, but also ensure that the user can clearly see the information on the screen.

[0256] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software combined with a hardware platform. Based on such understanding, all or part of the technical solutions of the present application that contribute to the background art can be embodied in the form of a computer software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method described in each embodiment or some part of the embodiments of the present application.

[0257] Correspondingly, the present application also provides a computer readable storage medium having computer readable instructions or programs stored thereon, which are executed by a processor to cause a computer to perform the following operations, including the steps of the diagnostic method according to any one of the above embodiments, which will not be repeated here. The storage medium can include, for example, optical disks, hard disks, floppy disks, flash memories, magnetic tapes, etc.

[0258] In addition, the present application also provides a computer device including a memory and a processor, wherein the memory is used for storing one or more computer readable instructions or programs, and the one or more computer readable instructions or programs are executed by the processor to implement the diagnostic method according to any one of the above embodiments. The computer device can be, for example, a server, a desktop computer, a notebook computer, a tablet computer, etc.

[0259] The embodiment of the present application further provides a computer program product comprising a computer program containing program codes for executing the diagnostic method shown in the flow chart. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the diagnostic method provided by the embodiment of the present application.

[0260] According to the embodiments of the present disclosure, the program codes of the computer program for executing the embodiments of the present disclosure can be written in any combination of one or more programming languages, and specifically, the computer programs can be implemented by using high-level procedural and / or object-oriented programming language, and / or assembly / machine language. The programming language includes but is not limited to, for example, Java, C++, python, "C" language or similar programming language. The program codes can be executed completely on a user computing device, partially on a user device, partially on a remote computing device, or completely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected to the Internet through an Internet service provider).

[0261] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. Therefore, the protection scope of the present application should be subject to the claims.

Claims

1. A diagnostic method for an elevator multi-caliper brake, wherein the elevator's braking system includes a multi-caliper brake, which includes a plurality of brake calipers, and wherein: The diagnostic method comprises: Establishing a physical model of the multi-caliper brake based on the physical structure of the multi-caliper brake and the geometric relationship between the calipers of the multi-caliper brake; Obtaining a comprehensive reliability index of the multi-caliper brake according to the multi-caliper brake physical model; Based on the comprehensive reliability index of the multi-caliper brake, brake caliper failure, and comprehensive braking capacity evaluation results of high-speed elevators, a Markov jump system state transition model is established to describe the characteristic mapping between a single brake caliper failure and the braking performance of the multi-caliper brake; Obtaining a state transfer matrix between various preset states in the Markov jump system state transfer model; Acquiring first operating data of the multi-caliper brake and performing preprocessing; Obtaining a current brake system state estimate value based on the preprocessed first operating data, and obtaining an operating data range corresponding to the current brake system state estimate value; collecting second operating data of the multi-caliper brake in real time and performing preprocessing; When the preprocessed second operating data exceeds the operating data range, the fault type of the multi-caliper brake is determined according to the current brake system state estimation value, the Markov jump system state transition model and the state transition matrix.

2. The diagnostic method according to claim 1, wherein The diagnostic method further includes updating the state transition matrix according to the first operating data and the second operating data of the multi-caliper brake and the maintenance information of the elevator.

3. The diagnostic method according to claim 1, wherein The first operating data and the second operating data include: a temperature of the multi-caliper brake, a vibration signal of the multi-caliper brake, and a pressure signal of the multi-caliper brake.

4. The diagnostic method according to claim 1, wherein An observer technique is used to obtain a current brake system state estimate value based on the preprocessed first operating data and a dynamic equation, wherein the dynamic equation is a linear dynamic equation for describing the dynamic characteristics of the brake system.

5. The diagnostic method according to claim 4, wherein The linear dynamic equation is: in, is a state vector, the state vector including state information of the braking system; is the input vector; is an output vector, which includes readings of various acquisition sensors; 、 、 is a Markov jump system dynamic characteristic matrix, wherein the Markov jump system dynamic characteristic matrix is ​​related to a preset state in a Markov jump system state transition model; is the process noise; is the measurement noise.

6. The diagnostic method according to claim 5, wherein The state transition probability in the state transition matrix is ​​updated according to the input vector.

7. The diagnostic method according to claim 4, wherein The current braking system state estimation value is calculated using a Kalman filter or an extended Kalman filter technology.

8. The diagnostic method according to claim 7, wherein Calculating the current brake system state estimate using the Kalman filter technique includes: Using Kalman filter technology, calculating the brake clearance and braking time according to the preprocessed first operating data; generating fusion data according to the brake clearance and the braking time and the temperature of the multi-caliper brake; The current braking system state estimation value is calculated based on the fused data and a Markov jump system state transition model.

9. The diagnostic method according to claim 1, wherein Determining the fault type of the multi-caliper brake according to the current brake system state estimation value, the Markov jump system state transition model and the state transition matrix includes: The current brake system state estimate is compared with each preset state in the Markov jump system state transition model, and the probability value of each Markov state in the Markov jump system state transition model is calculated in combination with the state transition matrix, and the fault type of the multi-caliper brake is determined based on the probability value.

10. The diagnostic method according to claim 1, wherein The diagnostic method further includes: after determining the fault type, locating the faulty brake caliper according to the following steps: Extracting fault features of the vibration signal of each brake caliper in the preprocessed first operating data and the preprocessed second operating data, the fault features including time domain fault features and frequency domain fault features; Inputting the time domain fault features and frequency domain fault features of the vibration signal into a fault mode recognition model, the fault mode recognition model outputting a fault mode of the brake caliper; and One or more faulty brake calipers are located according to the abnormality level of the fault characteristic of the vibration signal of each brake caliper.

11. The diagnostic method according to claim 10, wherein The time domain fault characteristics of the vibration signal include: peak value, mean value, variance and kurtosis of the vibration signal; The frequency domain fault characteristics of the vibration signal include: main frequency and spectrum energy distribution.

12. The diagnostic method according to claim 10, wherein The diagnostic method further includes generating an operational recommendation based on the faulty brake caliper or calipers after locating the faulty brake caliper.

13. The diagnostic method according to claim 12, wherein The diagnostic method further includes: displaying the status of the brake system, the faulty brake caliper, and operation suggestions through a graphical interface.

14. An elevator multi-caliper brake diagnostic system, characterized in that: The diagnostic system is used to implement the diagnostic method for an elevator multi-caliper brake according to any one of claims 1 to 13, and the diagnostic system includes: A model parameter acquisition module is used to obtain the state transfer matrix between each preset state in the Markov jump system state transfer model; a data acquisition module, configured to acquire first operating data of the multi-caliper brake, acquire second operating data of the multi-caliper brake in real time, and pre-process the first operating data and the second operating data; a state estimation module, configured to obtain a current brake system state estimation value based on the preprocessed first operating data, and obtain an operating data range corresponding to the current brake system state estimation value; a fault diagnosis module, configured to determine a fault type of the multi-caliper brake according to the current brake system state estimate, a Markov jump system state transition model, and a state transition matrix when the preprocessed second operating data exceeds the operating data range; The model parameter acquisition module obtains the Markov jump system state transition model in the following manner: Establishing a physical model of the multi-caliper brake based on the physical structure of the multi-caliper brake and the geometric relationship between the calipers of the multi-caliper brake; Obtaining a comprehensive reliability index of the multi-caliper brake according to the multi-caliper brake physical model; Based on the comprehensive reliability index of the multi-caliper brake, brake caliper failure and high-speed elevator comprehensive braking capacity evaluation results, the Markov jump system state transition model is established to describe the characteristic mapping between a single brake caliper failure and the braking performance of the multi-caliper brake.

15. The diagnostic system according to claim 14, wherein: The diagnostic system further includes a fault location module configured to locate a faulty brake caliper according to the following steps: Extracting fault features of the vibration signal of each brake caliper in the preprocessed first operating data and the preprocessed second operating data, the fault features including time domain fault features and frequency domain fault features; Inputting the time domain fault features and frequency domain fault features of the vibration signal into a fault mode recognition model, the fault mode recognition model outputting a fault mode of the brake caliper; and One or more faulty brake calipers are located according to the abnormality level of the fault characteristic of the vibration signal of each brake caliper.

16. The diagnostic system according to claim 14, wherein: The diagnostic system further includes a recommendation generating module for generating an operating recommendation based on the faulty one or more brake calipers.

17. The diagnostic system according to claim 16, wherein: The diagnostic system further includes a display module for displaying the status of the brake system, faulty brake calipers, and operation suggestions through a graphical interface.

18. A computer-readable storage medium storing computer-readable instructions, characterized in that: The computer-readable instructions are executed by a processor to implement the diagnostic method according to any one of claims 1 to 13.

19. A computer device comprising a memory and a processor, The memory stores computer-readable instructions, characterized in that: The processor executes the computer-readable instructions to implement the diagnostic method according to any one of claims 1 to 13.

20. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the diagnostic method according to any one of claims 1 to 13 is implemented.

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