Brake fault monitoring method, device, equipment, medium and product

Abstract

The invention discloses a brake fault monitoring method, device and equipment, a medium and a product, and relates to the technical field of data processing.The method is applied to an elevator brake and a cloud server, firstly, in response to the change of the braking state of the elevator brake, pressure information of the inner surface of a band-type brake is collected; then, performing first processing on the pressure information to obtain electric signal data, and transmitting the electric signal data to a gateway; and finally, performing second processing on the electric signal data through the gateway to obtain to-be-inspected data, and sending the to-be-inspected data to a cloud server through the gateway, so that the cloud server performs consistency inspection on the to-be-inspected data and preset standard data to obtain a monitoring result. According to the method, the high-sensitivity sensor technology, the intelligent signal processing algorithm and the cloud big data analysis platform are integrated, fault monitoring is conducted on the elevator brake, the real-time performance and accuracy of elevator fault detection can be improved, and the fault prediction and maintenance capacity is greatly improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method, device, equipment, medium, and product for monitoring brake failures. Background Art

[0002] The elevator brake is a core component to ensure the safe operation of the elevator, and the failure of its function may lead to serious accidents such as car slipping and elevator falling. At present, the fault monitoring of elevator brakes mainly relies on regular manual inspections or offline detections of traditional mechanical sensors (such as limit switches and displacement sensors), which have some technical defects. For example, key pressure parameters cannot be monitored in real time, the single data dimension leads to the risk of misjudgment, manual inspections rely on experience and are inefficient, etc.

[0003] Therefore, it is urgent to improve the real-time, accuracy, and predictability of elevator brake fault monitoring. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment, medium, and product for monitoring brake failures, aiming to improve the real-time, accuracy, and predictability of elevator brake fault monitoring.

[0005] To achieve the above object, this application proposes a method for monitoring brake failures. The method for monitoring brake failures is applied to an elevator brake, and the elevator brake includes a brake shoe. The method includes:

[0006] In response to a change in the braking state of the elevator brake, collect pressure information on the inner surface of the brake shoe;

[0007] Perform a first process on the pressure information to obtain electrical signal data, and transmit the electrical signal data to a gateway;

[0008] Perform a second process on the electrical signal data through the gateway to obtain data to be tested, and send the data to be tested to a cloud server through the gateway for the cloud server to perform a consistency test on the data to be tested and preset standard data to obtain a monitoring result.

[0009] In one embodiment, a pressure sensor is provided on the inner surface of the brake shoe. The step of collecting pressure information on the inner surface of the brake shoe includes:

[0010] Divide the inner surface of the brake shoe into grid regions, and determine each grid region;

[0011] Detect the pressure of each grid region through the pressure sensor to obtain the pressure information.

[0012] In one embodiment, the step of performing a first process on the pressure information to obtain electrical signal data includes:

[0013] Convert the pressure information into an initial electrical signal;

[0014] Filter the noise of the initial electrical signal through a filter, and perform outlier detection and removal on the filtered initial electrical signal to obtain a processed initial electrical signal;

[0015] Perform a normalization transformation on the processed initial electrical signal to obtain the electrical signal data.

[0016] In one embodiment, the step of performing a second process on the electrical signal data through the gateway to obtain data to be tested includes:

[0017] Determine whether the electrical signal data is less than a preset threshold, and count the number of times the electrical signal data is less than the preset threshold;

[0018] In the case where the number of times the electrical signal data is less than the preset threshold reaches a preset number threshold, use the electrical signal data less than the preset threshold as the data to be tested.

[0019] In addition, to achieve the above object, the present application also proposes a brake failure monitoring method, which is applied to a cloud server, and the method includes:

[0020] Receive data to be tested sent by an elevator brake through a gateway, where the data to be tested is obtained by the elevator brake collecting pressure information on the inner surface of the brake of the elevator brake in response to a change in the braking state of the elevator brake, performing a first process on the pressure information to obtain electrical signal data, and transmitting the electrical signal data to the gateway, and performing a second process on the electrical signal data through the gateway;

[0021] Perform a consistency test on the data to be tested and preset standard data to obtain a detection result.

[0022] In one embodiment, the preset standard data includes a preset sequence value, and the data to be tested includes a to-be-tested sequence value. The step of performing a consistency test on the data to be tested and the preset standard data to obtain a monitoring result includes:

[0023] Identify the data type and structural characteristics of the preset sequence value and the to-be-tested sequence value;

[0024] Calculate a consistency index of the preset sequence value and the to-be-tested sequence value according to the data type and structural characteristics;

[0025] Compare the consistency index with a preset index threshold range;

[0026] If the consistency index is within the preset index threshold range, it is determined that the monitoring result is normal;

[0027] If the consistency index is not within the preset index threshold range, it is determined that the monitoring result is abnormal.

[0028] In addition, to achieve the above object, the present application also proposes a brake failure monitoring device, which includes an elevator brake and a cloud server;

[0029] The elevator brake includes:

[0030] An acquisition module, configured to acquire pressure information on the inner surface of the brake shoe in response to a change in the braking state of the elevator brake;

[0031] A first processing module, configured to perform a first processing on the pressure information to obtain electrical signal data, and transmit the electrical signal data to the gateway;

[0032] A second processing module, configured to perform a second processing on the electrical signal data through the gateway to obtain data to be inspected, and send the data to be inspected to the cloud server through the gateway for the cloud server to perform a consistency inspection on the data to be inspected and the preset standard data to obtain a monitoring result;

[0033] The cloud server includes:

[0034] A receiving module, configured to receive the data to be inspected sent by the elevator brake through the gateway, where the data to be inspected is obtained by the elevator brake in response to a change in the braking state of the elevator brake, acquiring pressure information on the inner surface of the brake shoe of the elevator brake, performing a first processing on the pressure information to obtain electrical signal data, and transmitting the electrical signal data to the gateway, and performing a second processing on the electrical signal data through the gateway;

[0035] An inspection module, configured to perform a consistency inspection on the data to be inspected and the preset standard data to obtain a detection result.

[0036] In addition, to achieve the above object, the present application also proposes a brake failure monitoring device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the brake failure monitoring method as described above.

[0037] In addition, to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the brake failure monitoring method as described above.

[0038] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the brake fault monitoring method described above are implemented.

[0039] The brake fault monitoring method, device, equipment, medium and product proposed by the present application are applied to an elevator brake and a cloud server. First, in response to a change in the braking state of the elevator brake, pressure information on the inner surface of the brake is collected; then, the pressure information is subjected to a first process to obtain electrical signal data, and the electrical signal data is transmitted to a gateway; finally, the gateway performs a second process on the electrical signal data to obtain data to be inspected, and the gateway sends the data to be inspected to the cloud server for the cloud server to perform a consistency check on the data to be inspected and preset standard data to obtain a monitoring result. This method integrates high-sensitivity sensor technology, intelligent signal processing algorithms and a cloud big data analysis platform to monitor the faults of elevator brakes, which can improve the real-time performance and accuracy of detecting elevator faults, greatly enhance the ability of fault prediction and maintenance, and effectively reduce the risk of safety accidents caused by brake failure. Description of the Drawings

[0040] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0041] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the brake fault monitoring method of the present application;

[0043] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the brake fault monitoring method of the present application;

[0044] Figure 3 It is a schematic flowchart of the brake fault monitoring method provided for Embodiment 1 and Embodiment 2 of the present application;

[0045] Figure 4 It is a schematic module structure diagram of the brake fault monitoring device according to the embodiment of the present application;

[0046] Figure 5This is a schematic diagram of the device structure of the hardware operating environment involved in the brake fault monitoring method in the embodiments of the present application.

[0047] The implementation, functional features, and advantages of the present application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. Detailed implementation manners

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] For a better understanding of the technical solutions of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0050] The main solution of the embodiments of the present application is:

[0051] In this embodiment, for the sake of easy expression, the elevator safety system is used as the execution entity for the following elaboration.

[0052] Currently, the fault monitoring of elevator brakes mainly relies on regular manual inspections or offline detections of traditional mechanical sensors (such as travel switches and displacement sensors), which have some technical defects. For example, key pressure parameters cannot be monitored in real time, the single data dimension leads to the risk of misjudgment, manual inspections rely on experience and are inefficient, etc.

[0053] The present application provides a solution. By immediately starting the pressure information collection when the braking state changes, any abnormal situation that may occur during the braking process can be captured, thereby improving the timeliness of fault detection; by performing the first processing on the pressure information, the quality of the original pressure information is significantly improved, the influence of interference signals is reduced, and subsequent analysis is more reliable; by performing the second processing on the electrical signal data through the gateway, valuable information can be further refined, providing a solid foundation for the final consistency check; by uploading the data to be inspected to the cloud server and comparing it with the preset standard data, using big data analysis technology and machine learning models, accurate judgment of the state of the elevator brake can be achieved, which can not only quickly discover the existing problems, but also predict possible future faults and provide preventive maintenance suggestions.

[0054] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of implementing the above functions. The following takes a personal computer as an example to illustrate this embodiment and the following embodiments.

[0055] Based on this, the embodiments of the present application provide a brake fault monitoring method, referring to Figure 1 , Figure 1Schematic flow diagram of the first embodiment of the brake fault monitoring method of the present application.

[0056] In this embodiment, the brake fault monitoring method includes steps S10 to S30:

[0057] Step S10, in response to a change in the braking state of the elevator brake, collect the pressure information on the inner surface of the brake shoe.

[0058] It should be noted that this embodiment is applied to an elevator brake. The elevator brake is mainly used to fix the position of the car when the elevator stops running or to quickly decelerate and stop the movement of the car in an emergency. It is usually directly connected to the elevator drive motor and can quickly respond when an instruction is issued by the elevator control system to ensure the safe operation of the elevator. The working principle of the elevator brake is based on friction, and sufficient braking force is applied to prevent the elevator car from moving.

[0059] The elevator brake at least includes a brake shoe. The brake shoe is the core execution component of the elevator brake. Its main function is to tightly hold the brake wheel or brake disc after receiving the braking signal, thereby generating sufficient friction to stop the elevator.

[0060] In a feasible embodiment, step S10 may include steps S11 to S12:

[0061] Step S11, divide the inner surface of the brake shoe into grid regions, and determine each grid region.

[0062] It should be noted that in this embodiment, a pressure sensor is provided on the inner surface of the brake shoe for collecting the pressure information acting on the inner surface of the brake shoe.

[0063] Specifically, according to the actual size of the inner surface of the brake shoe, the contact surface is divided into multiple equal-sized rectangular or square grid regions. For example, if the inner surface of the brake shoe is rectangular, it can be divided into N×M grids (such as 10×10 grids, a total of 100 regions) along the length and width directions.

[0064] Then, assign a unique identifier (such as a number or coordinates) to each grid region, and correspond it to the pre-installed pressure sensor one by one. For example, grid region G i,j corresponds to pressure sensor S i,j .

[0065] By dividing the contact surface of the brake shoe into multiple discrete sub-regions, the pressure distribution characteristics of each region can be accurately monitored, so as to locate local anomalies. For example, accurately identify which region of the inner surface of the brake shoe is more severely worn, and achieve precise fault detection.

[0066] Step S12: Detect the pressure of each grid area through the pressure sensor to obtain the pressure information.

[0067] Specifically, install a pressure sensor at the center of each grid area to directly collect the contact pressure in this area. Additionally, a flexible pressure sensing array can be laid within the grid area to achieve continuous monitoring of the pressure distribution within the area.

[0068] Set the sampling rate (such as 100Hz) according to the frequency of the elevator braking state change to ensure capturing transient pressure fluctuations. When the pressure sensor detects that the pressure fluctuation in this grid area reaches the preset sampling rate, the pressure information is collected.

[0069] Through the above steps, through grid-based pressure detection, local abnormal pressure information that cannot be recognized by traditional integral sensors can be captured.

[0070] Step S20: Perform a first processing on the pressure information to obtain electrical signal data, and transmit the electrical signal data to the gateway;

[0071] It should be noted that since the original pressure signal is vulnerable to external factors such as environmental noise interference, sensor nonlinear error, and mechanical vibration, the directly output original data may have problems such as noise pollution, abnormal fluctuations, or nonlinear distortion. Therefore, converting the original pressure information into structured and highly reliable electrical signal data lays a reliable foundation for subsequent cloud consistency verification, fault mode identification, and predictive maintenance.

[0072] In a feasible embodiment, step S20 may include steps S21 - S23:

[0073] Step S21: Convert the pressure information into an initial electrical signal;

[0074] Specifically, when pressure acts on a piezoresistive sensor, it will cause a change in the strain gauge, break the balance of the Wheatstone bridge, and output a voltage signal proportional to the pressure.

[0075]

[0076] where V exc is the excitation voltage, is the resistance change rate.

[0077] Alternatively, when pressure acts on a capacitive sensor, the pressure causes a change in the distance or area between the capacitor plates, and thus the capacitance change is converted into an initial electrical signal through a capacitance-voltage conversion circuit (such as a charge amplifier or a dedicated ASIC chip).

[0078] Step S22, filtering the initial electrical signal for noise through a filter, and performing an outlier inspection and removal on the filtered initial electrical signal to obtain a processed initial electrical signal;

[0079] Specifically, by combining the Kalman filter strategy, the pressure is dynamically detected (such as the sudden change in pressure when the elevator starts and stops). And a prediction model based on the pressure change rate is constructed using historical data. For example, if the pressure change trend over time during the elevator braking process is known, the following prediction equation can be established:

[0080] P k|k-1 =F k P k-1 +B k u k

[0081] Among them, P k|k-1 is the pressure value predicted at the current moment based on the state at the previous moment, F k is the state transfer matrix, B k is the control input model, u k is the control variable.

[0082] The noise covariance matrix and the observation noise covariance matrix in the Kalman filter determine the trust level of the filter. During the elevator braking process, due to the drastic pressure changes, the noise covariance matrix and the observation noise covariance matrix can be dynamically adjusted by real-time monitoring of the pressure change rate to optimize the filtering effect. For example, when the pressure change rate is detected to increase, the noise covariance matrix is appropriately increased to allow more process noise to enter the model, thereby better tracking the rapidly changing pressure.

[0083] After completing the Kalman filter, check whether the initial electrical signal after filtering meets the preset normal working range. If it does not meet the preset normal working range, the initial electrical signal is considered to be an abnormal value and is removed. Usually, this range can be determined based on the characteristics of the sensor (such as range, accuracy) and historical data analysis.

[0084] Step S23, performing standardization conversion on the processed initial electrical signal to obtain the electrical signal data.

[0085] The processed initial electrical signal is converted into an industrial standard signal form to obtain electrical signal data that meets the standard.

[0086] After the final electrical signal data is obtained, it is transmitted to the gateway for further processing by the gateway.

[0087] Through the above steps, not only the noise and outliers in the original electrical signal are effectively eliminated, but also the data quality and consistency in the subsequent processing stage are ensured, greatly improving the reliability and accuracy of fault detection.

[0088] Step S30: Perform a second processing on the electrical signal data through the gateway to obtain the data to be inspected, and send the data to be inspected to the cloud server through the gateway for the cloud server to perform a consistency check on the data to be inspected with the preset standard data to obtain a monitoring result.

[0089] It should be noted that in order to effectively focus on the most critical fault information in the electrical signal data, therefore, performing step S30 can ensure that only the data that truly reflects the health status of the elevator brake will be uploaded to the cloud for the final consistency check.

[0090] In a feasible embodiment, step S30 may include steps S31 to S32:

[0091] Step S31: Determine whether the electrical signal data is less than a preset threshold, and count the number of times the electrical signal data is less than the preset threshold;

[0092] Specifically, for each piece of electrical signal data from the pressure sensor, first compare it with the preset threshold set in advance through the gateway. If the piece of electrical signal data is less than the preset threshold, record a count. This process continues until a certain time window (such as within one minute) or a specific number of data samples are processed.

[0093] This step aims to screen out the data points that may indicate potential problems. For example, when the pressure of the brake is lower than the first preset threshold, it may mean that there is wear or other faults in the brake.

[0094] Step S32: When the number of times the electrical signal data is less than the preset threshold reaches the preset number threshold, use the electrical signal data less than the preset threshold as the data to be inspected.

[0095] Specifically, once it is detected that the number of times the electrical signal data is less than the preset threshold reaches the preset number threshold (for example, 5 times in a row), then mark these data as "data to be inspected". Subsequently, these data will be packaged and sent to the cloud server through the gateway for consistency verification, and based on the verification result, it is determined whether there are potential faults in the elevator.

[0096] Through the above steps, select the data showing potential problems for transmission, reducing unnecessary network traffic and cloud processing load. And by setting reasonable thresholds and number limits, the problem areas that really need attention can be more accurately identified, improving the overall system response speed and accuracy.

[0097] Through the method of the above embodiments, in response to a change in the braking state of the elevator brake, pressure information on the inner surface of the brake shoe is collected; then, the pressure information is subjected to a first process to obtain electrical signal data, and the electrical signal data is transmitted to the gateway; finally, the gateway performs a second process on the electrical signal data to obtain data to be inspected, and the gateway sends the data to be inspected to the cloud server for the cloud server to perform a consistency check on the data to be inspected and the preset standard data to obtain a monitoring result. This method integrates high-sensitivity sensor technology, intelligent signal processing algorithms, and a cloud big data analysis platform to monitor faults of the elevator brake, which can improve the real-time performance and accuracy of detecting elevator faults and greatly enhance the fault prediction and maintenance ability.

[0098] Based on this, an embodiment of the present application further provides a method for monitoring brake faults, referring to Figure 2 , Figure 2 which is a schematic flowchart of the second embodiment of the method for monitoring brake faults of the present application.

[0099] In this embodiment, the method for monitoring brake faults includes steps S40 to S50:

[0100] Step S40, receiving the data to be inspected sent by the elevator brake through the gateway, where the data to be inspected is obtained by the elevator brake in response to a change in the braking state of the elevator brake, collecting pressure information on the inner surface of the brake shoe of the elevator brake, performing a first process on the pressure information to obtain electrical signal data, transmitting the electrical signal data to the gateway, and performing a second process on the electrical signal data through the gateway;

[0101] It should be noted that this embodiment is applied to the cloud server, which is used to receive the data to be inspected sent by the elevator brake through the gateway and further analyze the data to be inspected, where the data to be inspected is obtained by the elevator brake in response to a change in the braking state of the elevator brake, collecting pressure information on the inner surface of the brake shoe of the elevator brake, then performing a first process on the pressure information to obtain electrical signal data, subsequently transmitting the electrical signal data to the gateway, and then performing a second process on the electrical signal data through the gateway.

[0102] Step S50, performing a consistency check on the data to be inspected and the preset standard data to obtain a detection result.

[0103] It should be noted that the data to be tested includes corresponding sequence values to be tested, and the preset standard data includes corresponding preset sequence values. The sequence value refers to the electrical signal data converted from the pressure information collected at fixed or variable time intervals within a specific time period. Each sequence value represents the pressure reading at a certain moment (or the average value within a small time period). These data points are arranged in the collection order to form a time series.

[0104] In a feasible embodiment, step S50 may include steps S51 to S55:

[0105] Step S51, identifying the data types and structural characteristics of the preset sequence value and the sequence value to be tested;

[0106] To confirm the original data types of the preset sequence value and the sequence value to be tested, this process can be completed by querying the database. This step aims to clarify whether the data is of basic types such as numeric, string, or object. For example, pressure values are usually numeric, while timestamps may exist in string or numeric formats.

[0107] Next, according to the database structure classification, evaluate whether the preset sequence value and the sequence value to be tested have structural characteristics. This means it is necessary to determine whether these data follow a relational structure (such as a table form with fixed columns and rows), or are presented in an unstructured or semi-structured format (such as key-value pairs or free text in JSON format).

[0108] Once it is determined that the data has structural characteristics, further extract the specific structural characteristics. This process includes:

[0109] Determine the sequence dimension: Analyze whether the sequence is a univariate time series (such as only containing a series of pressure readings) or a multivariate data set (for example, data that simultaneously records multiple variables such as pressure values and temperatures).

[0110] Calculate the number of sampling points: Count the specific number of measurement points or records in the sequence.

[0111] Record the timestamp information: For time series, special attention should be paid to recording the timestamp interval of each sample (such as collecting data once per second) and the time range covered by the entire sequence.

[0112] Check the dimensional consistency: Ensure that the data used for comparison is physically comparable. For example, confirm that the pressure value, as a ratio data, supports ratio calculations to ensure the effectiveness of subsequent analysis.

[0113] Step S52, calculating the consistency index of the preset sequence value and the sequence value to be tested according to the data type and structural characteristics;

[0114] Specifically, first, select an appropriate similarity calculation method. For example, use the Euclidean distance formula:

[0115]

[0116] where x i is the i-th sequence value of the preset standard data, and y i is the i-th sequence value of the data to be tested.

[0117] Alternatively, use the dynamic time warping algorithm:

[0118] D(i, j) = d(x i , y j ) + min(D(i - 1, j), D(i, j - 1), D(i - 1, j - 1))

[0119] where x i is the i-th sequence value of the preset standard data, y i is the i-th sequence value of the data to be tested, and D(i, j) represents the cumulative distance between the two sequences at positions i and j.

[0120] Or use the Pearson correlation coefficient to measure the correlation between two sets of data. The value range is [-1, 1], and the closer the value is to 1, the higher the similarity.

[0121] Step S53, compare the consistency index with the preset index threshold range;

[0122] It should be noted that the preset index threshold range can set an absolute threshold according to the physical characteristics of the elevator brake. For example: trigger an alarm when the pressure difference exceeds 5%, or consider it abnormal when the correlation coefficient is lower than 0.8.

[0123] Compare the consistency index calculated in step S52 with the preset index threshold. For example, set the threshold range of distance - type indicators (such as Euclidean distance, DTW) to [0, T_max]. If the calculated consistency index exceeds this range, the monitoring result is abnormal. Set the threshold range of correlation - coefficient - type indicators to [0.8, 1.0]. If the calculated consistency index is lower than the lower limit, the monitoring result is abnormal.

[0124] Step S54, if the consistency index is within the preset index threshold range, determine that the monitoring result is normal;

[0125] If the consistency index falls within the preset threshold range, mark the detection result as "normal" and update the normal data set.

[0126] Step S55: If the consistency index is not within the preset index threshold range, determine that the monitoring result is abnormal.

[0127] If the consistency index exceeds the preset threshold range, determine that the monitoring result is abnormal, and further determine that there is a potential fault in the elevator.

[0128] After determining that there is a potential fault in the elevator, the cloud server will generate a detailed detection report, including the time of the fault occurrence, the specific location (such as the specific area on the inner surface of the brake area), and the type of the fault (such as insufficient pressure, excessive fluctuation, etc.).

[0129] At the same time, the system can notify the maintenance personnel through text messages, emails or other instant messaging tools and provide preliminary diagnostic suggestions to take timely measures to avoid accidents.

[0130] Through the method of the above embodiment, receive the data to be inspected sent by the elevator brake through the gateway by the cloud server; and perform consistency inspection on the data to be inspected and the preset standard data to obtain the detection result, so as to effectively monitor the working state of the elevator brake, and can also give early warning of potential faults, thereby greatly improving the safety and reliability of the elevator.

[0131] Exemplarily, to help understand the implementation process of the brake fault monitoring method obtained by combining the above embodiment one, please refer to Figure 3 , Figure 3 A brief flow schematic diagram of a brake fault monitoring method is provided. Specifically:

[0132] This embodiment is applied to an elevator brake and a cloud server, where the elevator brake further includes a brake.

[0133] First, divide the inner surface of the brake into grid areas, determine each grid area, and monitor the force conditions of each area of the brake in real time through a pressure sensor to obtain corresponding pressure information. The pressure sensor is installed on the inner surface layer of the brake.

[0134] Further, filter the noise of the pressure information, monitor and remove outliers, and convert the preprocessed pressure information into electrical signal data that can be processed by an electronic device. Ensure the quality of the transmitted data, reduce unnecessary data volume, and improve the transmission efficiency and accuracy.

[0135] Further, perform further analysis and processing on the electrical signal data processed by the gateway. For example, determine whether the electrical signal data is less than a first preset threshold, and count the number of times the electrical signal data is less than the first preset threshold. When the number of times the electrical signal data is less than the first preset threshold reaches the preset number threshold, use the electrical signal data less than the first preset threshold as the data to be inspected.

[0136] Further, the data to be tested is sent to the cloud server through the gateway for fault prediction.

[0137] Further, once the cloud server receives the data to be tested, it calculates the difference value between the sequence value of the data to be tested and the sequence value of the preset standard data. If the difference value between the sequence value of the data to be tested and the sequence value of the preset standard data exceeds the second preset threshold, it is confirmed that the elevator has potential faults, and corresponding fault reports and maintenance suggestions are output.

[0138] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the brake fault monitoring method of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0139] The present application also provides a brake fault monitoring device. Please refer to Figure 4 The brake fault monitoring device includes an elevator brake and a cloud server;

[0140] The elevator brake includes:

[0141] An acquisition module 10, configured to acquire pressure information on the inner surface of the brake shoe in response to a change in the braking state of the elevator brake;

[0142] A first processing module 20, configured to perform a first processing on the pressure information to obtain electrical signal data, and transmit the electrical signal data to the gateway;

[0143] A second processing module 30, configured to perform a second processing on the electrical signal data through the gateway to obtain data to be tested, and send the data to be tested to the cloud server through the gateway for the cloud server to perform a consistency test on the data to be tested and preset standard data to obtain a monitoring result;

[0144] The cloud server includes:

[0145] A receiving module 40, configured to receive the data to be tested sent by the elevator brake through the gateway, where the data to be tested is obtained by the elevator brake in response to a change in the braking state of the elevator brake, acquiring pressure information on the inner surface of the brake shoe of the elevator brake, performing a first processing on the pressure information to obtain electrical signal data, transmitting the electrical signal data to the gateway, and performing a second processing on the electrical signal data through the gateway;

[0146] An inspection module 50, configured to perform a consistency test on the data to be tested and preset standard data to obtain a detection result.

[0147] The brake failure monitoring device provided by this application adopts the brake failure monitoring method in the above-mentioned embodiment, which can solve the technical problems of how to improve the real-time performance and accuracy of elevator failure monitoring and improve the fault prediction and maintenance ability. Compared with the prior art, the beneficial effects of the brake failure monitoring device provided by this application are the same as those of the brake failure monitoring method provided by the above-mentioned embodiment, and the other technical features in the brake failure monitoring device are the same as those disclosed in the method of the above-mentioned embodiment, which will not be elaborated here.

[0148] This application provides a brake failure monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the brake failure monitoring method in the first embodiment above.

[0149] Refer to the following Figure 5 , which shows a schematic structural diagram of a brake failure monitoring device suitable for implementing the embodiments of this application. The brake failure monitoring device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The brake failure monitoring device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0150] As shown in Figure 5As shown, the brake failure monitoring device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the brake failure monitoring device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the brake failure monitoring device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a brake failure monitoring device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be alternatively implemented or had.

[0151] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0152] The brake failure monitoring device provided by the present application adopts the brake failure monitoring method in the above embodiments, and can solve the technical problems of how to improve the real-time performance and accuracy of monitoring elevator failures and improve the fault prediction and maintenance ability. Compared with the prior art, the beneficial effects of the brake failure monitoring device provided by the present application are the same as those of the brake failure monitoring method provided by the above embodiments, and other technical features in the brake failure monitoring device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0153] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0154] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0155] This application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the brake failure monitoring method in the above embodiments.

[0156] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM: Random Access Memory), read-only memory (ROM: Read Only Memory), erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0157] The above computer-readable storage medium can be included in the brake failure monitoring device; it can also exist separately without being assembled into the brake failure monitoring device.

[0158] The above computer-readable storage medium carries one or more programs, which, when executed by a brake fault monitoring device, cause the brake fault monitoring device to: in response to a change in the braking state of an elevator brake, collect pressure information on the inner surface of the brake shoe; then, perform a first processing on the pressure information to obtain electrical signal data, and transmit the electrical signal data to a gateway; finally, perform a second processing on the electrical signal data through the gateway to obtain data to be inspected, and send the data to be inspected to a cloud server through the gateway for the cloud server to perform a consistency check on the data to be inspected and the preset standard data to obtain a monitoring result.

[0159] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, execute as a stand-alone software package, execute partially on the user's computer and partially on a remote computer, or execute entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0161] The modules involved in the embodiments of the present application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0162] The readable storage medium provided by the present application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned brake fault monitoring method, which can solve the technical problems of how to improve the real-time performance and accuracy of elevator fault monitoring and improve the fault prediction and maintenance ability. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the brake fault monitoring method provided by the above embodiments, and will not be elaborated here.

[0163] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the brake fault monitoring method as described above are implemented.

[0164] The computer program product provided by the present application can solve the technical problems of how to improve the real-time performance and accuracy of elevator fault monitoring and improve the fault prediction and maintenance ability. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the brake fault monitoring method provided by the above embodiments, and will not be elaborated here.

[0165] The above are only some embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A brake failure monitoring method, characterized in that, The brake fault monitoring method is applied to an elevator brake, and the elevator brake includes a brake shoe. The method includes: In response to a change in the braking state of the elevator brake, collecting pressure information on the inner surface of the brake shoe; Performing a first processing on the pressure information to obtain electrical signal data, and transmitting the electrical signal data to a gateway; Performing a second processing on the electrical signal data through the gateway to obtain data to be tested, and sending the data to be tested to a cloud server through the gateway for the cloud server to perform a consistency test between the data to be tested and preset standard data to obtain a monitoring result.

2. The brake fault monitoring method according to claim 1, wherein, A pressure sensor is provided on the inner surface of the brake shoe. The step of collecting pressure information on the inner surface of the brake shoe includes: Dividing the inner surface of the brake shoe into grid regions to determine each grid region; Detecting the pressure of each grid region through the pressure sensor to obtain the pressure information.

3. The brake fault monitoring method according to claim 2, wherein the step of performing a first processing on the pressure information to obtain electrical signal data includes: Converting the pressure information into an initial electrical signal; Filtering out noise from the initial electrical signal through a filter, and performing an outlier test and removal on the filtered initial electrical signal to obtain a processed initial electrical signal; Performing a normalization conversion on the processed initial electrical signal to obtain the electrical signal data.

4. The brake failure monitoring method according to claim 3, characterized in that, The step of performing a second processing on the electrical signal data through the gateway to obtain data to be tested includes: Judging whether the electrical signal data is less than a preset threshold value, and counting the number of times the electrical signal data is less than the preset threshold value; When the number of times the electrical signal data is less than the preset threshold value reaches a preset number threshold, using the electrical signal data less than the preset threshold value as the data to be tested.

5. A brake fault monitoring method, characterized in that, The brake fault monitoring method is applied to a cloud server. The method includes: Receiving data to be tested sent by an elevator brake through a gateway, where the data to be tested is obtained by the elevator brake in response to a change in the braking state of the elevator brake, collecting pressure information on the inner surface of the brake shoe of the elevator brake, performing a first processing on the pressure information to obtain electrical signal data, transmitting the electrical signal data to the gateway, and performing a second processing on the electrical signal data through the gateway; Performing a consistency test between the data to be tested and preset standard data to obtain a detection result.

6. The elevator fault detection method according to claim 5, characterized in that The preset standard data includes a preset sequence value, and the data to be tested includes a data sequence value to be tested. The step of performing a consistency test between the data to be tested and the preset standard data to obtain a monitoring result includes: Identifying the data type and structural characteristics of the preset sequence value and the data sequence value to be tested; Calculating a consistency index between the preset sequence value and the data sequence value to be tested according to the data type and structural characteristics; Comparing the consistency index with a preset index threshold range; If the consistency index is within the preset index threshold range, determining that the monitoring result is normal; If the consistency index is not within the preset index threshold range, it is determined that the monitoring result is abnormal.

7. A brake failure monitoring device, characterized in that, The brake failure monitoring device includes an elevator brake and a cloud server; The elevator brake includes: A collection module, configured to collect pressure information on the inner surface of the brake shoe in response to a change in the braking state of the elevator brake; A first processing module, configured to perform a first processing on the pressure information to obtain electrical signal data, and transmit the electrical signal data to the gateway; A second processing module, configured to perform a second processing on the electrical signal data through the gateway to obtain data to be inspected, and send the data to be inspected to the cloud server through the gateway for the cloud server to perform a consistency inspection on the data to be inspected and the preset standard data to obtain a monitoring result; The cloud server includes: A receiving module, configured to receive the data to be inspected sent by the elevator brake through the gateway, where the data to be inspected is obtained by the elevator brake collecting pressure information on the inner surface of the brake shoe of the elevator brake in response to a change in the braking state of the elevator brake, performing a first processing on the pressure information to obtain electrical signal data, and transmitting the electrical signal data to the gateway, and performing a second processing on the electrical signal data through the gateway; An inspection module, configured to perform a consistency inspection on the data to be inspected and the preset standard data to obtain a detection result.

8. A brake failure monitoring device, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the brake failure monitoring method according to any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the brake failure monitoring method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, the steps of the brake failure monitoring method according to any one of claims 1 to 6 are implemented.

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