Elevator brake failure early warning device

Abstract

The invention provides an elevator brake failure early warning device, and relates to the technical field of elevators. According to the failure early warning device for the elevator brake, the temperature sensing assembly, the vibration sensing assembly, the torque sensing assembly, the air gap sensing assembly and the pressure sensing assembly are arranged through the sensor module, so that a plurality of key parameters such as the temperature of a brake shoe, a vibration signal, a brake torque, a brake air gap and hydraulic pressure or spring pressure can be collected in real time; the comprehensive monitoring of the working state of the elevator brake is realized; the data processing and analysis module applies a multi-dimensional data analysis technology, combines various failure judgment algorithms such as a simple algorithm based on threshold judgment, an intelligent algorithm based on pattern recognition and a prediction algorithm based on machine learning, and a data analysis model comprising a feature layer, a prediction layer and a decision layer; and the collected data can be deeply analyzed and processed, whether the brake has a failure risk or not is comprehensively judged, and the accuracy and reliability of early warning are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of elevator technology, and more specifically, to an elevator brake failure early warning device. Background Technology

[0002] In elevator operating systems, the brake is a key component that ensures elevator safety. Its performance stability and reliability are directly related to the life safety of passengers and the normal operation of elevator equipment. However, because elevator brakes are in a state of frequent start-stop and high load for a long time, coupled with the complex and changeable working environment, they are prone to various malfunctions and performance degradation problems, such as brake shoe wear, abnormal brake air gap, and insufficient hydraulic pressure. If these problems are not detected and dealt with in time, they may lead to brake failure, which in turn may cause serious safety accidents such as elevator slippage, overshooting, or bottoming out.

[0003] Currently, traditional elevator brake monitoring methods mainly rely on periodic manual inspections and simple mechanical protection devices. Manual inspections have several limitations. Firstly, the inspection cycle is long, making it difficult to monitor the brake's operating status in real time and detect potential faults promptly. Secondly, the inspection results are greatly affected by the inspectors' professional skills and experience, easily leading to missed inspections and misjudgments. Simple mechanical protection devices typically only function when a brake malfunctions, failing to provide early warnings and prevention of early brake failure risks, thus failing to effectively prevent accidents. Therefore, we propose an improvement: an elevator brake failure early warning device. Summary of the Invention

[0004] This invention provides an elevator brake failure early warning device, comprising:

[0005] Sensor module: Used to monitor the parameters of the elevator brake in real time;

[0006] Data processing and analysis module: Connected to the sensor module, it has a built-in failure determination algorithm and data analysis model. It is used to receive and process the data collected by the sensor module and comprehensively judge whether the brake has a failure risk through multi-dimensional data analysis technology.

[0007] Early warning execution module: connected to the data processing and analysis module, when the data processing and analysis module determines that there is a risk of brake failure, it immediately issues an early warning signal;

[0008] Communication module: It adopts both wired and wireless communication methods to transmit early warning signals to the elevator control system or remote monitoring center.

[0009] As a preferred technical solution of this application, the sensor module includes:

[0010] Temperature sensing components are installed on the surface of the brake shoes to collect the temperature of the brake shoes;

[0011] The vibration sensing component is installed on the brake housing and is used to collect vibration signals;

[0012] A torque sensing component is fitted onto the brake output shaft to collect braking torque.

[0013] An air gap sensing component is installed at the gap between the brake caliper and the brake disc to collect brake air gap data.

[0014] Pressure sensing components are installed in the brake hydraulic cylinder or spring chamber to collect hydraulic pressure or spring pressure.

[0015] As a preferred technical solution of this application, the failure determination algorithm includes a simple algorithm based on threshold judgment, an intelligent algorithm based on pattern recognition, and a prediction algorithm based on machine learning.

[0016] As a preferred technical solution of this application, the early warning execution module includes:

[0017] Local alarm unit: uses red LED indicator lights and a buzzer;

[0018] Linkage control interface: Outputs dry contact signals to the elevator main control system to trigger "deceleration and stop" or "emergency braking" commands.

[0019] As a preferred technical solution of this application, it also includes a self-diagnosis and self-calibration module, which is used to periodically perform fault detection and performance calibration on the device itself;

[0020] The self-diagnosis and self-calibration module is connected to the data processing and analysis module. When a fault or performance degradation is detected in the device itself, the early warning execution module is triggered to issue a fault warning signal.

[0021] As a preferred technical solution of this application, it also includes a data storage and backup module for storing historical data collected by the sensor module, processing results of the data processing and analysis module, and early warning logs;

[0022] The data storage and backup module is connected to the data processing and analysis module to achieve real-time data synchronization and regular backup.

[0023] As a preferred technical solution of this application, it also includes a user interface module for providing an operation interface.

[0024] As a preferred technical solution of this application, the data analysis model includes:

[0025] Feature layer: Energy entropy features of vibration signals are extracted by wavelet packet decomposition, and multi-dimensional feature vectors are constructed by combining temperature drift coefficient and pressure attenuation rate;

[0026] Prediction layer: LSTM neural network is used to perform time-series prediction on feature vectors and output the failure probability in the next N days;

[0027] Decision-making level: Based on Bayesian inference and the fusion of prediction results with expert rule base, a three-level early warning system of "low, medium and high" is generated.

[0028] As a preferred technical solution of this application, it also includes a power supply module, which adopts a dual power supply design, including a main power supply for the elevator control cabinet DC24V power input and a lithium battery backup power supply. When the main power supply is interrupted, it is switched to the lithium battery backup power supply.

[0029] As a preferred technical solution of this application, it also includes a brake life prediction module. The brake life prediction module is connected to the data storage and backup module and the data analysis model. Based on historical operating data and the basic life parameters provided by the brake manufacturer, the remaining service life of the brake is calculated using the Weibull model, and the prediction results are displayed through the user interface module.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] In the scheme of this application:

[0032] This elevator brake failure early warning device is equipped with temperature, vibration, torque, air gap, and pressure sensors. It can collect multiple key parameters in real time, including brake shoe temperature, vibration signal, braking torque, brake air gap, and hydraulic or spring pressure, enabling comprehensive monitoring of the elevator brake's operating status. The data processing and analysis module utilizes multi-dimensional data analysis technology, combining simple threshold-based algorithms, intelligent pattern recognition algorithms, and machine learning-based predictive algorithms for failure assessment. It also employs a data analysis model encompassing feature layers, prediction layers, and decision layers to perform in-depth analysis and processing of the collected data, comprehensively determining whether the brake is at risk of failure, thus significantly improving the accuracy and reliability of the early warning. Attached Figure Description

[0033] Figure 1 System diagram of the elevator brake failure early warning device provided in this application;

[0034] Figure 2 A schematic diagram of the sensor module provided in this application;

[0035] Figure 3 A flowchart illustrating the use of the elevator brake failure early warning device provided in this application. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0037] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other.

[0038] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0039] For an example, please refer to... Figure 1 An elevator brake failure early warning device includes:

[0040] Sensor module: Used to monitor elevator brake parameters in real time, including braking force, braking distance, temperature, vibration frequency, and acceleration. Through real-time monitoring of multiple parameters, it can comprehensively capture the brake's operating status, avoiding the limitations of monitoring a single parameter and providing comprehensive and accurate raw data for subsequent failure assessment. Data processing and analysis module: Connected to the sensor module, it has built-in failure assessment algorithms and data analysis models. It receives and processes the data collected by the sensor module and uses multi-dimensional data analysis technology to comprehensively determine whether the brake has a failure risk.

[0041] Early warning execution module: Connected to the data processing and analysis module, when the data processing and analysis module determines that there is a risk of brake failure, it immediately issues an early warning signal. The early warning signal forms include sound alarm, light alarm, and SMS / APP push. Different early warning forms are suitable for different scenarios and personnel. Sound and light alarms can promptly remind on-site personnel near the elevator, while SMS / APP push allows remote management personnel to keep track of the situation at any time, improving the effectiveness and coverage of the early warning.

[0042] Communication module: It adopts both wired (such as RS485, CAN bus) and wireless (such as Wi-Fi, 4G / 5G, LoRa) communication methods to transmit early warning signals to the elevator control system or remote monitoring center so that timely countermeasures can be taken. The wired communication method has stable transmission performance in complex environments such as elevator shafts, while the wireless communication method can realize flexible remote transmission. The dual communication methods back each other up to ensure uninterrupted information transmission.

[0043] Furthermore, such as Figure 2As shown, the sensor module includes:

[0044] The temperature sensing component uses a PT100 platinum resistance sensor, which is installed on the surface of the brake shoe to collect the temperature of the brake shoe. The measurement range is -50℃ to 300℃, and the accuracy is ±0.5℃.

[0045] The vibration sensing component uses a MEMS accelerometer sensor, which is installed in the brake housing to collect vibration signals. The sampling frequency is 100Hz~1kHz and the range is ±16g.

[0046] The torque sensing component uses a non-contact torque sensor, which is fitted onto the brake output shaft to collect braking torque. The measurement range is 0 to 500 N·m, and the accuracy is ±0.5%FS.

[0047] The air gap sensing component uses a laser displacement sensor, which is installed at the gap between the brake caliper and the brake disc to collect the brake air gap. The measurement range is 0-10mm and the resolution is 0.01mm.

[0048] The pressure sensing component uses a piezoelectric pressure sensor, which is installed in the brake hydraulic cylinder or spring chamber to collect hydraulic pressure or spring pressure. The measurement range is 0 to 30 MPa, and the accuracy is ±1% FS.

[0049] Furthermore, the failure determination algorithm includes a simple algorithm based on threshold judgment, an intelligent algorithm based on pattern recognition, and a prediction algorithm based on machine learning. It can dynamically adjust the judgment threshold according to different working conditions and historical data, thereby improving the accuracy and reliability of the early warning.

[0050] Furthermore, the early warning execution module includes:

[0051] Local alarm unit: uses red LED indicator and buzzer (sound pressure level ≥85dB);

[0052] Linkage control interface: Outputs dry contact signals to the elevator main control system to trigger "deceleration and stop" or "emergency braking" commands; local alarms can promptly remind people near the elevator to pay attention to risks, and a loud buzzer and a conspicuous red LED indicator ensure that they can be detected in various environments; the linkage control interface can directly interact with the elevator main control system.

[0053] Furthermore, it also includes a self-diagnosis and self-calibration module, which is used to periodically detect faults and calibrate the performance of the device itself to ensure the stability and accuracy of the device's long-term operation;

[0054] The self-diagnosis and self-calibration module is connected to the data processing and analysis module. When a fault or performance degradation is detected in the device itself, the early warning execution module is triggered to issue a fault warning signal. The self-diagnosis and self-calibration module performs a self-test every 24 hours, and determines the drift by comparing the sensor reference value (such as standard resistance or pressure source). When the error exceeds ±2%FS, calibration is automatically triggered. If calibration fails, the early warning execution module immediately issues a "device fault" signal.

[0055] Furthermore, it also includes a data storage and backup module, used to store historical data collected by the sensor module, processing results from the data processing and analysis module, and early warning logs;

[0056] The data storage and backup module is connected to the data processing and analysis module to achieve real-time data synchronization and regular backup. The storage of historical data provides a basis for subsequent data analysis, fault tracing and life prediction. Real-time synchronization and regular backup ensure the security and integrity of the data and prevent data loss.

[0057] Furthermore, it also includes a user interface module, which provides an operating interface so that users can view the brake status, warning information, historical data, and configure parameters.

[0058] Furthermore, the data analysis model includes:

[0059] Feature layer: Energy entropy features of vibration signals are extracted through wavelet packet decomposition, and multi-dimensional feature vectors are constructed by combining temperature drift coefficient and pressure attenuation rate; the feature layer can effectively extract key features and provide high-quality input for subsequent prediction;

[0060] Prediction layer: LSTM neural network is used to perform time-series prediction on feature vectors and output the failure probability in the next N days, such as the failure probability in 30 days, to detect potential risks in advance;

[0061] Decision-making level: Based on Bayesian inference and the fusion of prediction results with expert rule base, a three-level warning level of "low, medium and high" is generated. The decision-making level generates a clear warning level by fusion of prediction results and expert experience, so that users can take corresponding countermeasures.

[0062] Furthermore, it also includes a power supply module, which adopts a dual-power supply design, including a main power supply for the elevator control cabinet DC24V power input and a lithium battery backup power supply (capacity ≥10Ah, runtime ≥4 hours). When the main power supply is interrupted, it switches to the lithium battery backup power supply. The elevator operating environment is complex, and the main power supply may be interrupted unexpectedly. The backup power supply can play a role in this situation to ensure the safe operation of the elevator.

[0063] Furthermore, it also includes a brake life prediction module, which is connected to the data storage and backup module and the data analysis model. Based on historical operating data (such as cumulative braking times, average braking load, and number of extreme operating condition experiences) and basic life parameters provided by the brake manufacturer, it uses the Weibull model to calculate the remaining service life of the brake and displays the prediction results through the user interface module. The basic life parameters include rated braking times (such as 1 million times), average braking load (such as 200 N·m), and maximum allowable temperature (300℃). The Weibull model parameters are obtained by fitting the life test data (such as 10 sets of failure samples) provided by the manufacturer, with shape parameter m = 2.5 and scale parameter η = 800 days.

[0064] like Figure 3 As shown, during use, according to the installation requirements of each component of the sensor module, the temperature sensing component is installed on the surface of the brake shoe, the vibration sensing component is installed on the brake housing, the torque sensing component is sleeved on the brake output shaft, the air gap sensing component is installed in the gap between the brake caliper and the brake disc, and the pressure sensing component is installed in the brake hydraulic cylinder or spring cavity; connect the power supply module to ensure that the main power supply and the backup lithium battery are correctly connected.

[0065] Connect the wired interface (RS485, CAN bus) of the communication module to the elevator control system, and configure the wireless communication (Wi-Fi, 4G / 5G, LoRa) parameters to connect to the remote monitoring center;

[0066] Install the local alarm unit (red LED indicator and buzzer) with the early warning execution module in a conspicuous location in the elevator machine room or monitoring room;

[0067] The sensor module collects various parameters of the elevator brake in real time at a set frequency. The collected data is transmitted to the data processing and analysis module through the internal bus. After receiving the sensor data, the data processing and analysis module extracts the energy entropy features of the vibration signal through the feature layer, and constructs a multi-dimensional feature vector by combining the temperature drift coefficient and pressure attenuation rate. The prediction layer uses an LSTM neural network to perform time-series prediction on the feature vector and outputs the failure probability in the next 30 days. The decision layer generates a three-level early warning judgment of "low, medium and high" based on Bayesian inference, fusion prediction results and expert rule base.

[0068] The user interface displays the current values, historical change curves, and equipment operating status of various brake parameters in real time. The brake life prediction module calculates and displays the remaining service life based on historical operating data and basic life parameters provided by the manufacturer.

[0069] When the data processing and analysis module determines that there is a risk of brake failure, it immediately triggers the early warning execution module;

[0070] Low-level warning: The red LED indicator flashes, the buzzer sounds intermittently (sound pressure level ≥ 85dB), and a warning SMS / APP push is sent to the designated management personnel at the same time;

[0071] Medium-level warning: The red LED indicator light stays on, the buzzer sounds continuously, a dry contact signal is output to the elevator control system, triggering the "decelerate and stop" command, and a warning message is sent to the management center and management personnel;

[0072] High-level warning: Activate all alarm modes, output dry contact signals to the elevator control system, trigger the "emergency braking" command, and send emergency warning information to the management center, management personnel and maintenance unit at the same time;

[0073] Upon receiving an early warning signal, managers should view detailed warning information and related data through the user interface.

[0074] Low-level warning: Arrange for maintenance personnel to conduct inspections and maintenance in the near future;

[0075] Medium-level warning: Immediately dispatch maintenance personnel to the site for inspection, and decide whether to suspend elevator use based on the situation;

[0076] High-level warning: The elevator has been brought to an emergency stop. Maintenance personnel must arrive immediately to investigate the fault and eliminate the risk before the elevator can be put back into use.

[0077] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0078] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. An elevator brake failure early warning device, characterized by, The application relates to an elevator brake failure prediction device, which comprises the following modules: a sensor module for real-time monitoring of parameters of an elevator brake; a data processing and analysis module connected with the sensor module, which is internally provided with a failure judgment algorithm and a data analysis model, is used for receiving and processing data collected by the sensor module, and comprehensively judges whether the brake has a failure risk through multidimensional data analysis technology; a pre-warning execution module connected with the data processing and analysis module, which immediately sends a pre-warning signal when the data processing and analysis module judges that the brake has a failure risk; a communication module which adopts double communication modes of wire and wireless, and is used for transmitting the pre-warning signal to an elevator control system or a remote monitoring center.

2. The elevator brake failure warning device according to claim 1, characterized in that The sensor module comprises the following components: a temperature sensing assembly installed on the surface of a brake shoe block and used for collecting the temperature of the brake shoe block; a vibration sensing assembly installed on the shell of the brake and used for collecting vibration signals; a torque sensing assembly sleeved on the output shaft of the brake and used for collecting brake torque; an air gap sensing assembly installed at the gap between a brake caliper and a brake disc and used for collecting brake air gap; a pressure sensing assembly installed on a brake hydraulic cylinder or a spring cavity and used for collecting hydraulic pressure or spring pressure.

3. The elevator brake failure warning device according to claim 1, characterized in that, The failure judgment algorithm comprises a simple algorithm based on threshold judgment, an intelligent algorithm based on pattern recognition and a prediction algorithm based on machine learning.

4. The elevator brake failure warning device according to claim 1, characterized in that, The pre-warning execution module comprises the following components: a local alarm unit adopting a red LED indicator and a buzzer; a linkage control interface which outputs a dry contact point signal to an elevator main control system and triggers a "reduced speed landing" or "emergency braking" instruction.

5. The elevator brake failure warning device according to claim 1, characterized in that, The device further comprises a self-diagnosis and self-calibration module which is used for periodically detecting faults and calibrating performance of the device itself; the self-diagnosis and self-calibration module is connected with the data processing and analysis module, and triggers the pre-warning execution module to send a fault pre-warning signal when it is detected that the device itself has a fault or performance decline.

6. The elevator brake failure warning device according to claim 1, characterized in that, The device further comprises a data storage and backup module which is used for storing historical data collected by the sensor module, processing results of the data processing and analysis module and pre-warning logs; the data storage and backup module is connected with the data processing and analysis module, and realizes real-time synchronization and periodic backup of data.

7. The elevator brake failure warning device according to claim 6, characterized in that The device further comprises a user interactive interface module which is used for providing an operation interface.

8. The elevator brake failure warning device of claim 1, wherein, The data analysis model comprises the following components: a feature layer which extracts energy entropy features of vibration signals through wavelet packet decomposition, combines temperature drift coefficients and pressure attenuation rates to construct a multidimensional feature vector; a prediction layer which adopts an LSTM neural network to perform time series prediction on the feature vector and outputs failure probabilities in the next N days; a decision layer which fuses prediction results and an expert rule base based on Bayesian inference to generate "low, medium and high" three-level pre-warning grades.

9. The elevator brake failure warning device according to claim 1, characterized in that, The device further comprises a power supply module which adopts a double power supply design, comprises main power supply of a DC 24V power supply input of an elevator control cabinet and a lithium battery backup power supply, and switches to the lithium battery backup power supply when the main power supply is interrupted.

10. The elevator brake failure warning device according to claim 7, characterized in that, The device further comprises a brake service life prediction module which is connected with the data storage and backup module and the data analysis model, adopts a Weibull model to calculate the remaining service life of the brake based on historical operation data and basic life parameters provided by a brake manufacturer, and displays the prediction results through the user interactive interface module.

Images