Fault prediction method and system based on elevator parts

Abstract

The invention is suitable for the technical field of intelligent monitoring, and provides a fault prediction method and system based on elevator parts, and the method comprises the steps: obtaining the real-time operation data set information of a plurality of elevator parts based on a plurality of sensors, and then generating operation evaluation information based on the real-time operation data set information, and finally, sending the operation evaluation information to a specified display terminal. According to the method and the device, an owner party can carry out work scheduling of shutdown maintenance on the elevator to be maintained in advance, the occurrence probability of sudden failures is avoided, the use safety of the elevator is improved, an elevator maintenance company can purchase and prepare targeted key parts, the inventory number of the key parts is effectively reduced, and the maintenance cost is reduced. According to the method and the system, key parts of corresponding brands and models can be more accurately provided for a user for maintenance, meanwhile, an owner can follow real-time data of a mechanical vibration analysis curve for comparison, the operation state of the key parts can be clearly mastered in real time, and maintenance work is effectively supervised.

Description

Technical Field

[0001] This application relates to the technical field of intelligent monitoring, and more specifically, to a fault prediction method and system based on elevator components. Background Technology

[0002] Elevator maintenance is usually carried out in batches on a regular basis, with each key component being inspected and maintained. However, this maintenance method may lead to over-maintenance for elevators with low usage frequency, wasting unnecessary manpower and resources. On the other hand, for elevators with very frequent use, the maintenance interval is relatively long, which increases potential safety hazards and results in poor maintenance reliability, requiring further improvement. Summary of the Invention

[0003] Based on this, this application provides a fault prediction method and system based on elevator components to solve the problem of poor maintenance reliability in the prior art.

[0004] In a first aspect, embodiments of this application provide a fault prediction method based on elevator components, the method comprising: Based on preset sensors, real-time operation dataset information corresponding to multiple elevator components is obtained; Based on the real-time running dataset information and the preset early warning threshold set information, running evaluation information is generated; Send the operational evaluation information to the designated display terminal.

[0005] Compared with the prior art, the beneficial effects are as follows: The fault prediction method based on elevator components provided in this application embodiment allows the terminal device to first acquire real-time operation dataset information corresponding to multiple elevator components based on a variety of preset sensors, and then accurately generate operation evaluation information based on the real-time operation dataset information and the preset warning threshold set information. Finally, the operation evaluation information is quickly sent to the designated display terminal, thereby effectively improving maintenance reliability, reducing the waste of human and material resources, reducing potential safety hazards, and solving the problem of poor maintenance reliability to a certain extent.

[0006] Secondly, embodiments of this application provide a fault prediction system based on elevator components, the system comprising: Real-time running data set acquisition module: used to acquire real-time running data set information for multiple elevator components based on preset sensors; Operational evaluation information generation module: used to generate operational evaluation information based on the real-time operational dataset information and the preset early warning threshold set information; Operational evaluation information sending module: used to send the operational evaluation information to the designated display terminal.

[0007] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0009] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0011] Figure 1 This is a schematic flowchart of a fault prediction method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the process after step S100 in a fault prediction method provided in an embodiment of this application. Figure 3 This is a flowchart illustrating step S200 in a fault prediction method provided in an embodiment of this application; Figure 4 This is a first schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 5 This is a second schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 6 This is a third schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 7 This is a fourth schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 8 This is a schematic diagram of the first process after step S300 in a fault prediction method provided in an embodiment of this application; Figure 9 This is a schematic diagram of the second process after step S300 in a fault prediction method provided in an embodiment of this application; Figure 10 This is a fifth schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 11 This is a sixth schematic diagram of the interface of a display terminal provided in an embodiment of this application; Figure 12 This is a schematic diagram of the third process after step S300 in a fault prediction method provided in an embodiment of this application; Figure 13 This is a schematic diagram of the fourth process after step S300 in a fault prediction method provided in an embodiment of this application; Figure 14 This is a block diagram of a fault prediction system provided in an embodiment of this application; Figure 15 This is a schematic diagram of a terminal device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0015] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0016] Please see Figure 1 , Figure 1This is a flowchart illustrating the fault prediction method based on elevator components provided in this application embodiment. In this embodiment, the execution subject of the fault prediction method is a terminal device. It is understood that the types of terminal devices include, but are not limited to, tablet computers, laptops, Ultra-Mobile Personal Computers (UMPCs), netbooks, Personal Digital Assistants (PDAs), etc., and this application embodiment does not impose any restrictions on the specific type of terminal device.

[0017] Please see Figure 1 The fault prediction method provided in this application includes, but is not limited to, the following steps: In S100, real-time operation dataset information corresponding to multiple elevator components is acquired based on preset sensors.

[0018] Specifically, the terminal device can first acquire real-time operation dataset information corresponding to multiple elevator components based on preset sensors. The sensors can be preset to be installed at designated locations in the target elevator. The sensors include acceleration sensors, impact force sensors, and temperature sensors. The temperature sensors can be installed in the form of patches or magnetic attachment, so as to collect data on the external surface temperature of key components and other parts of the target elevator without damaging or interfering with the original elevator system. The elevator components include drive motors, auxiliary wheels, drive wheels, and guide wheels. The real-time operation dataset information includes real-time vibration signals, real-time impact load information, and real-time temperature information.

[0019] In another possible implementation, the terminal device can combine the real-time operation dataset information of elevator components with the real-time operation status information of the elevator provided by the elevator control system, and cross-reference the data to improve the accuracy of the data. The real-time operation status information of the elevator provided by the elevator control system can include floor, door opening / closing, fault detection, and overload.

[0020] For information on generating accurate runtime evaluation information, please refer to the following in some possible implementations. Figure 2 Before step S100, the method further includes, but is not limited to, the following steps: In S101, the real-time vibration signal is processed to remove the trend term and filtered to generate a time-domain acceleration signal.

[0021] Specifically, the terminal device can remove the trend term from the real-time vibration signal to generate a real-time vibration signal after trend term removal, thereby eliminating the slowly changing nonlinear components in the real-time vibration signal caused by temperature drift or sensor-related factors. Then, the terminal device can filter the real-time vibration signal after trend term removal to generate a time-domain acceleration signal, thereby retaining the frequency band signal that is effective for fault analysis.

[0022] In S102, based on the preset fast Fourier transform algorithm, the time-domain acceleration signal is processed by frequency domain transformation to generate acceleration spectrum information.

[0023] Specifically, after the terminal device generates the time-domain acceleration signal, it can perform frequency domain conversion processing on the time-domain acceleration signal based on a preset fast Fourier transform algorithm to quickly generate acceleration spectrum information.

[0024] In S103, based on a preset frequency domain conversion function, the acceleration spectrum information is processed by row frequency domain conversion to generate velocity spectrum information.

[0025] Specifically, after the terminal device generates acceleration spectrum information, it can perform row frequency domain transformation on the acceleration spectrum information based on a preset frequency domain transformation function to effectively generate velocity spectrum information. The frequency domain transformation function can be: , In the formula, For velocity spectrum information; For acceleration spectrum information; This is the real-time vibration signal after trend removal and filtering.

[0026] In S104, the velocity spectrum information is corrected based on a preset high-pass filter to generate corrected velocity spectrum information.

[0027] Specifically, after the terminal device generates velocity spectrum information, it can perform correction processing on the velocity spectrum information based on a preset high-pass filter to generate corrected velocity spectrum information, thereby suppressing the amplification effect of low-frequency components during integration and correcting the range below a certain cutoff frequency.

[0028] In S105, based on the preset inverse Fourier transform algorithm, the corrected velocity spectrum information is restored to the time domain to generate real-time vibration velocity information.

[0029] Specifically, after the terminal device generates the corrected velocity spectrum information, the terminal device can perform time-domain processing on the corrected velocity spectrum information based on a preset inverse Fourier transform algorithm to generate real-time vibration velocity information. This enables the corrected velocity spectrum information to be restored back to the time domain through inverse fast Fourier transform. The real-time vibration velocity information is used to describe the real-time vibration velocity of elevator components.

[0030] In S106, the real-time maximum amplitude information corresponding to the characteristic frequency point is determined based on the preset health baseline spectrum and acceleration spectrum information.

[0031] Specifically, after the terminal device generates real-time vibration velocity information, it can determine the real-time maximum amplitude information corresponding to the characteristic frequency point based on the preset health baseline spectrum and acceleration spectrum information. The real-time maximum amplitude information is used to describe the real-time maximum amplitude of the characteristic frequency point.

[0032] For example, the terminal device can pre-construct a healthy baseline spectrum for elevator components in a healthy state, then locate the position of the characteristic frequency on the real-time generated acceleration spectrum information, further determine the characteristic frequency point, and then read the maximum amplitude value of the characteristic frequency point on the acceleration spectrum information.

[0033] In S200, operational evaluation information is generated based on real-time operational dataset information and preset early warning threshold set information.

[0034] Specifically, after the terminal device acquires the real-time operation dataset information, it can effectively generate operation evaluation information based on the real-time operation dataset information and the preset warning threshold set information. The warning threshold set information includes vibration velocity warning threshold, maximum amplitude warning threshold, impact load warning threshold, and temperature warning threshold. The specific values ​​of the warning threshold set information can be predefined by the operation and maintenance personnel.

[0035] Without loss of generality, the operational assessment information includes vibration characteristic assessment information, wear characteristic assessment information, impact load characteristic assessment information, and temperature characteristic assessment information. Specifically, the vibration characteristic assessment information is either normal or abnormal; normal vibration characteristic information describes that the elevator components are normal in terms of vibration, while abnormal vibration characteristic information describes that the elevator components are abnormal in terms of vibration. Similarly, the wear characteristic assessment information is either normal or abnormal; normal wear characteristic information describes that the elevator components are normal in terms of the degree of wear, while abnormal wear characteristic information describes that the elevator components are abnormal in terms of the degree of wear. Likewise, the impact load characteristic assessment information is either normal or abnormal; normal impact load characteristic information describes that the elevator components are normal in terms of the degree of impact load, while abnormal impact load characteristic information describes that the elevator components are abnormal in terms of the degree of impact load. Finally, the temperature characteristic assessment information is either normal or abnormal; normal temperature characteristic information describes that the elevator components are normal in terms of the degree of temperature, while abnormal temperature characteristic information describes that the elevator components are abnormal in terms of the degree of temperature.

[0036] In some possible implementations, for generating runtime evaluation information, please refer to [link / reference]. Figure 3 Step S200 includes, but is not limited to, the following steps: In S210, the real-time vibration velocity information is compared with the vibration velocity warning threshold.

[0037] Specifically, the terminal device can compare real-time vibration velocity information with vibration velocity warning thresholds.

[0038] In S220, if the real-time vibration velocity information is greater than or equal to the vibration velocity warning threshold, vibration characteristic abnormality information is generated.

[0039] Specifically, if the real-time vibration velocity information is greater than or equal to the vibration velocity warning threshold, the terminal device can quickly generate abnormal vibration characteristic information.

[0040] In S230, if the real-time vibration velocity information is less than the vibration velocity warning threshold, then normal vibration characteristic information is generated.

[0041] Specifically, if the real-time vibration velocity information is less than the vibration velocity warning threshold, the terminal device can quickly generate normal vibration characteristic information.

[0042] In S240, the real-time maximum amplitude information and the maximum amplitude warning threshold are compared.

[0043] Specifically, after the terminal device compares the real-time vibration velocity information with the vibration velocity warning threshold, the terminal device can compare the real-time maximum amplitude information with the maximum amplitude warning threshold.

[0044] In S250, if the real-time maximum amplitude information is greater than or equal to the maximum amplitude warning threshold, wear characteristic abnormal information is generated.

[0045] Specifically, if the real-time maximum amplitude information is greater than or equal to the maximum amplitude warning threshold, the terminal device can quickly generate abnormal wear characteristic information.

[0046] In S260, if the real-time maximum amplitude information is less than the maximum amplitude warning threshold, normal wear characteristic information is generated.

[0047] Specifically, if the real-time maximum amplitude information is less than the maximum amplitude warning threshold, the terminal device can quickly generate normal wear characteristic information.

[0048] In S270, real-time impact load information and impact load early warning thresholds are compared.

[0049] Specifically, after the terminal device compares the real-time maximum amplitude information with the maximum amplitude warning threshold, the terminal device can compare the real-time impact load information with the impact load warning threshold.

[0050] In S280, if the real-time impact load information is greater than or equal to the impact load warning threshold, impact load characteristic anomaly information is generated.

[0051] Specifically, if the real-time impact load information is greater than or equal to the impact load warning threshold, the terminal device can quickly generate impact load characteristic anomaly information.

[0052] In S290, if the real-time impact load information is less than the impact load warning threshold, normal impact load characteristic information is generated.

[0053] Specifically, if the real-time impact load information is less than the impact load warning threshold, the terminal device can quickly generate normal impact load characteristic information.

[0054] In S291, real-time temperature information is compared with temperature warning thresholds.

[0055] Specifically, after the terminal device compares the real-time impact load information with the impact load warning threshold, the terminal device can compare the real-time temperature information with the temperature warning threshold.

[0056] In S292, if the real-time temperature information is greater than or equal to the temperature warning threshold, temperature feature anomaly information is generated.

[0057] Specifically, if the real-time temperature information is greater than or equal to the temperature warning threshold, the terminal device can quickly generate abnormal temperature characteristics.

[0058] In S293, if the real-time temperature information is less than the temperature warning threshold, normal temperature characteristic information is generated.

[0059] Specifically, if the real-time temperature information is lower than the temperature warning threshold, the terminal device can quickly generate normal temperature characteristic information.

[0060] In S300, operational evaluation information is sent to the designated display terminal.

[0061] Specifically, after the terminal device generates operational evaluation information, it can send this information to a designated display terminal, which can be the terminal where the maintenance personnel are located. For example, please refer to [link to example]. Figure 4 , Figure 5 , Figure 6 and Figure 7 Maintenance personnel can remotely view the specific operating status of each elevator component through a display terminal.

[0062] For further information on possible implementations that facilitate comprehensive monitoring of the target elevator, please refer to [link / reference]. Figure 8 After step S300, the method further includes, but is not limited to, the following steps: In S400, real-time water content information and real-time impurity particle size information of the target hydraulic cylinder are obtained.

[0063] Generally speaking, when the target elevator is a traction elevator, the traction machine pre-installed on the roof can suspend the elevator car with steel cables, while the other end is equipped with a counterweight block of similar weight suspended by pulleys to maintain the balance on both sides of the pulleys. This method is used to move the height of the elevator car. When the target elevator is a hydraulic elevator, the hydraulic elevator can raise or lower the height of the elevator car using hydraulic principles.

[0064] Specifically, the terminal device can acquire real-time water content information and real-time impurity particle size information of the target hydraulic cylinder, where the target hydraulic cylinder is the hydraulic cylinder of the target elevator; the real-time water content information is used to describe the real-time water content of the target hydraulic cylinder; and the real-time impurity particle size information is used to describe the real-time impurity particle size of the target hydraulic cylinder.

[0065] In one possible implementation, real-time moisture content and real-time impurity particle size information can be input by maintenance personnel after detecting the hydraulic oil in the target hydraulic cylinder; these information can also be obtained using near-infrared spectroscopy. In another possible implementation, the data can be collected from bearings or other transmission mechanisms containing lubricating oil, or from areas where lubrication is required.

[0066] In S410, the real-time moisture content information is compared with the preset compliant moisture content threshold, and the real-time impurity particle size information is compared with the preset compliant particle size information.

[0067] Specifically, after the terminal device obtains real-time moisture content information and real-time impurity particle size information, the terminal device can compare the real-time moisture content information with the preset compliant moisture content threshold, and at the same time compare the real-time impurity particle size information with the preset compliant particle size information. The specific values ​​of the compliant moisture content threshold and compliant particle size information can be predefined by the operation and maintenance personnel.

[0068] In S420, if the real-time moisture content information is greater than the compliant moisture content threshold, or the real-time impurity particle size information is greater than the compliant particle size information, hydraulic oil abnormality information is generated; otherwise, hydraulic oil normality information is generated.

[0069] Specifically, if the real-time moisture content information is greater than the compliant moisture content threshold, or the real-time impurity particle size information is greater than the compliant particle size information, it indicates that the hydraulic oil has a high safety hazard. Therefore, the terminal equipment can generate abnormal hydraulic oil information. Otherwise, the terminal equipment can generate normal hydraulic oil information.

[0070] For further comprehensive monitoring of the target elevator, please refer to some possible implementation methods. Figure 9 After step S300, the method further includes, but is not limited to, the following steps: In the S500, based on a preset LSTM timing network, the current running time node information and performance degradation prediction curve information of multiple elevator components are obtained.

[0071] Specifically, the terminal device can obtain the current operating time node information and performance degradation prediction curve information of multiple elevator components based on a preset LSTM timing network. The current operating time node information is used to describe the cumulative lifespan of the elevator components, and the performance degradation prediction curve information is used to describe the trend curve of the elevator components' performance gradually decreasing with the change of time conditions.

[0072] In one possible implementation, the LSTM time series network algorithm can be designed in a distributed manner. It can be deployed at the smart gateway using an edge computing architecture, or at the server using a centralized computing architecture, making the deployment method relatively flexible. It can even be deployed at the data acquisition device and calculated during sensor data acquisition.

[0073] It should be noted that the LSTM time series network is used for predictive analysis of performance degradation trends, playing a core role in evaluating early warning values. Its preset alarm value can calculate how much time is left before a failure occurs. This preset alarm value can be provided by the component's own performance degradation curve database, i.e., automatic matching based on the corresponding brand and model. When it is necessary to match a component whose brand and model cannot be found in the professional database, the LSTM algorithm can analyze the characteristics of the current operating data, iteratively calculating with the continuously accumulated operating data, gradually returning to a more accurate prediction curve, providing users with a prediction of the time before the component will fail in the future.

[0074] For example, please refer to Figure 10 The terminal device can first obtain historical performance data of elevator components, such as vibration and temperature characteristics and their corresponding degradation indicators. Then, it performs data cleaning, smoothing, noise reduction and normalization preprocessing, and then constructs "input-output" sequence pairs in chronological order. Subsequently, it constructs a neural network model containing multiple LSTM and Dropout layers, and learns the mapping relationship between the historical sequence and future performance changes using the historical sequence as input. During training, mean square error and other parameters are used as loss functions to optimize parameters. After training, the latest monitoring data is input into the model to realize the output of future performance degradation trend prediction, that is, performance degradation prediction curve information.

[0075] In S510, based on a preset time threshold, the performance degradation prediction curve information is divided and processed to generate information on the good operation stage, the degradation initiation stage, the degradation acceleration stage, the performance decline stage, and the near-failure stage.

[0076] For example, please refer to Figure 11 After the terminal device obtains the current running time node information and the performance degradation prediction curve information, the terminal device can divide the performance degradation prediction curve information based on the preset time threshold to generate information on the good running stage, the degradation initiation stage, the degradation acceleration stage, the performance decline stage, and the near failure stage.

[0077] In S520, the current stage information and the next stage information are determined based on the current running time node information.

[0078] Specifically, after the terminal device segments the performance degradation prediction curve information, it can determine the current stage information and the next stage information based on the current running time node information. The current stage information describes the stage in which the current running time node information is located, and the next stage information describes the stage following the current running time node information. For example, if the current stage information is the degradation bud stage information, then the next stage information is the degradation acceleration stage information.

[0079] In S530, the current stage information and the next stage information are sent to the designated display terminal.

[0080] Specifically, after the terminal device determines the current stage information and the next stage information, the terminal device can send the current stage information and the next stage information to the designated display terminal.

[0081] In some possible implementations, to facilitate timely maintenance of the elevator car doors, please refer to [link / reference needed]. Figure 12 After step S300, the method further includes, but is not limited to, the following steps: In S600, the cumulative number of times the target door has been opened and closed is obtained.

[0082] Specifically, the terminal device can obtain information on the cumulative number of times the target door has been opened and closed, whereby the cumulative number of opening and closing information describes the total number of times the target door has been opened and closed.

[0083] In S610, the cumulative number of switching times is compared with the preset threshold number of switching times.

[0084] Specifically, after the terminal device obtains the cumulative number of switches, it can compare the cumulative number of switches with a preset threshold for the number of switches. The specific value of the threshold for the number of switches can be predefined by the maintenance personnel.

[0085] In S620, if the cumulative number of switching times is greater than or equal to the switching time threshold, a door maintenance command or a door replacement command is generated.

[0086] Specifically, if the cumulative number of opening and closing times is greater than or equal to the threshold, the terminal device can generate a door maintenance command or a door replacement command. By using distance sensors installed at both ends of the elevator door, the opening and closing status of the door can be monitored. This not only determines whether the door is properly opened or closed, but also records the number of times the door is opened and closed. By comparing the usage frequency of key parts and components with a reference, the system can more accurately determine the time cycle for maintenance or replacement of key parts and components of the door. The door maintenance command is used to instruct the elevator door to be maintained, while the door replacement command is used to instruct the elevator door to be replaced.

[0087] In some possible implementations, to improve the safety of the target elevator, please refer to [link / reference needed]. Figure 13 After step S300, the method further includes, but is not limited to, the following steps: In the S700, the leveling alignment status information of the target elevator is obtained based on preset sensors.

[0088] Specifically, the terminal device can acquire the leveling alignment status information of the target elevator based on a preset leveling sensor. The leveling alignment status information can be either normal alignment status information or abnormal alignment status information.

[0089] In S710, if the leveling alignment status information is abnormal alignment status information, a fault alarm command is generated.

[0090] Specifically, if the leveling alignment status information is abnormal alignment status information, the terminal device can generate a fault alarm command, which is used to indicate that the target elevator has a leveling misalignment fault.

[0091] It should be noted that all data involved in this application may be stored on a local server or a cloud server.

[0092] The implementation principle of the elevator component fault prediction method in this application embodiment is as follows: The terminal device can first acquire real-time operation dataset information corresponding to multiple elevator components based on a variety of preset sensors. Then, based on the real-time operation dataset information and the preset early warning threshold set information, it accurately generates operation evaluation information. Finally, the operation evaluation information is quickly sent to the designated display terminal, thereby effectively improving maintenance reliability. This allows owners to schedule maintenance work for elevators that need to be maintained in advance, effectively avoiding the probability of sudden failures and significantly improving the safety of elevator use. It also allows elevator maintenance companies to make targeted procurement and preparation of key components based on the early warning information, effectively reducing the inventory of key components and providing users with more accurate spare parts of corresponding brands and models for maintenance, improving work efficiency and user satisfaction. At the same time, it also allows owners to track and compare the data of real-time mechanical vibration analysis curves, clearly grasp the operating status of key components in real time, and promptly detect the elevator maintenance company's misconduct of substandard or old parts during maintenance, effectively supervising the maintenance work.

[0093] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Embodiments of this application also provide a fault prediction system based on elevator components. For ease of explanation, only the parts relevant to this application are shown, such as... Figure 14 As shown, the system 140 includes: Real-time running data set information acquisition module 141: used to acquire real-time running data set information corresponding to multiple elevator components based on preset sensors; Operational assessment information generation module 142: used to generate operational assessment information based on real-time operational dataset information and preset early warning threshold set information; Operational evaluation information sending module 143: Used to send operational evaluation information to a specified display terminal.

[0095] Optionally, the real-time running dataset information includes real-time vibration signals, real-time impact load information, and real-time temperature information; the early warning threshold set information includes vibration velocity early warning threshold, maximum amplitude early warning threshold, impact load early warning threshold, and temperature early warning threshold; the system 140 also includes: Time-domain acceleration signal generation module: used to remove trend terms and filter real-time vibration signals to generate time-domain acceleration signals; Acceleration spectrum information generation module: used to perform frequency domain transformation processing on time-domain acceleration signals based on a preset fast Fourier transform algorithm to generate acceleration spectrum information; The first velocity spectrum information generation module is used to perform row frequency domain transformation processing on the acceleration spectrum information based on a preset frequency domain transformation function to generate velocity spectrum information. Second velocity spectrum information generation module: used to correct the velocity spectrum information based on a preset high-pass filter and generate corrected velocity spectrum information; Real-time vibration velocity information generation module: used to perform time-domain processing on the corrected velocity spectrum information based on a preset inverse Fourier transform algorithm to generate real-time vibration velocity information; Real-time maximum amplitude information determination module: used to determine the real-time maximum amplitude information corresponding to the characteristic frequency point based on the preset health baseline spectrum and acceleration spectrum information.

[0096] Optionally, the operational evaluation information includes vibration characteristic evaluation information, wear characteristic evaluation information, impact load characteristic evaluation information, and temperature characteristic evaluation information. The vibration characteristic evaluation information is either normal or abnormal; the wear characteristic evaluation information is either normal or abnormal; the impact load characteristic evaluation information is either normal or abnormal; and the temperature characteristic evaluation information is either normal or abnormal. The aforementioned operational evaluation information generation module 142 includes: Real-time vibration velocity information comparison submodule: used to compare real-time vibration velocity information with vibration velocity warning threshold; Vibration feature anomaly information generation submodule: used to generate vibration feature anomaly information if the real-time vibration velocity information is greater than or equal to the vibration velocity warning threshold; Vibration characteristic normal information generation submodule: used to generate vibration characteristic normal information if the real-time vibration velocity information is less than the vibration velocity warning threshold; Real-time maximum amplitude information comparison submodule: used to compare real-time maximum amplitude information with the maximum amplitude warning threshold; Wear feature anomaly information generation submodule: used to generate wear feature anomaly information if the real-time maximum amplitude information is greater than or equal to the maximum amplitude warning threshold; Wear characteristic normal information generation submodule: used to generate wear characteristic normal information if the real-time maximum amplitude information is less than the maximum amplitude warning threshold; Real-time impact load information comparison submodule: used to compare real-time impact load information with impact load early warning threshold; Impact load characteristic anomaly information generation submodule: used to generate impact load characteristic anomaly information if the real-time impact load information is greater than or equal to the impact load warning threshold; Impact load characteristic normal information generation submodule: used to generate impact load characteristic normal information if the real-time impact load information is less than the impact load warning threshold; Real-time temperature information comparison submodule: used to compare real-time temperature information with temperature warning thresholds; Temperature feature anomaly information generation submodule: used to generate temperature feature anomaly information if the real-time temperature information is greater than or equal to the temperature warning threshold; Temperature characteristic normal information generation submodule: used to generate temperature characteristic normal information if the real-time temperature information is less than the temperature warning threshold.

[0097] Optionally, the system 140 also includes: Real-time moisture content information acquisition module: used to acquire real-time moisture content information and real-time impurity particle size information of the target hydraulic cylinder; Real-time moisture content information comparison module: used to compare real-time moisture content information with preset compliant moisture content thresholds, and to compare real-time impurity particle size information with preset compliant particle size information. Hydraulic oil abnormality information generation module: If the real-time water content information is greater than the compliant water content threshold, or the real-time impurity particle size information is greater than the compliant particle size information, then hydraulic oil abnormality information is generated; otherwise, hydraulic oil normality information is generated.

[0098] Optionally, the system 140 also includes: Current running time node information acquisition module: used to acquire current running time node information and performance degradation prediction curve information of multiple elevator components based on a preset LSTM time series network; Phase Information Generation Module: Based on a preset time threshold, the performance degradation prediction curve information is divided and processed to generate information on the good operation phase, the degradation budding phase, the degradation acceleration phase, the performance decline phase, and the near-failure phase. Current Stage Information Determination Module: Used to determine the current stage information and the next stage information based on the current running time node information; Current stage information sending module: Used to send current stage information and next stage information to the designated display terminal.

[0099] Optionally, the system 140 also includes: Cumulative opening and closing count information acquisition module: used to acquire the cumulative opening and closing count information of the target door; Cumulative switch count information comparison module: used to compare the cumulative switch count information with the preset switch count threshold; Door maintenance instruction generation module: If the cumulative number of opening and closing times is greater than or equal to the threshold number of opening and closing times, a door maintenance instruction or a door replacement instruction will be generated.

[0100] Optionally, the system 140 also includes: Leveling alignment status information acquisition module: used to acquire the leveling alignment status information of the target elevator based on preset sensors, wherein the leveling alignment status information is normal alignment status information or abnormal alignment status information; Fault alarm command generation module: Used to generate a fault alarm command if the level alignment status information is abnormal alignment status information.

[0101] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0102] This application also provides a terminal device, such as... Figure 15 As shown, the terminal device 150 of this embodiment includes: a processor 151, a memory 152, and a computer program 153 stored in the memory 152 and executable on the processor 151. When the processor 151 executes the computer program 153, it implements the steps in the above-described fault prediction method embodiment, for example... Figure 1 The steps S100 to S300 are shown; or, when the processor 151 executes the computer program 153, it implements the functions of each module in the above-described device, for example... Figure 14 The functions of modules 141 to 143 are shown.

[0103] The terminal device 150 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The terminal device 150 includes, but is not limited to, a processor 151 and a memory 152. Those skilled in the art will understand that... Figure 15 This is merely an example of terminal device 150 and does not constitute a limitation on terminal device 150. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device 150 may also include input / output devices, network access devices, buses, etc.

[0104] The processor 151 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0105] The memory 152 can be an internal storage unit of the terminal device 150, such as the hard disk or memory of the terminal device 150. The memory 152 can also be an external storage device of the terminal device 150, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 150. Furthermore, the memory 152 can include both internal storage units and external storage devices of the terminal device 150. The memory 152 can also store computer program 153 and other programs and data required by the terminal device 150. The memory 152 can also be used to temporarily store data that has been output or will be output.

[0106] One embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0107] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the methods, principles and structures of this application should be covered within the scope of protection of this application.

Claims

1. A fault prediction method based on elevator components, characterized in that, The method includes: Based on preset sensors, real-time operation dataset information corresponding to multiple elevator components is obtained; Based on the real-time running dataset information and the preset early warning threshold set information, running evaluation information is generated; Send the operational evaluation information to the designated display terminal.

2. The method according to claim 1, characterized in that, The real-time operation dataset information includes real-time vibration signals, real-time impact load information, and real-time temperature information; the early warning threshold set information includes vibration velocity early warning threshold, maximum amplitude early warning threshold, impact load early warning threshold, and temperature early warning threshold. After acquiring real-time operational dataset information corresponding to multiple elevator components based on preset sensors, the method further includes: The real-time vibration signal is subjected to trend term removal and filtering to generate a time-domain acceleration signal; Based on a preset fast Fourier transform algorithm, the time-domain acceleration signal is subjected to frequency domain transformation processing to generate acceleration spectrum information; Based on a preset frequency domain transformation function, the acceleration spectrum information is subjected to row frequency domain transformation processing to generate velocity spectrum information; Based on a preset high-pass filter, the velocity spectrum information is corrected to generate corrected velocity spectrum information. Based on a preset inverse Fourier transform algorithm, the corrected velocity spectrum information is restored to the time domain to generate real-time vibration velocity information. Based on the preset health baseline spectrum and the acceleration spectrum information, the real-time maximum amplitude information corresponding to the characteristic frequency point is determined.

3. The method according to claim 2, characterized in that, The operational evaluation information includes vibration characteristic evaluation information, wear characteristic evaluation information, impact load characteristic evaluation information, and temperature characteristic evaluation information. The vibration characteristic evaluation information is either normal or abnormal vibration characteristic information; the wear characteristic evaluation information is either normal or abnormal wear characteristic information; the impact load characteristic evaluation information is either normal or abnormal impact load characteristic information; and the temperature characteristic evaluation information is either normal or abnormal temperature characteristic information. The step of generating operational evaluation information based on the real-time operational dataset information and the preset early warning threshold set information includes: Compare the real-time vibration velocity information with the vibration velocity warning threshold; If the real-time vibration velocity information is greater than or equal to the vibration velocity warning threshold, then vibration characteristic abnormality information is generated; If the real-time vibration velocity information is less than the vibration velocity warning threshold, then normal vibration characteristic information is generated; Compare the real-time maximum amplitude information with the maximum amplitude warning threshold; If the real-time maximum amplitude information is greater than or equal to the maximum amplitude warning threshold, then wear characteristic abnormal information is generated; If the real-time maximum amplitude information is less than the maximum amplitude warning threshold, then normal wear characteristic information is generated; Compare the real-time impact load information with the impact load early warning threshold; If the real-time impact load information is greater than or equal to the impact load warning threshold, then impact load characteristic anomaly information is generated; If the real-time impact load information is less than the impact load warning threshold, then normal impact load characteristic information is generated. Compare the real-time temperature information with the temperature warning threshold; If the real-time temperature information is greater than or equal to the temperature warning threshold, then abnormal temperature characteristic information is generated; If the real-time temperature information is less than the temperature warning threshold, then normal temperature characteristic information is generated.

4. The method according to claim 1, characterized in that, After generating operational evaluation information based on the real-time operational dataset information and the preset early warning threshold set information, the method includes: Obtain real-time water content and impurity particle size information of the target hydraulic cylinder; The real-time moisture content information is compared with the preset compliant moisture content threshold, and the real-time impurity particle size information is compared with the preset compliant particle size information. If the real-time moisture content information is greater than the compliant moisture content threshold, or the real-time impurity particle size information is greater than the compliant particle size information, then hydraulic oil abnormality information is generated; otherwise, hydraulic oil normality information is generated.

5. The method according to claim 1, characterized in that, After sending the operational evaluation information to the designated display terminal, the method further includes: Based on a preset LSTM timing network, the current running time node information and performance degradation prediction curve information of multiple elevator components are obtained. Based on a preset time threshold, the performance degradation prediction curve information is divided and processed to generate information on the good operation stage, the degradation initiation stage, the degradation acceleration stage, the performance decline stage, and the near-failure stage. Based on the current running time node information, determine the current stage information and the next stage information; Send the current stage information and the next stage information to the designated display terminal.

6. The method according to claim 1, characterized in that, After sending the operational evaluation information to the designated display terminal, the method further includes: Obtain the cumulative number of times the target door has been opened and closed; Compare the cumulative number of switches with a preset threshold number of switches; If the cumulative number of switches is greater than or equal to the switch count threshold, a door maintenance command or a door replacement command is generated.

7. The method according to claim 1, characterized in that, After sending the operational evaluation information to the designated display terminal, the method further includes: Based on preset sensors, the leveling alignment status information of the target elevator is obtained, wherein the leveling alignment status information is normal alignment status information or abnormal alignment status information. If the level alignment status information is abnormal alignment status information, a fault alarm command is generated.

8. A fault prediction system based on elevator components, characterized in that, The system includes: Real-time running data set acquisition module: used to acquire real-time running data set information for multiple elevator components based on preset sensors; Operational evaluation information generation module: used to generate operational evaluation information based on the real-time operational dataset information and the preset early warning threshold set information; Operational evaluation information sending module: used to send the operational evaluation information to the designated display terminal.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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