Online monitoring system for abrasion of front fork bowl group

Abstract

The invention relates to the technical field of mechanical part state monitoring, and discloses a front fork bowl group wear online monitoring system which comprises a state sensing module, a feature extraction module, a wear judgment module, a data storage module, a result output module and a result feedback module. The state sensing module obtains vibration, displacement and temperature signals during operation of the front fork bowl set, the feature extraction module preprocesses and extracts wear feature data through the signal processing assembly, the wear judgment module marks potential wear links according to the feature data, the data storage module stores related data in a classified mode, and the result output module marks the normal state. And the result feedback module outputs the result to the monitoring terminal. The system further comprises an early warning prompt module which can generate an early warning signal when the wear characteristic data exceed a threshold value. According to the system, the accuracy and timeliness of wear monitoring are improved, a reliable basis is provided for equipment maintenance, and the system is suitable for wear monitoring of different models of front fork bowl groups in various scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical component condition monitoring, and in particular to an online monitoring system for wear of a front fork bowl assembly. Background Art

[0002] The front fork headset is a key component connecting the front fork to the frame of vehicles like bicycles and motorcycles. Its wear directly impacts vehicle handling stability and driving safety. As vehicles become more frequently used and their service life increases, the headset is subject to alternating loads, vibration, and friction, making it susceptible to wear and looseness. Failure to detect and address these issues can lead to serious safety incidents, such as steering failure.

[0003] Existing fork headset wear monitoring technologies have significant limitations. On the one hand, traditional offline detection methods require manual disassembly of components for visual inspection or measurement with the help of handheld instruments. This is not only time-consuming and labor-intensive, but also unable to capture the evolution of wear during operation in real time, making it difficult to issue early warnings in the early stages of failure. On the other hand, some online monitoring solutions rely solely on a single vibration signal or displacement signal for analysis, without considering the coupling effects of multiple physical quantities such as temperature, resulting in low wear identification accuracy and prone to misjudgment or omission. In addition, existing systems generally lack adaptability to different models of fork headsets, making it difficult to dynamically adjust monitoring parameters according to the equipment operation scenario, and the data storage and management methods are extensive, unable to provide effective support for subsequent fault analysis and maintenance decisions.

[0004] The signal processing components of existing technologies lack targeted training based on historical monitoring data, resulting in limited feature extraction capabilities and an inability to accurately identify wear characteristics under complex operating conditions. In terms of wear type identification, a lack of systematic analysis of the correlation between multiple indicators makes it difficult to precisely locate wear points, leading to inefficient maintenance. Furthermore, the threshold setting of the early warning mechanism lacks a dynamic adjustment mechanism, making it unable to adapt to the monitoring needs of different usage scenarios, further limiting the application scope of existing technologies. Summary of the Invention

[0005] The object of the present invention is to provide an online monitoring system for wear of a front fork headset to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an online monitoring system for front fork headset wear, the system comprising: The state perception module is used to obtain real-time vibration signals, displacement signals, and temperature signals during the operation of the front fork headset; a feature extraction module, configured to perform preprocessing and feature extraction on the real-time vibration signal, the displacement signal, and the temperature signal through a signal processing component to obtain wear feature data; A wear identification module is configured to mark a potential wear link in the current operation link of the front fork headset when the wear feature data exceeds an allowable feature range; A data storage module is configured to classify and store the data related to the operation link of the front fork headset having the potential wear link mark through a storage management device to obtain a first storage result; A result output module is configured to mark the current operation link of the front fork headset as normal when the wear characteristic data is within the characteristic allowable range, and obtain a second storage result; A result feedback module is configured to output the first stored result or the second stored result to a monitoring terminal.

[0007] Preferably, the execution process of the feature extraction module includes: Compare the vibration signal time domain sequence, displacement signal fluctuation value, and temperature signal change curve with the standard vibration sequence, standard displacement value, and standard temperature curve to obtain the time domain root mean square index, displacement peak-to-peak index, and temperature mean index; constructing a wear characteristic data set according to the time domain root mean square index, the displacement peak-to-peak index, and the temperature mean index; The wear characteristic data set is input into the signal processing component, and the wear characteristic data is output.

[0008] Preferably, the execution process of the state perception module includes: Establishing a communication connection with a vibration sensor, a displacement sensor, and a temperature sensor installed on the front fork headset and receiving a vibration signal time domain sequence, a displacement signal fluctuation value, and a temperature signal change curve during the operation of the front fork headset; Establishing a communication connection with the monitoring system control terminal and receiving the standard vibration sequence, standard displacement value, and standard temperature curve monitored by the fork headset; The vibration signal time domain sequence, the displacement signal fluctuation value, and the temperature signal change curve are classified into the real-time vibration signal, the displacement signal, and the temperature signal; The standard vibration sequence, the standard displacement value, and the standard temperature curve are included in the preset reference data for monitoring the front fork bowl assembly.

[0009] Preferably, the execution process of the feature extraction module further includes: Setting a feature evaluation rule based on the fork headset model, the feature evaluation rule is used to quantify the degree of difference between the vibration signal time domain sequence and the standard vibration sequence, the displacement signal fluctuation value and the standard displacement value, and the temperature signal change curve and the standard temperature curve; According to the characteristic evaluation rules, the vibration signal time domain sequence, the displacement signal fluctuation value, and the temperature signal change curve are compared with the standard vibration sequence, the standard displacement value, and the standard temperature curve to obtain the time domain root mean square index, the displacement peak-to-peak value index, and the temperature mean index.

[0010] Preferably, the execution process of the feature extraction module further includes: According to the current front fork headset usage scenario, calling the corresponding signal processing component to process the wear characteristic data set to obtain the wear characteristic data; The signal processing component construction process includes: Collecting historical monitoring record data, wherein the historical monitoring record data includes historical vibration signals, historical displacement signals, and historical temperature signals; Constructing a historical feature data set according to the historical vibration signal, the historical displacement signal, and the historical temperature signal; Using the historical feature data set as a reference, collect historical monitoring result data, and count the proportion of misjudgment record data in the historical monitoring result data, which is set as the feature misjudgment probability identification value; The signal processing component is generated by training using the feature misjudgment probability identification value as a training basis and the historical feature data set as an input sample.

[0011] Preferably, the execution process of the wear determination module includes: Filtering first historical monitoring result data whose time domain root mean square index exceeds the allowable index range, and whose displacement peak-to-peak index and temperature mean index do not exceed the allowable index range; The wear type whose trigger frequency exceeds the frequency allowable range in the first historical monitoring result data is counted and set as the time domain deviation high frequency wear type.

[0012] Preferably, the execution process of the wear determination module further includes: Traversing the time domain root mean square index, the displacement peak-to-peak index, and the temperature mean index to perform wear type correlation matching to obtain a time domain correlation wear type, a displacement correlation wear type, and a temperature correlation wear type; Sort the time-domain associated wear type, the displacement associated wear type, and the temperature associated wear type according to the time-domain root mean square index, the displacement peak-to-peak index, and the temperature mean index in descending order to obtain a wear type sorting result; The determination and decision unit determines the wear status of the front fork headset operation link having the potential wear link mark according to the wear type sorting result to obtain the first stored result.

[0013] Preferably, the execution process of the result output module includes: When the wear characteristic data is within the characteristic allowable range, calling the preset normal state parameters; According to the normal state parameters, parameters of the vibration monitoring unit, the displacement monitoring unit, and the temperature monitoring unit of the front fork headset are set to generate the second storage result.

[0014] Preferably, the device further comprises a data storage module, wherein the data storage module is used to: receiving the real-time vibration signal, the displacement signal, the temperature signal, and the preset reference data acquired by the state sensing module; receiving the first stored result and the second stored result output by the result feedback module; The real-time vibration signal, the displacement signal, the temperature signal, the preset reference data, the first storage result and the second storage result are classified and stored in different data partitions to form a wear monitoring record database.

[0015] Preferably, the system further includes an early warning prompt module, and the execution process of the early warning prompt module includes: When the wear identification module marks the potential wear link of the front fork headset operation link, calling the preset warning threshold range; Comparing the wear characteristic data with the warning threshold range, and generating a warning signal if the threshold is exceeded; The warning signal is sent to the sound and light prompt device of the monitoring terminal to trigger the warning response Compared with the prior art, the present invention has the following beneficial effects: The online fork headset wear monitoring system provided by this invention uses a multi-dimensional state perception module to collect vibration, displacement, and temperature signals in real time, enabling comprehensive monitoring of the fork headset's operating status. Compared to traditional single-parameter monitoring, this system improves the accuracy and timeliness of wear identification. A feature extraction module generates multi-dimensional indicators by comparing them with standard data, and sets feature evaluation rules based on fork headset model and usage scenario, constructing an adaptive signal processing component. This effectively addresses the existing technology's lack of adaptability to different equipment models and complex operating conditions.

[0016] The wear identification module accurately locates potential wear links by matching and ranking the correlations of multiple indicators, avoiding misjudgments or missed detections caused by a single indicator and improving the reliability of wear status identification. The data storage module categorizes and stores real-time signals, reference data, and identification results to form a structured database of wear monitoring records. This provides rich data support for subsequent fault analysis and maintenance strategy optimization, overcoming the chaotic data management shortcomings of existing technologies.

[0017] The collaborative work of the result output module and the early warning prompt module can trigger a warning in a timely manner when the wear characteristic data exceeds the allowable range, and feed the results back to the monitoring terminal, realizing early warning and real-time response to faults, and providing guarantees for the safe operation of the equipment. In addition, the signal processing component is generated based on historical data training, and through the statistical misjudgment probability optimization model, the feature extraction capability is continuously improved, so that the system has the characteristics of self-learning and adaptive optimization, and can continuously improve the monitoring accuracy in long-term operation. Overall, through the integration of multiple technical means, the system realizes intelligent and precise online monitoring of front fork headset wear, improving equipment maintenance efficiency and operational safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a working principle diagram of the front fork headset wear online monitoring system of the present invention; Figure 2 Flowchart executed for the feature extraction module; Figure 3 Flowchart executed by the state awareness module; Figure 4 Flowchart constructed for the signal processing component. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] See also Figures 1-4 The present invention relates to an online monitoring system for front fork headset wear, which includes: a state perception module, a feature extraction module, a wear identification module, a data storage module, a result output module, and a result feedback module. The specific implementation steps are as follows: The state perception module establishes a communication connection with the vibration sensor, displacement sensor, and temperature sensor installed on the front fork bowl group, receives the time domain sequence of the running vibration signal, the displacement signal fluctuation value, and the temperature signal change curve, and communicates with the monitoring system control end to receive the standard vibration sequence, standard displacement value, and standard temperature curve, and classifies these signals into real-time signals and preset reference data respectively.

[0021] The feature extraction module preprocesses and extracts features from real-time vibration, displacement, and temperature signals through signal processing components to obtain wear feature data.

[0022] When the wear characteristic data exceeds the allowable range, the wear identification module marks the current operation link as potentially worn.

[0023] The data storage module classifies and stores the data related to the operation links with potential wear marks through the storage management device to obtain a first storage result.

[0024] When the wear characteristic data is within the allowable range, the result output module performs a normal state mark to obtain a second storage result.

[0025] The result feedback module outputs the first or second stored result to the monitoring terminal.

[0026] Example 1: The execution process of the feature extraction module in this embodiment is as follows: the three types of real-time signals, namely the vibration signal time domain sequence, the displacement signal fluctuation value, and the temperature signal change curve, are compared with the three types of preset reference data, namely the standard vibration sequence, the standard displacement value, and the standard temperature curve. During the comparison process, the time domain root mean square index, the displacement peak-to-peak index, and the temperature mean index are obtained through a specific calculation method. The comparison here is not a simple numerical comparison, but an analysis based on the characteristic parameters of the signal. For example, the vibration signal time domain sequence will involve amplitude changes at different time points, which are matched with the corresponding parameters of the standard vibration sequence and the difference is calculated to obtain the time domain root mean square index that can reflect the vibration characteristics.

[0027] A wear characteristic dataset is constructed based on the acquired time-domain RMS, peak-to-peak displacement, and mean temperature metrics. This dataset requires organizing each metric according to a specific logical structure, ensuring that each metric can be found within the dataset and that relationships are established for subsequent processing and analysis. For example, each sample data point will include the specific values ​​for these three metrics, as well as the corresponding fork and headset operating status information.

[0028] The constructed wear feature dataset is input into the signal processing component, which then processes and calculates the wear feature data. The signal processing component plays a key role in this process, further extracting and optimizing the input dataset. This may involve filtering, noise reduction, and feature enhancement to produce feature data that more accurately reflects the wear status of the fork headset.

[0029] In the above process, it is also necessary to set feature evaluation rules based on the specific model of the front fork headset. Different models of front fork headsets may have differences in structure, materials, operating parameters, etc., so evaluation rules need to be formulated for specific models. The function of this rule is to quantify the degree of difference between the three groups of signals: the time domain sequence of the vibration signal and the standard vibration sequence, the displacement signal fluctuation value and the standard displacement value, and the temperature signal change curve and the standard temperature curve. The quantification method can be to set different weight coefficients, or to use a specific mathematical model to calculate the difference value, so that the degree of difference can be expressed by a specific value.

[0030] Based on the established characteristic evaluation rules, the vibration signal time-domain sequence, displacement signal fluctuation values, and temperature signal variation curves are again compared with the corresponding standard sequences and values ​​to more accurately obtain the time-domain RMS index, displacement peak-to-peak index, and temperature mean index. This step further refines and clarifies the previous index acquisition process, ensuring the accuracy and reliability of the index by following specific evaluation rules.

[0031] Based on the current fork headset usage scenario, the corresponding signal processing component is called. Different usage scenarios, such as different working environments and load conditions, may result in different wear characteristics of the fork headset, necessitating different signal processing components to handle them. For example, in high-load scenarios, the signal processing component may need to focus more on analyzing high-frequency vibration signals; while in harsh environments, it may need to pay more attention to changes in temperature signals.

[0032] The signal processing component construction process is as follows: First, historical monitoring data is collected, including historical vibration signals, historical displacement signals, and historical temperature signals. The collected historical data needs to be comprehensive and representative, covering different operating conditions and wear levels. Then, based on the collected historical vibration signals, historical displacement signals, and historical temperature signals, a historical feature dataset is constructed. This dataset is constructed in a similar manner to the previous wear feature dataset, but the data source is historical monitoring records.

[0033] Using the constructed historical feature dataset as a reference, we collected historical monitoring results and calculated the percentage of misjudgment records. This percentage is used as the feature misjudgment probability indicator. Misjudgment records refer to historical monitoring results where the actual wear status differs from the monitoring results. By calculating the misjudgment percentage, we can understand the accuracy of the current monitoring system.

[0034] The feature misjudgment probability identification value is taken as a training basis, and a historical feature data set is taken as an input sample to train a signal processing component. During the training process, the parameters and model structure of the signal processing component are continuously adjusted, so that the signal processing component can reduce the misjudgment probability and improve the accurate extraction capability of the wear feature data when processing the input sample. After training, the signal processing component can better adapt to different signal inputs and output more accurate wear feature data.

[0035] Embodiment 2 The execution process of the state perception module in this embodiment is as follows: the state perception module needs to establish a communication connection with the vibration sensor, displacement sensor, and temperature sensor installed on the fork bowl set. These sensors are arranged at key positions of the fork bowl set to collect relevant signals in real time during operation. When establishing the communication connection, the compatibility and stability of the communication protocol need to be ensured to ensure that the data sent by the sensor can be accurately received. The vibration sensor monitors the vibration of the fork bowl set in real time during operation to generate a vibration signal time sequence, which contains vibration amplitude, frequency, and other information at different time points; the displacement sensor is used to measure the relative displacement change between components of the fork bowl set to obtain displacement signal fluctuation values; and the temperature sensor monitors the temperature change of the fork bowl set in real time during operation to form a temperature signal change curve.

[0036] After establishing the communication connection with the sensor and ensuring the normal communication, the state perception module starts to receive the vibration signal time sequence, displacement signal fluctuation value, and temperature signal change curve during the operation of the fork bowl set. The process of receiving data needs to have real-time and reliability, and can timely handle possible data transmission abnormal situations such as signal interruption and data loss, and take corresponding recovery measures to ensure the integrity of the data.

[0037] The state perception module also needs to establish a communication connection with the monitoring system control end. The monitoring system control end is usually a platform that centrally manages and controls the entire monitoring system, and stores standard reference data for different models and different working conditions of the fork bowl set. The state perception module receives the standard vibration sequence, standard displacement value, and standard temperature curve of the fork bowl set through communication with the control end. These standard data are summarized through a large number of experiments and actual operation experience, and are used as a basis for judging whether the running state of the fork bowl set is normal.

[0038] After receiving real-time signals from sensors and standard data from the control end, the state perception module needs to classify and organize this data. First, the vibration signal time domain series, displacement signal fluctuation values, and temperature signal change curves are classified into three categories of real-time monitoring data: real-time vibration signal, displacement signal, and temperature signal. This classification process requires precise alignment to ensure that each signal is correctly classified for subsequent processing and analysis.

[0039] Standard vibration sequences, standard displacement values, and standard temperature curves are included in the preset reference data for fork headset monitoring. This preset reference data is an important basis for comparison in the subsequent feature extraction and wear identification processes, so its storage accuracy and completeness must be ensured.

[0040] Throughout the execution of the state perception module, data transmission and processing must meet certain timeliness requirements. The state of the fork headset can change rapidly during operation, so the timely acquisition and transmission of real-time signals is crucial to ensure that any signs of wear are detected. Furthermore, communication with the monitoring system's control terminal must remain stable to ensure timely access to the latest standard reference data to meet monitoring needs in various situations.

[0041] The state perception module also needs to have a certain degree of anti-interference capability. In actual operating environments, various factors such as electromagnetic interference and mechanical vibration may affect the accuracy of sensor signals and the stability of communication. Therefore, when designing the state perception module, appropriate anti-interference measures such as signal filtering and shielding are required to ensure the authenticity and reliability of the collected data.

[0042] Different models of front fork headsets may have different structures and operating parameters. Therefore, when the state perception module establishes a communication connection with the sensor and the control end, it needs to be able to automatically adjust the communication parameters and data processing methods according to the model of the front fork headset to ensure that the monitoring data of different models of front fork headsets can be correctly received and processed.

[0043] During data reception, the state perception module also performs preliminary verification and screening of the data. Obvious abnormal data, such as those outside the sensor's measurement range or with incorrect data formats, must be marked or removed to prevent these invalid data from adversely affecting subsequent feature extraction and wear identification processes.

[0044] The state perception module needs to record the received and processed data in real time for subsequent query and analysis. The recorded data should include real-time signal data, preset reference data, data reception time, communication status, and other information to form a complete monitoring data record, providing a foundation for subsequent data storage and analysis.

[0045] The state perception module, through communication with sensors and the control terminal, collects, receives, and classifies real-time signals and standard reference data from the fork headset during operation. This provides accurate and reliable data support for subsequent modules such as feature extraction and wear identification, and serves as a key data input link for the entire online fork headset wear monitoring system. During execution, it is crucial to ensure the real-time, accuracy, and integrity of the data, as well as the stability and anti-interference capabilities of the communication, to guarantee the normal operation of the entire monitoring system and the reliability of the monitoring results.

[0046] Example 3: The execution process of the wear identification module in this embodiment is as follows: The wear identification module needs to filter historical monitoring result data. It selects the first historical monitoring result data whose time domain root mean square index exceeds the allowable range, while the displacement peak-to-peak index and the temperature mean index are both within their respective allowable ranges. The allowable range of indicators here is pre-set based on the normal operating status of the front fork headset and is used to determine whether each indicator is within the normal range. During the screening process, the time domain root mean square index, displacement peak-to-peak index, and temperature mean index in each historical monitoring result data item are compared one by one to determine whether they meet the screening criteria.

[0047] After filtering the first set of historical monitoring results, count the wear types whose trigger frequencies exceed the allowable frequency range and define them as time-domain deviation high-frequency wear types. The trigger frequency here refers to the ratio of the number of times a wear type appears in historical monitoring results to the total number of monitoring times, while the allowable frequency range is the normal frequency range pre-set based on actual operating experience and equipment characteristics. By counting wear types whose trigger frequencies exceed the allowable range, it is possible to identify the more common wear types when the time-domain RMS indicator is abnormal but other indicators are normal, thus providing a basis for subsequent wear identification.

[0048] The wear identification module also needs to traverse the time domain root mean square index, displacement peak-to-peak index, and temperature mean index to match the correlation of wear types. This means that it is necessary to analyze the correspondence between each index and a specific wear type to determine the possible wear type when a certain index is abnormal. For example, an abnormality in the time domain root mean square index may be associated with a specific vibration wear type, an abnormality in the displacement peak-to-peak index may be associated with a displacement-related wear type, and an abnormality in the temperature mean index may be associated with a thermal wear type, thereby obtaining the time domain correlation wear type, displacement correlation wear type, and temperature correlation wear type.

[0049] After obtaining the three correlated wear types, the time-domain correlated wear type, displacement-correlated wear type, and temperature-correlated wear type need to be sorted in descending order based on the time-domain RMS, displacement-peak-to-peak, and temperature-mean values ​​to obtain the wear type sorting results. The purpose of sorting is to determine which wear types require priority attention and treatment when multiple indicators are abnormal. For example, if the time-domain RMS value is the highest, it means that the abnormality of this indicator is the most serious, and the corresponding time-domain correlated wear type will be placed at the top of the sorting.

[0050] The discriminant decision unit then determines the wear status of the fork headset operating stages marked with potential wear links based on the wear type ranking results, obtaining a first stored result. The discriminant decision unit, pre-configured with corresponding discrimination rules and logic, comprehensively evaluates the fork headset operating stages based on the wear type ranking results and other relevant factors, such as wear severity and development trends, to determine their wear status. The unit then stores the relevant data in a specified format and requirements, forming the first stored result.

[0051] Throughout the wear identification module, data screening and statistics must be accurate. The integrity and accuracy of historical monitoring data must be ensured, as this data is crucial for determining and ranking wear types. When screening the initial historical monitoring data, it must be done strictly within the established indicator range to avoid errors in subsequent judgments due to deviations in the screening criteria.

[0052] When performing wear type correlation matching, it is necessary to establish an accurate correspondence between indicators and wear types based on a large amount of historical data and actual operating experience. This correspondence may need to be determined through the analysis and summary of a large number of failure cases to ensure the accuracy of the correlation matching.

[0053] When sorting wear types, in addition to considering the highest or lowest indicator values, it's also important to consider the impact of any abnormalities on the fork headset's operation. For example, even if a certain indicator's value isn't the highest, if its abnormality significantly impacts the safe operation of the equipment, it may be prioritized higher in the sorting process.

[0054] The judgment rules and logic of the decision-making unit must be fully verified and optimized to ensure the accuracy and reliability of wear status judgment. In actual application, the judgment rules and logic may need to be continuously adjusted and improved based on new monitoring data and operating experience to improve the accuracy of wear judgment.

[0055] The wear identification module also needs to be real-time, capable of identifying wear characteristics from the currently collected data, so that potential wear issues can be identified and appropriate measures can be taken. When processing real-time data, it is necessary to ensure that the data transmission and processing speeds meet the requirements of real-time monitoring to avoid delays in processing that could lead to wear issues not being discovered in time.

[0056] The wear identification module also needs to have a certain degree of fault tolerance and be able to handle some data anomalies or uncertainties. For example, when the values ​​of certain indicators are close to the limit of the allowable range, it is necessary to consider other factors when making judgments, rather than simply judging by the threshold, to avoid misjudgments.

[0057] After obtaining the first stored result, it needs to be accurately transferred to the data storage module for storage to ensure data traceability and facilitate subsequent analysis. The stored data should include information such as the wear type sorting results, the judgment basis, and the judgment time, so that subsequent wear conditions can be queried and analyzed.

[0058] The wear identification module accurately assesses the wear status of the fork headset during operation by filtering and compiling historical data, matching and sorting wear types, and performing comprehensive assessments using the decision-making unit. This provides a crucial basis for subsequent data storage and early warning. During implementation, the accuracy, real-time nature, and reliability of each step must be ensured to guarantee the effectiveness of the entire monitoring system.

[0059] Example 4: The execution process of the result output module and the data storage module in this embodiment is as follows: When the wear characteristic data is within the characteristic allowable range, the result output module will call the preset normal state parameters. These normal state parameters are pre-set according to various indicators during the normal operation of the front fork bowl assembly, such as the normal vibration frequency range of the vibration monitoring unit, the allowable displacement fluctuation amplitude of the displacement monitoring unit, and the suitable working temperature range of the temperature monitoring unit. Taking a certain model of front fork bowl assembly as an example, during its normal operation, the normal state parameters of the vibration monitoring unit may be set to a vibration frequency between 20-50Hz, the normal state parameters of the displacement monitoring unit are a displacement fluctuation value not exceeding 0.5mm, and the normal state parameters of the temperature monitoring unit are a temperature between -20℃ and 60℃.

[0060] Next, the result output module sets the parameters of the vibration monitoring unit, displacement monitoring unit, and temperature monitoring unit of the front fork headset according to the retrieved normal state parameters. For example, when the current parameter setting of the vibration monitoring unit does not match the normal state parameters, the result output module will adjust it to the normal state parameter range to ensure that each monitoring unit operates with normal parameters, thereby continuously and accurately collecting various signals during the operation of the front fork headset. After completing the parameter setting, the result output module generates a second storage result, which is used to record relevant data when the front fork headset is in a normal state, including normal state parameters, setting parameters of each monitoring unit, current wear characteristic data, and other information.

[0061] The execution process of the data storage module includes receiving the real-time vibration signal, displacement signal, temperature signal and preset reference data obtained by the state perception module. For example, the state perception module receives the vibration signal of the front fork headset at a certain moment, which is in the time domain sequence of [10, 12, 15, 8, 11] (unit: mm / s). 2 ), the displacement signal fluctuation value is 0.3mm, the temperature signal change curve rises slowly from 25℃ to 28℃ in the past 10 minutes, and the standard vibration sequence received in the preset reference data is [8-15mm / s 2 ] fluctuation range, the standard displacement value is no more than 0.5mm, and the standard temperature curve is in the range of 20-30℃. The data storage module will receive all these real-time signals and preset reference data.

[0062] In addition, the data storage module also receives the first and second stored results output by the result feedback module. For example, if the wear characteristic data exceeds the permitted range, the first stored result output by the result feedback module will include information such as the potential wear link marker, wear characteristic data, and wear type ranking results. If the wear characteristic data is within the permitted range, the second stored result output will include information such as the normal state marker, normal state parameters, and setting parameters for each monitoring unit. The data storage module will receive these results together.

[0063] After receiving all of the aforementioned data, the data storage module categorizes and stores the real-time vibration signal, displacement signal, temperature signal, preset reference data, first stored result, and second stored result into different data partitions. For example, the real-time vibration signal, displacement signal, and temperature signal are stored in the real-time monitoring data partition, the preset reference data is stored in the standard parameter partition, and the first stored result and second stored result are stored in the abnormal state data partition and normal state data partition, respectively. This categorized storage method creates a complete wear monitoring record database, facilitating subsequent querying, analysis, and tracing of the operating status of the fork headset.

[0064] Taking the monitoring data of the front fork headset in a certain period as an example, the time domain sequence of the real-time vibration signal received by the data storage module is [11, 13, 12, 10, 14] (unit: mm / s 2 ), the displacement signal fluctuation value is 0.2mm, the temperature signal change curve is maintained between 22-25℃ within 30 minutes, and the standard vibration sequence in the preset reference data is 8-16mm / s 2 , the standard displacement value is ≤0.5mm, and the standard temperature curve is 20-35℃. Since the wear characteristic data is within the characteristic allowable range, the result output module calls the normal state parameters and sets the parameters of the vibration monitoring unit to monitor 8-16mm / s 2 The vibration range is set to 0.5 mm or less for the displacement monitoring unit, and 20-35°C for the temperature monitoring unit. A second storage result is generated and transmitted to the data storage module. The data storage module stores the real-time vibration, displacement, and temperature signals in the real-time monitoring data partition, the preset reference data in the standard parameter partition, and the second storage result in the normal state data partition, thus forming a complete monitoring data record for that period in the wear monitoring record database.

[0065] Throughout the execution process, the result output module must ensure that the retrieved general state parameters match the fork headset model and usage scenario. Different fork headset models may have different general state parameters. For example, the vibration general state parameters of a fork headset used in heavy machinery may differ from those used in lightweight equipment. In different usage scenarios, such as high temperature or high vibration environments, general state parameters may also need to be adjusted accordingly to ensure parameter settings are accurate.

[0066] The data storage module must ensure data integrity and reliability when receiving and storing data. Each piece of data received must be verified to ensure the correct format, complete content, and no data loss or errors. Furthermore, the data storage module must provide efficient data storage and retrieval capabilities to quickly retrieve the required monitoring data when needed.

[0067] Furthermore, the data storage module regularly maintains and manages the wear monitoring database, such as clearing outdated data and backing up important data, to ensure proper database operation and data security. This provides a solid data foundation for long-term monitoring and analysis of fork headset wear. This allows maintenance personnel to analyze historical data to understand the wear patterns and trends of the fork headset, providing a basis for equipment maintenance and servicing.

[0068] In short, when the wear characteristic data is normal, the result output module generates a second storage result by retrieving and setting the normal status parameters. The data storage module is responsible for receiving and classifying and storing various monitoring data and results. The two work together to ensure the accurate recording and effective management of the front fork headset operating status data, providing reliable data support for the subsequent analysis and application of the entire monitoring system.

[0069] Example 5: The execution process of the early warning module in this embodiment is as follows: When the wear identification module marks the potential wear link of the front fork headset operation link, the early warning module will call the preset early warning threshold range. These early warning threshold ranges are pre-set based on the structural characteristics, material properties and actual operating experience of the front fork headset to determine whether the wear characteristic data exceeds the normal range and needs to issue an early warning. For example, the early warning threshold range of a certain model of front fork headset may be set to a time domain root mean square index exceeding 80mm. 2 / s 4 , the peak-to-peak displacement index exceeds 0.8mm, and the temperature average index exceeds 70℃.

[0070] The early warning module compares the wear characteristic data with the warning threshold range. Assume that the wear characteristic data of a certain fork bowl group currently monitored is the time domain root mean square index of 85mm. 2 / s 4 , the peak-to-peak displacement index is 0.6mm, the average temperature index is 65℃, and the time domain root mean square index is 85mm 2 / s 4 Exceeds the preset 80mm 2 / s 4 If the displacement peak-to-peak value index and the temperature mean value index do not exceed the threshold, the early warning prompt module will generate an early warning signal.

[0071] The generated warning signal is sent to the monitoring terminal's audio and visual prompt device, triggering an early warning response. For example, the monitoring terminal's audio and visual prompt device may emit a flashing red light and a buzzer alarm to alert relevant personnel that there may be potential wear issues with the front fork headset.

[0072] Taking another scenario as an example, during the operation of the front fork headset, the state perception module collects abnormal fluctuations in the time domain sequence of the vibration signal. After processing by the feature extraction module, the time domain root mean square index obtained is 90mm. 2 / s 4 , the peak-to-peak displacement index is 0.7mm, and the temperature average index is 60℃. Based on these data, the wear identification module determines that the time domain root mean square index exceeds the allowable range of the index and marks the current operation link as a potential wear link. At this time, the early warning prompt module is triggered and the preset early warning threshold range is called. The early warning threshold of the time domain root mean square index is 85mm2 / s 4 . Set the current time domain RMS index to 90mm 2 / s 4 With warning threshold 85mm 2 / s 4 After comparison, if it is found that the threshold is exceeded, an early warning signal is generated and sent to the sound and light prompt device of the monitoring terminal, causing the light of the monitoring terminal to flash and an alarm to sound to alert the operator.

[0073] Throughout the early warning module's execution, the preset warning threshold range needs to be adjusted based on different fork headset models and different usage scenarios. For example, for fork headsets used in high-load industrial environments, the warning threshold range may be set relatively high to avoid frequent warning triggers; while in scenarios with high operating precision requirements, the warning threshold range may be set relatively low to detect potential wear issues earlier.

[0074] When retrieving the warning threshold range, the warning module needs to accurately identify the current fork headset model and usage scenario to ensure that the threshold range matches the actual situation. This requires the warning module to interact with other modules in the system to obtain relevant information about the fork headset, such as model and operating environment.

[0075] When comparing wear signature data against the warning threshold range, data accuracy and real-time performance must be ensured. Because the operating status of the fork headset changes in real time, the wear signature data also changes dynamically. Therefore, the warning module must promptly obtain the latest wear signature data and perform a real-time comparison to ensure that threshold violations can be detected and a warning signal generated.

[0076] Once a warning signal is generated, the process of sending it to the monitoring terminal's audio and visual prompt device must maintain stable communication to avoid signal transmission delays or loss, ensuring that the warning response is triggered in a timely manner. The audio and visual prompt device at the monitoring terminal must have a clear prompt effect and can be clearly perceived by operators in noisy working environments.

[0077] Furthermore, the warning module also records relevant information about the warning event, such as the time the warning occurred, the specific values ​​of the wear characteristic data, and the warning threshold range. This information is then transmitted to the data storage module for storage and subsequent analysis. By analyzing historical warning events, we can understand the patterns and trends of fork headset wear, providing a reference for equipment maintenance and servicing.

[0078] For example, if a certain fork headset triggers the time domain root mean square indicator warning multiple times over a period of time, by checking the wear characteristic data and operating time and other information in the warning record, maintenance personnel can analyze that the fork headset may be prone to abnormal vibration under specific operating conditions, and thus conduct targeted inspections and maintenance to prevent the problem from further expansion.

[0079] When designing the early warning module, it's also important to consider avoiding false alarms. For example, if wear characteristic data only briefly exceeds the warning threshold, rather than continuously, a delay may be necessary to prevent unnecessary warnings from being triggered by momentary signal fluctuations, potentially disrupting normal operation.

[0080] At the same time, the early warning module also needs to be able to interact with other modules. For example, when a warning signal is triggered, in addition to emitting audio and visual prompts, it can also automatically notify the relevant maintenance management system, generate a maintenance work order, and remind maintenance personnel to inspect and handle the problem, thereby improving equipment maintenance efficiency.

[0081] When the Wear Identification Module flags a potential wear link, the Early Warning Module retrieves the preset warning threshold range and compares it with the wear signature data. When the threshold is exceeded, a warning signal is generated and sent to the monitoring terminal's audio and visual prompts, providing timely warning of potential wear issues on the fork headset. This process requires ensuring the appropriate setting of warning thresholds, accurate and real-time data comparison, timely transmission of warning signals, and complete recording of warning information. This ensures the safe operation of the fork headset, allowing relevant personnel to take timely measures to prevent further wear issues and reduce equipment failures and downtime.

[0082] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0083] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An online monitoring system for front fork headset wear, characterized in that: include: The state perception module is used to obtain real-time vibration signals, displacement signals, and temperature signals during the operation of the front fork headset; a feature extraction module, configured to perform preprocessing and feature extraction on the real-time vibration signal, the displacement signal, and the temperature signal through a signal processing component to obtain wear feature data; A wear identification module is configured to mark a potential wear link in the current operation link of the front fork headset when the wear feature data exceeds an allowable feature range; A data storage module is configured to classify and store the data related to the operation link of the front fork headset having the potential wear link mark through a storage management device to obtain a first storage result; A result output module is configured to mark the current operation link of the front fork headset as normal when the wear characteristic data is within the characteristic allowable range, and obtain a second storage result; A result feedback module is configured to output the first stored result or the second stored result to a monitoring terminal.

2. The front fork headset wear online monitoring system according to claim 1, characterized in that: The execution process of the feature extraction module includes: Compare the vibration signal time domain sequence, displacement signal fluctuation value, and temperature signal change curve with the standard vibration sequence, standard displacement value, and standard temperature curve to obtain the time domain root mean square index, displacement peak-to-peak index, and temperature mean index; constructing a wear characteristic data set according to the time domain root mean square index, the displacement peak-to-peak index, and the temperature mean index; The wear characteristic data set is input into the signal processing component, and the wear characteristic data is output.

3. The front fork headset wear online monitoring system according to claim 2, characterized in that: The execution process of the state perception module includes: Establishing a communication connection with a vibration sensor, a displacement sensor, and a temperature sensor installed on the front fork headset and receiving a vibration signal time domain sequence, a displacement signal fluctuation value, and a temperature signal change curve during the operation of the front fork headset; Establishing a communication connection with the monitoring system control terminal and receiving the standard vibration sequence, standard displacement value, and standard temperature curve monitored by the fork headset; The vibration signal time domain sequence, the displacement signal fluctuation value, and the temperature signal change curve are classified into the real-time vibration signal, the displacement signal, and the temperature signal; The standard vibration sequence, the standard displacement value, and the standard temperature curve are included in the preset reference data for monitoring the front fork bowl assembly.

4. The front fork headset wear online monitoring system according to claim 3, characterized in that: The execution process of the feature extraction module also includes: Setting a feature evaluation rule based on the fork headset model, the feature evaluation rule is used to quantify the degree of difference between the vibration signal time domain sequence and the standard vibration sequence, the displacement signal fluctuation value and the standard displacement value, and the temperature signal change curve and the standard temperature curve; According to the characteristic evaluation rules, the vibration signal time domain sequence, the displacement signal fluctuation value, and the temperature signal change curve are compared with the standard vibration sequence, the standard displacement value, and the standard temperature curve to obtain the time domain root mean square index, the displacement peak-to-peak value index, and the temperature mean index.

5. The front fork headset wear online monitoring system according to claim 4, characterized in that: The execution process of the feature extraction module also includes: According to the current front fork headset usage scenario, calling the corresponding signal processing component to process the wear characteristic data set to obtain the wear characteristic data; The signal processing component construction process includes: Collecting historical monitoring record data, wherein the historical monitoring record data includes historical vibration signals, historical displacement signals, and historical temperature signals; Constructing a historical feature data set according to the historical vibration signal, the historical displacement signal, and the historical temperature signal; Using the historical feature data set as a reference, collect historical monitoring result data, and count the proportion of misjudgment record data in the historical monitoring result data, which is set as the feature misjudgment probability identification value; The signal processing component is generated by training using the feature misjudgment probability identification value as a training basis and the historical feature data set as an input sample.

6. The front fork headset wear online monitoring system according to claim 3, characterized in that: The execution process of the wear identification module includes: Filtering first historical monitoring result data whose time domain root mean square index exceeds the allowable index range, and whose displacement peak-to-peak index and temperature mean index do not exceed the allowable index range; The wear type whose trigger frequency exceeds the frequency allowable range in the first historical monitoring result data is counted and set as the time domain deviation high frequency wear type.

7. The front fork headset wear online monitoring system according to claim 6, characterized in that: The execution process of the wear determination module also includes: Traversing the time domain root mean square index, the displacement peak-to-peak index, and the temperature mean index to perform wear type correlation matching to obtain a time domain correlation wear type, a displacement correlation wear type, and a temperature correlation wear type; Sort the time-domain associated wear type, the displacement associated wear type, and the temperature associated wear type according to the time-domain root mean square index, the displacement peak-to-peak index, and the temperature mean index in descending order to obtain a wear type sorting result; The determination and decision unit determines the wear status of the front fork headset operation link having the potential wear link mark according to the wear type sorting result to obtain the first stored result.

8. The front fork headset wear online monitoring system according to claim 1, characterized in that: The execution process of the result output module includes: When the wear characteristic data is within the characteristic allowable range, calling the preset normal state parameters; According to the normal state parameters, parameters of the vibration monitoring unit, the displacement monitoring unit, and the temperature monitoring unit of the front fork headset are set to generate the second storage result.

9. The front fork headset wear online monitoring system according to claim 1, characterized in that: Also included is a data storage module, wherein the data storage module is used to: receiving the real-time vibration signal, the displacement signal, the temperature signal, and the preset reference data acquired by the state sensing module; receiving the first stored result and the second stored result output by the result feedback module; The real-time vibration signal, the displacement signal, the temperature signal, the preset reference data, the first storage result and the second storage result are classified and stored in different data partitions to form a wear monitoring record database.

10. The front fork headset wear online monitoring system according to claim 1, characterized in that: It also includes an early warning prompt module, and the execution process of the early warning prompt module includes: When the wear identification module marks the potential wear link of the front fork headset operation link, calling the preset warning threshold range; Comparing the wear characteristic data with the warning threshold range, and generating a warning signal if the threshold is exceeded; The warning signal is sent to the sound and light prompt device of the monitoring terminal to trigger a warning response.

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