Multi-source data analysis gear reducer performance optimization method

Abstract

The invention provides a gear reducer performance optimization method based on multi-source data analysis, and relates to the technical field of performance optimization, and the method comprises the steps: carrying out the real-time monitoring of a gear reducer, and obtaining multi-source monitoring data; self-adaptive sensing abnormal influence correction is carried out on the multi-source monitoring data, and a speed reducer monitoring result is generated; performing transmission characteristic identification and integration, and establishing a transmission characteristic monitoring matrix; performing transmission performance deviation optimization on the gear reducer in combination with the transmission characteristic monitoring matrix to obtain a first strategy of reducer performance optimization; performing life loss prediction on the gear reducer to obtain a life loss prediction result; performing service life performance compensation optimization on the first speed reducer performance optimization strategy to generate a second speed reducer performance optimization strategy; and the performance of the gear reducer is optimized. According to the invention, the technical problems of low monitoring and maintenance precision and high singleness of the gear reducer in the prior art can be solved, and the technical effects of improving the monitoring precision and realizing diversified monitoring are achieved.

Description

Technical Field

[0001] This application relates to the technical field of performance optimization, and particularly to a method for optimizing the performance of a gear reducer through multi-source data analysis. Background Art

[0002] With the continuous development of mechanical equipment and industrial production, as one of the core transmission components, the reducer is widely used in various industrial equipment, such as automated production lines, power, petrochemical, mining and other fields. During operation, the reducer bears a large load and a complex working environment. Therefore, the stability and reliability of its performance are crucial for the overall operation of the equipment. However, there are still many deficiencies in the existing technologies for monitoring, fault prediction and performance optimization of the reducer, which urgently need to be improved.

[0003] Currently, most traditional monitoring methods rely on a single sensor and are difficult to comprehensively and accurately reflect all working parameters of the reducer. For example, vibration sensors can effectively detect mechanical failures, but have poor recognition ability for problems such as overheating and poor lubrication. In addition, although some advanced prediction methods have been proposed, such as prediction models based on machine learning and data-driven analysis methods, these technologies still face problems such as low data quality and poor model generalization ability in practical applications. Most prediction models often rely on historical data for training, and in the actual production environment, factors such as data incompleteness and noise interference often affect the accuracy of the model, resulting in inaccurate prediction results or delayed fault warnings. Therefore, the existing reducer life prediction technologies have the disadvantages of low accuracy and poor real-time performance, and it is difficult to achieve accurate fault warnings and life predictions.

[0004] In summary, there is a technical problem in the existing technology that due to the low monitoring and maintenance accuracy and strong singularity of the gear reducer, the performance of the gear reducer decreases. Summary of the Invention

[0005] The purpose of this application is to provide a method for optimizing the performance of a gear reducer through multi-source data analysis, so as to solve the technical problem in the existing technology that due to the low monitoring and maintenance accuracy and strong singularity of the gear reducer, the performance of the gear reducer decreases.

[0006] In view of the above problems, the present application provides a method for optimizing the performance of a gear reducer through multi-source data analysis, including: performing real-time monitoring on the gear reducer according to a pre-deployed multi-source sensing array to obtain multi-source monitoring data; performing adaptive correction of the influence of sensing anomalies on the multi-source monitoring data according to a sensing anomaly correction model to generate a monitoring result of the reducer; performing transmission feature recognition and integration according to the monitoring result of the reducer to establish a transmission feature monitoring matrix; reading the control scheme of the gear reducer, and combining the transmission feature monitoring matrix to optimize the deviation of the transmission performance of the gear reducer to obtain a first strategy for optimizing the performance of the reducer; based on the monitoring result of the reducer, predicting the life loss of the gear reducer according to the life loss prediction channel of the reducer to obtain a life loss prediction result; performing life performance compensation optimization on the first strategy for optimizing the performance of the reducer based on the life loss prediction result to generate a second strategy for optimizing the performance of the reducer; and optimizing the performance of the gear reducer according to the second strategy for optimizing the performance of the reducer.

[0007] The technical solution provided in the present application has at least the following technical effects or advantages: by performing real-time monitoring on the gear reducer according to a pre-deployed multi-source sensing array to obtain multi-source monitoring data; performing adaptive correction of the influence of sensing anomalies on the multi-source monitoring data according to a sensing anomaly correction model to generate a monitoring result of the reducer; performing transmission feature recognition and integration according to the monitoring result of the reducer to establish a transmission feature monitoring matrix; reading the control scheme of the gear reducer, and combining the transmission feature monitoring matrix to optimize the deviation of the transmission performance of the gear reducer to obtain a first strategy for optimizing the performance of the reducer; based on the monitoring result of the reducer, predicting the life loss of the gear reducer according to the life loss prediction channel of the reducer to obtain a life loss prediction result; performing life performance compensation optimization on the first strategy for optimizing the performance of the reducer based on the life loss prediction result to generate a second strategy for optimizing the performance of the reducer; and optimizing the performance of the gear reducer according to the second strategy for optimizing the performance of the reducer. That is to say, by achieving the technical goal of improving the monitoring accuracy of the gear reducer and multi-dimensional fault detection, the technical effects of improving the monitoring accuracy, realizing diversified monitoring and intelligent maintenance are achieved.

[0008] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically describes the embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Description of the Drawings

[0009] To more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0010] Figure 1 It is a schematic flowchart of the gear reducer performance optimization method for multi-source data analysis in the present application;

[0011] Figure 2 It is a schematic flowchart of obtaining the first strategy for gear reducer performance optimization in the gear reducer performance optimization method for multi-source data analysis in the present application. Detailed implementation manners

[0012] By providing a gear reducer performance optimization method for multi-source data analysis, the present application solves the technical problem in the prior art that due to the low monitoring and maintenance accuracy and strong singularity of the gear reducer, the performance of the gear reducer declines. It realizes the technical goal of improving the monitoring accuracy of the gear reducer and multi-dimensional fault detection, and achieves the technical effect of improving the monitoring accuracy, realizing diversified monitoring and intelligent maintenance.

[0013] Next, the technical solutions in the present application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0014] Please refer to the attached Figure 1 , the present application provides a gear reducer performance optimization method for multi-source data analysis, which specifically includes the following steps:

[0015] Step 1: Perform real-time monitoring on the gear reducer according to the pre-deployed multi-source sensor array to obtain multi-source monitoring data.

[0016] Specifically, based on the pre-arranged multi-source sensing array, the real-time monitoring of the operating state of the gear reducer is achieved. Pre-arrangement means installing sensors in advance at key parts or important positions of the gear reducer, and these sensors can collect various types of signals. The multi-source sensing array represents a set composed of different types of sensors, such as vibration sensors, temperature sensors, and acoustic emission sensors, etc. These sensors work independently but cooperate with each other to provide more comprehensive data. Through the multi-source sensing array, the operating state of the gear reducer can be monitored in real time. The operation of the multi-source sensing array is immediate, and data can be quickly acquired and analyzed to timely detect potential problems. In specific operations, for example, five vibration sensors, two temperature sensors, and one acoustic emission sensor are arranged to collect different signals respectively. Further, the vibration sensor can record the vibration frequency and amplitude during the operation of the reducer, the temperature sensor can measure the temperature change of the bearing part, and the acoustic emission sensor can capture the high-frequency noise signal generated by the internal gear meshing. The data provided by these sensors together constitute the multi-source monitoring data, which reflects the health state of the gear reducer from different angles.

[0017] Step 2: Perform adaptive sensing anomaly impact correction on the multi-source monitoring data according to the sensing anomaly correction model to generate the reducer monitoring result.

[0018] Specifically, perform adaptive sensing anomaly impact correction on the multi-source monitoring data according to the sensing anomaly correction model, adjust and correct the monitoring data collected from different sensors to eliminate the errors and biases that may occur in the sensors under abnormal conditions, and generate the reducer monitoring result. Automatically adjust its correction method according to different data inputs to adapt to different monitoring environments and sensor states, and finally obtain a more accurate reducer monitoring result to more truly reflect the operating state of the reducer, help the operation and maintenance personnel timely detect potential faults and handle them, thereby improving the reliability and safety of the equipment.

[0019] Step 3: Identify and integrate the transmission characteristics according to the reducer monitoring result to establish a transmission characteristic monitoring matrix.

[0020] Specifically, by analyzing the monitoring data obtained from the reducer, identify the key characteristics related to the transmission of the reducer, including data indicators such as vibration frequency, temperature change, and rotational speed. These characteristics reflect the transmission state of the reducer during operation. Then, integrate the identified characteristic information to form a transmission characteristic monitoring matrix, arrange different transmission characteristics into a data set, which contains the transmission characteristic information of each operating moment of the reducer. The operation and maintenance personnel can more conveniently compare, analyze, and judge the overall operating state of the reducer, timely detect possible transmission problems, and improve the equipment maintenance efficiency.

[0021] Step 4: Read the control scheme of the gear reducer, and optimize the transmission performance deviation of the gear reducer in combination with the transmission characteristic monitoring matrix to obtain the first strategy for optimizing the reducer performance.

[0022] Specifically, read the control scheme of the gear reducer. The control scheme refers to the operation rules and parameter settings formulated to ensure the efficient and stable operation of the gear reducer during the working process, including the start, stop, load adjustment during operation, temperature control, and possible fault protection measures of the reducer. For example, the reducer automatically decelerates or stops under high temperature or overload conditions to avoid damage.

[0023] By analyzing the transmission characteristic monitoring matrix of the reducer, evaluate the performance parameters of the reducer during operation, and identify the deviation from the normal working state. For example, if the monitoring data shows that the temperature of the reducer continues to be too high or the vibration is too large, it indicates that there may be problems with its transmission performance. Combine this deviation information to perform transmission performance deviation optimization, and restore the normal transmission performance of the reducer by adjusting the control strategy, improving the operating conditions, or optimizing the maintenance plan, etc. Finally, generate the first strategy for optimizing the reducer performance, which is a specific solution for dealing with the performance deviation of the reducer, and then improve the operating efficiency and reliability of the reducer through effective optimization means.

[0024] Step 5: Based on the monitoring results of the reducer, predict the life loss of the gear reducer according to the life loss prediction channel of the reducer to obtain the life loss prediction result.

[0025] Specifically, by analyzing the monitoring results of the reducer, use the life loss prediction channel of the reducer to predict the life loss of the gear reducer, predict the possible life loss of the reducer in the future, and obtain the life loss prediction result. The monitoring results of the reducer include various data during the operation of the reducer, such as temperature, vibration, rotational speed, etc., which are used to identify the operating state of the reducer and perform corresponding analysis. The life loss prediction channel is a prediction model or calculation method that combines the historical data of the reducer and the operating environment, and is used to predict the life loss situation of the reducer. The life loss prediction result will show the possible service life loss of the reducer within a certain period in the future. For example, if the vibration frequency of the reducer is high, it may cause premature wear, and the prediction model will estimate the degree of life loss based on this information and provide a specific prediction result.

[0026] Step 6: Based on the life loss prediction result, perform life performance compensation optimization on the first strategy for optimizing the reducer performance to generate the second strategy for optimizing the reducer performance.

[0027] Specifically, based on the life loss prediction results, perform life performance compensation optimization on the first strategy for the performance optimization of the speed reducer. By adjusting the operating parameters of the speed reducer, improving the maintenance cycle, optimizing the load control, etc., to reduce the impact of life loss on performance, and generate the second strategy for the performance optimization of the speed reducer, so as to make up for the possible impact caused by life loss. The second strategy for the performance optimization of the speed reducer can more effectively cope with the impact brought by life loss, thereby extending the overall service life of the equipment.

[0028] Step 7: Optimize the performance of the gear speed reducer according to the second strategy for the performance optimization of the speed reducer.

[0029] Specifically, apply the second strategy for the performance optimization of the speed reducer that has been generated to the actual operation of the speed reducer. By adjusting the operating conditions of the speed reducer, such as reducing the load, optimizing the speed, improving the lubrication, etc., to improve its performance, improve the working efficiency of the speed reducer, reduce the failure rate and extend the service life of the equipment.

[0030] The method for optimizing the performance of the gear speed reducer by multi-source data analysis can achieve the technical goals of improving the monitoring accuracy of the gear speed reducer and multi-dimensional fault detection, and achieve the technical effects of improving the monitoring accuracy, realizing diversified monitoring and intelligent maintenance.

[0031] Further, please refer to the appendix Figure 2 , this application further includes: performing the identification of the expected deviation of the transmission characteristics of the gear speed reducer according to the control scheme and the transmission characteristic monitoring matrix to obtain the transmission deviation identification result; performing the evaluation of the impact on the transmission performance according to the transmission deviation identification result to obtain the transmission performance impact coefficient; judging whether the transmission performance impact coefficient is greater than or equal to the transmission performance impact threshold; if the transmission performance impact coefficient is greater than or equal to the transmission performance impact threshold, activating the transmission performance deviation optimization map; inputting the transmission deviation identification result into the transmission performance deviation optimization map to generate the first strategy for the performance optimization of the speed reducer.

[0032] Specifically, perform the identification of the expected deviation of the transmission characteristics of the gear speed reducer according to the control scheme and the transmission characteristic monitoring matrix to judge whether the transmission characteristics of the current speed reducer deviate from the expected value in the normal operating state, and detect whether there are abnormal situations that do not meet the expectations during the operation of the speed reducer, so as to obtain the transmission deviation identification result. For example, the vibration of the speed reducer exceeds the normal range or the speed is too low.

[0033] Perform the evaluation of the impact on the transmission performance according to the transmission deviation identification result, conduct the impact assessment of the transmission performance, obtain the transmission performance impact coefficient, which is an index used to quantify the degree of performance decline or deviation, and determine whether the operating condition of the speed reducer has reached the critical value of warning or failure. For example, the greater the degree of deviation of the parameter, the higher the impact coefficient, and vice versa.

[0034] Judge whether the transmission performance influence coefficient is greater than or equal to the transmission performance influence threshold, and judge the calculated transmission performance influence coefficient to confirm whether it exceeds the preset threshold. If the transmission performance influence coefficient is greater than or equal to the transmission performance influence threshold, it indicates that there may already be relatively serious performance problems with the transmission components, activate the transmission performance deviation optimization map, and take further optimization or adjustment measures. For example, the transmission performance deviation optimization map may include adjusting operating parameters, replacing components, or performing other maintenance operations to restore the reducer to its optimal operating state.

[0035] Input the transmission deviation recognition result into the transmission performance deviation optimization map. Use the transmission deviation recognition result as input and generate specific performance optimization suggestions for the reducer in combination with the optimization map. The transmission performance deviation optimization map contains optimization measures for different deviation situations. The transmission performance deviation optimization map can help the operation and maintenance personnel find the best adjustment plan to restore the normal performance of the reducer. For example, if the load increase caused by the wear of the transmission components, the optimization map may recommend adjusting the load distribution or replacing the component to improve the performance.

[0036] Furthermore, this application also includes: performing a normal parameter retrieval of the transmission characteristics of the gear reducer according to the control scheme to obtain a transmission characteristic parameter retrieval set; performing a central tendency analysis according to the transmission characteristic parameter retrieval set to establish a transmission characteristic expectation matrix; and performing a deviation recognition on the transmission characteristic monitoring matrix according to the transmission characteristic expectation matrix to generate the transmission deviation recognition result.

[0037] Specifically, perform a normal parameter retrieval of the transmission characteristics of the gear reducer according to the control scheme of the gear reducer, search for the typical parameters that should appear under normal operating conditions, and obtain the transmission characteristic parameter retrieval set. The transmission characteristics include various important data during the operation of the reducer, such as speed, load, temperature, and vibration, which are used to evaluate the performance of the reducer. By performing a normal parameter retrieval of the transmission characteristics, a transmission characteristic parameter retrieval set can be obtained, which contains the standard values of each transmission characteristic when the reducer operates in a healthy state.

[0038] Perform a central tendency analysis according to the transmission characteristic parameter retrieval set to establish a transmission characteristic expectation matrix. Central tendency analysis is to calculate statistical quantities such as the average value and median of the transmission characteristic parameters to determine the typical range and expected values of the parameters under normal conditions. Establishing the transmission characteristic expectation matrix helps to obtain the typical behavior of each transmission characteristic when the reducer operates under various conditions.

[0039] Identify the deviation of the drive feature monitoring matrix according to the drive feature expectation matrix. By comparing the standard values in the drive feature expectation matrix with the actual monitoring data, identify the drive features with deviations and generate the drive deviation identification result, indicating that the operating parameters of the reducer are abnormal, which helps the operation and maintenance personnel identify potential faults or abnormal behaviors.

[0040] Furthermore, this application also includes: performing life-related cleaning based on the reducer monitoring results to obtain life-related monitoring data; the reducer life loss prediction channel includes P reducer life loss prediction models, where P is a positive integer greater than 1; inputting the life-related monitoring data into the P reducer life loss prediction models to obtain P life loss prediction coefficients; calculating the proportion according to the P life loss prediction accuracies corresponding to the P reducer life loss prediction models to obtain P life loss prediction incentive coefficients; performing weighted calculation on the P life loss prediction coefficients according to the P life loss prediction incentive coefficients to generate the life loss prediction result.

[0041] Specifically, by cleaning the monitoring data of the reducer, removing irrelevant or noise-interfering data, effective monitoring data related to the reducer life can be obtained, ensuring that only the data that can accurately reflect the health status and life change of the reducer is retained, such as factors like temperature, vibration, and load. This further helps to more accurately predict the remaining service life of the reducer and provides a basis for subsequent life prediction and optimization.

[0042] Use the P life loss prediction models in the reducer life loss prediction channel to predict the life loss of the reducer. Each reducer life loss prediction model is based on different algorithms and data processing methods. P is a positive integer greater than 1, indicating that multiple prediction models jointly participate in the prediction of life loss, and comprehensively considering multiple prediction results to improve the accuracy and reliability of the prediction.

[0043] Input the life-related monitoring data into the P reducer life loss prediction models for data processing and calculation, and obtain the P life loss prediction coefficients corresponding to the P reducer life loss prediction models, which are used to quantify the possible life loss of the reducer under specific conditions. For example, one model may predict life loss based on temperature changes, while another model may focus on vibration data.

[0044] Based on the proportion calculation of the P life loss prediction accuracies corresponding to the P reducer life loss prediction models, P life loss prediction incentive coefficients are obtained. The prediction accuracies of each model may be different. Furthermore, the P life loss prediction incentive coefficients calculated by the proportion calculation can be assigned different weights according to the reliability of each model, making the final prediction result more accurate. For example, if the prediction accuracy of a certain model is relatively high, its incentive coefficient may be larger, indicating that this model has a greater influence in the overall prediction.

[0045] By performing weighted calculation on the P life loss prediction coefficients according to the P life loss prediction incentive coefficients, a life loss prediction result is generated, which can make the final life prediction result more in line with the actual situation, and further help to make more accurate maintenance and replacement decisions.

[0046] Furthermore, this application also includes: determining whether the life loss prediction result is greater than or equal to the life loss threshold; if the life loss prediction result is greater than or equal to the life loss threshold, taking the life loss prediction result as the life performance optimization target, and making a life performance optimization decision for the gear reducer according to the life-related monitoring data to obtain the first life performance optimization strategy; performing a life impact analysis on the gear reducer according to the first reducer performance optimization strategy to obtain the first strategy life impact analysis result; correcting the first life performance optimization strategy according to the first strategy life impact analysis result to generate the second life performance optimization strategy; and compensating and optimizing the first reducer performance optimization strategy according to the second life performance optimization strategy to obtain the second reducer performance optimization strategy.

[0047] Specifically, the predicted reducer life loss result is compared with the pre-set life loss threshold to determine whether the life loss prediction result is greater than or equal to the life loss threshold. The life loss prediction result represents how much working life the reducer is expected to lose at a certain moment or under certain conditions. The life loss threshold is set according to the usage requirements and safety requirements of the equipment.

[0048] If the life loss prediction result is greater than or equal to the life loss threshold, it is determined that the life loss prediction result reaches the threshold, indicating that the reducer may need maintenance or optimization to prevent premature failure. By taking the life loss prediction result as the life performance optimization target and making a life performance optimization decision for the gear reducer according to the life-related monitoring data, such as the optimization decision may include adjusting the operating conditions, changing the maintenance cycle or optimizing the usage environment, etc., to reduce or delay the life loss, and finally generating the first life performance optimization strategy, that is, a set of implementation plans for the reducer life optimization, and finally obtaining the first life performance optimization strategy.

[0049] Conduct a life impact analysis on the gear reducer according to the first strategy for optimizing the performance of the reducer, evaluate the actual impact of the optimization strategy on the life of the reducer, obtain the results of the life impact analysis of the first strategy, and further obtain whether the life of the reducer is effectively extended after the implementation of the strategy, or what changes have occurred to its operating state. For example, assume that after adjusting the working load of the reducer, the life analysis results show that the equipment life has been extended, which is the result of the life impact analysis of the first strategy.

[0050] Correct the first strategy for optimizing life performance according to the results of the life impact analysis of the first strategy, and generate the second strategy for optimizing life performance to further improve the life performance of the reducer. If the analysis results show that the adjustment is not as effective as expected, the optimization strategy will be corrected, the corresponding parameters or conditions will be adjusted, and a new optimization strategy, that is, the second strategy for optimizing life performance, will be generated to ensure that the equipment operates under the best conditions and maximally extends its service life.

[0051] Furthermore, this application also includes: identifying aging characteristics based on the monitoring results of the reducer to obtain the aging identification results of the reducer; evaluating the aging degree based on the aging identification results of the reducer to obtain the aging coefficient of the reducer; if the aging coefficient of the reducer is greater than or equal to the aging threshold of the reducer, making a performance optimization decision based on the aging identification results of the reducer to obtain the first plan for optimizing the performance of aging characteristics; conducting an aging impact analysis on the gear reducer according to the second strategy for optimizing the performance of the reducer to obtain the analysis results of the aging impact of the second strategy; adjusting the first plan for optimizing the performance of aging characteristics according to the analysis results of the aging impact of the second strategy to obtain the second plan for optimizing the performance of aging characteristics; optimizing the second strategy for optimizing the performance of the reducer according to the second plan for optimizing the performance of aging characteristics to generate the third strategy for optimizing the performance of the reducer; and optimizing the performance of the gear reducer according to the third strategy for optimizing the performance of the reducer.

[0052] Specifically, by processing and comparing sensor data, identify aging characteristics based on the monitoring results of the reducer, that is, wear, corrosion, or other phenomena that cause performance degradation, to obtain the aging identification results of the reducer, such as rising temperature, increased vibration, etc.

[0053] Evaluate the aging degree based on the aging identification results of the reducer to obtain the aging coefficient of the reducer, which is used to represent the quantitative degree of performance loss caused by aging during the entire use process of the reducer. The aging of the reducer is determined by analyzing various operating data of the reducer, such as vibration amplitude, temperature change, etc.

[0054] If the aging coefficient of the speed reducer is greater than or equal to the aging threshold of the speed reducer, indicating a relatively high degree of aging of the speed reducer and the need for performance optimization, then a performance optimization decision is made based on the aging recognition result of the speed reducer. By adjusting the operation mode, control strategy or performing maintenance, a first aging characteristic performance optimization plan is obtained to extend the service life of the speed reducer and improve the operation efficiency.

[0055] After implementing the first aging characteristic performance optimization plan, according to the new performance optimization strategy, further analyze the impact of this strategy on the aging of the speed reducer. Based on the current state of the speed reducer, evaluate whether this strategy can effectively slow down the aging process and obtain the analysis result of the impact of the second strategy on aging. For example, the working load of the speed reducer may be adjusted, or a cooling system may be adopted to reduce the temperature, thereby slowing down the aging phenomenon.

[0056] Adjust the first aging characteristic performance optimization plan according to the analysis result of the impact of the second strategy on aging, further adjust the aging characteristic processing method of the speed reducer, optimize the identification and countermeasures of aging characteristics, and can continuously improve the operation state of the speed reducer and prevent it from deteriorating further. For example, if the analysis result shows that the cooling system is not effective enough, the cooling method may be adjusted or auxiliary heat dissipation equipment may be added to reduce the temperature and slow down the aging process.

[0057] According to the second aging characteristic performance optimization plan, optimize the second performance optimization strategy of the speed reducer, including adjusting operation parameters, optimizing the maintenance plan, etc., and then generate the third performance optimization strategy of the speed reducer. The third performance optimization strategy of the speed reducer can better address the aging problem of the speed reducer, thereby extending the service life of the equipment and improving its operation efficiency. For example, if aging causes increased wear of some components of the speed reducer, the optimization strategy may include replacing components in advance.

[0058] Perform performance optimization on the gear speed reducer according to the third performance optimization strategy of the speed reducer, that is, perform actual performance optimization operations on the speed reducer, including adjusting the working load of the speed reducer, optimizing the settings of the cooling system or replacing components with greater wear, so that the performance of the speed reducer is further improved, and it can maintain high efficiency and reliability during the aging process, thereby reducing downtime and increasing the use value of the overall equipment.

[0059] Furthermore, this application also includes: extracting the k-th parameter of the speed reducer monitoring according to the multi-source monitoring data, where k is a positive integer; collecting the state information of the sensing device corresponding to the k-th parameter of the speed reducer monitoring to obtain the k-th device state data; performing anomaly detection on the k-th device state data to obtain the k-th device state anomaly data; based on the k-th device state anomaly data and the k-th parameter of the speed reducer monitoring, according to the sensing anomaly correction model, obtaining the k-th parameter correction result; adding the k-th parameter correction result to the speed reducer monitoring result.

[0060] Specifically, multi-source monitoring data refers to various types of data collected from a speed reducer through multiple sensors, such as vibration signals, temperature signals, or acoustic signals. Based on the multi-source monitoring data, the k-th parameter for speed reducer monitoring is extracted, that is, specific information is selected from the overall data, which is the k-th type of parameter among all monitoring parameters, where k is a positive integer. For example, k can be 1 or 2. By extracting this parameter, it is possible to focus more on analyzing its changing trend.

[0061] Next, the state information of the sensing device corresponding to the k-th parameter for speed reducer monitoring is collected to obtain the k-th device state data. Here, "collection" means obtaining the corresponding data; the state information of the sensing device represents the operating condition of the sensor associated with the k-th parameter, such as the voltage, sensitivity, or operating temperature of the sensor, which is used to determine whether the sensor itself is working properly.

[0062] Then, anomaly detection is performed on the k-th device state data to obtain the k-th device state anomaly data. Here, anomaly detection refers to analyzing whether the data deviates from the normal range. For example, the change in the sensitivity of a temperature sensor is monitored. If the sensitivity decreases, it may be considered that there is an anomaly in the device, and thus the data marked as abnormal in the detection is obtained, providing a basis for subsequent processing.

[0063] Next, the sensing anomaly correction model is used to correct the deviation caused by sensor anomalies. For example, the intermittent signal of the sensor is corrected by an interpolation method or the problem of reduced sensitivity is corrected by parameter adjustment. Based on the k-th device state anomaly data and the k-th parameter for speed reducer monitoring, according to the sensing anomaly correction model, the corrected result of the k-th parameter is obtained, and the corrected result of the k-th parameter is data that is closer to the real situation after correction.

[0064] Finally, according to the method of obtaining the corrected result of the k-th parameter, the multi-source monitoring data is accessed in sequence to obtain the corrected result of each parameter corresponding to the speed reducer monitoring parameter, and each parameter corrected result is combined into the speed reducer monitoring result. The speed reducer monitoring result is the comprehensive performance of all monitoring parameters. For example, it records the health status of vibration, temperature, and noise signals, ensuring that the final monitoring data is more reliable, thereby providing a more scientific decision-making basis for equipment management.

[0065] Furthermore, this application also includes: performing a sensing anomaly impact evaluation based on the k-th device state anomaly data to obtain the k-th sensing anomaly impact coefficient; determining whether the k-th sensing anomaly impact coefficient is greater than or equal to the sensing anomaly impact threshold; if the k-th sensing anomaly impact coefficient is greater than or equal to the sensing anomaly impact threshold, activating the sensing anomaly correction model; inputting the k-th device state anomaly data and the k-th parameter for speed reducer monitoring into the sensing anomaly correction model to obtain the corrected result of the k-th parameter.

[0066] Specifically, by analyzing the abnormal state data generated during the operation of the k-th device, the possible impact of the abnormal data on the sensor performance is evaluated, and the k-th sensing abnormal impact coefficient is obtained. The abnormal data may come from faults or abnormal states that occur during the operation of the device, such as too high temperature or too large vibration, etc. By analyzing these data, the sensing abnormal impact coefficient is obtained, which represents the degree of influence suffered by the sensor in the abnormal state. For example, if a device fails and causes severe vibration, the vibration sensor may be greatly interfered, and the impact coefficient will be higher.

[0067] Next, the sensing abnormal impact coefficients are compared to determine whether the k-th sensing abnormal impact coefficient is greater than or equal to the sensing abnormal impact threshold. The sensing abnormal impact threshold is a predefined standard used to determine whether the abnormal state of the device is severe enough to require further corrective measures.

[0068] If the k-th sensing abnormal impact coefficient is greater than or equal to the sensing abnormal impact threshold, indicating that the impact of the abnormality is large enough, the sensing abnormal correction model is activated to adjust or correct the sensor readings and measurement results according to the abnormal state of the device and the impact coefficient, so as to obtain more accurate monitoring data.

[0069] After activating the sensing abnormal correction model, the state abnormal data of the k-th device and the k-th parameter monitored by the speed reducer related to the speed reducer are input into this model. The sensing abnormal correction model adjusts the measured result of the k-th parameter according to these data to obtain the corrected result of the k-th parameter. For example, if the temperature value of the speed reducer read by the sensor is inaccurate, the corrected result can be corrected to be closer to the actual temperature.

[0070] In summary, the gear reducer performance optimization method for multi-source data analysis provided by this application has the following technical effects: The gear reducer is monitored in real time according to a pre-deployed multi-source sensor array to obtain multi-source monitoring data; The multi-source monitoring data is adaptively corrected for the influence of sensing anomalies according to the sensing anomaly correction model to generate a reducer monitoring result; The transmission characteristics are identified and integrated according to the reducer monitoring result to establish a transmission characteristic monitoring matrix; The control scheme of the gear reducer is read, and the transmission performance deviation of the gear reducer is optimized in combination with the transmission characteristic monitoring matrix to obtain the first gear reducer performance optimization strategy; Based on the reducer monitoring result, the life loss of the gear reducer is predicted according to the reducer life loss prediction channel to obtain a life loss prediction result; Based on the life loss prediction result, the first gear reducer performance optimization strategy is optimized for life performance compensation to generate the second gear reducer performance optimization strategy; The performance of the gear reducer is optimized according to the second gear reducer performance optimization strategy. That is to say, by achieving the technical goals of improving the monitoring accuracy of the gear reducer and multi-dimensional fault detection, the technical effects of improving the monitoring accuracy, realizing diversified monitoring and intelligent maintenance are achieved.

[0071] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0072] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application is also intended to include these changes and modifications.

Claims

1. A gear reducer performance optimization method based on multi-source data analysis, characterized in that: include: The gear reducer is monitored in real time according to the pre-deployed multi-source sensor array to obtain multi-source monitoring data; Adaptively correct the impact of sensor anomalies on the multi-source monitoring data according to the sensor anomaly correction model to generate a reducer monitoring result; According to the reducer monitoring results, transmission characteristics are identified and integrated to establish a transmission characteristic monitoring matrix; Reading the control scheme of the gear reducer, optimizing the transmission performance deviation of the gear reducer in combination with the transmission characteristic monitoring matrix, and obtaining a first strategy for optimizing the performance of the reducer; Based on the reducer monitoring result, predicting the life loss of the gear reducer according to the reducer life loss prediction channel to obtain a life loss prediction result; Based on the life loss prediction result, the first strategy for optimizing the performance of the reducer is optimized for life performance compensation to generate a second strategy for optimizing the performance of the reducer; The gear reducer is optimized in performance according to the second strategy for optimizing the reducer performance.

2. The method according to claim 1, characterized in that The control scheme of the gear reducer is read, and the transmission performance deviation of the gear reducer is optimized in combination with the transmission characteristic monitoring matrix to obtain a first strategy for optimizing the performance of the reducer, including: Execute transmission characteristic expected deviation identification of the gear reducer according to the control scheme and the transmission characteristic monitoring matrix to obtain a transmission deviation identification result; Perform transmission performance impact evaluation according to the transmission deviation identification result to obtain a transmission performance impact coefficient; Determining whether the transmission performance influence coefficient is greater than or equal to a transmission performance influence threshold; If the transmission performance influence coefficient is greater than or equal to the transmission performance influence threshold, activating the transmission performance deviation optimization map; The transmission deviation identification result is input into the transmission performance deviation optimization map to generate the first strategy for optimizing the performance of the reducer.

3. The method according to claim 2, characterized in that Executing the transmission characteristic expected deviation identification of the gear reducer according to the control scheme and the transmission characteristic monitoring matrix to obtain the transmission deviation identification result includes: Perform transmission characteristic normal parameter retrieval on the gear reducer according to the control scheme to obtain a transmission characteristic parameter retrieval set; Performing a central tendency analysis based on the transmission characteristic parameter retrieval set to establish a transmission characteristic expectation matrix; Deviation identification is performed on the transmission characteristic monitoring matrix according to the transmission characteristic expected matrix to generate the transmission deviation identification result.

4. The method according to claim 1, characterized in that Based on the reducer monitoring result, the life loss prediction of the gear reducer is performed according to the reducer life loss prediction channel to obtain the life loss prediction result, including: Perform life-related cleaning according to the reducer monitoring result to obtain life-related monitoring data; The reducer life loss prediction channel includes P reducer life loss prediction models, where P is a positive integer greater than 1; Inputting the life-related monitoring data into the P reducer life loss prediction models to obtain P life loss prediction coefficients; According to the P life loss prediction accuracies corresponding to the P reducer life loss prediction models, a proportion calculation is performed to obtain P life loss prediction incentive coefficients; The P life loss prediction coefficients are weightedly calculated according to the P life loss prediction incentive coefficients to generate the life loss prediction result.

5. The method according to claim 4, characterized in that Based on the life loss prediction result, the first strategy for optimizing the performance of the reducer is optimized for life performance compensation to generate a second strategy for optimizing the performance of the reducer, including: Determining whether the life loss prediction result is greater than or equal to a life loss threshold; If the life loss prediction result is greater than or equal to the life loss threshold, the life loss prediction result is used as the life performance optimization target, and a life performance optimization decision is made for the gear reducer according to the life-related monitoring data to obtain a first life performance optimization strategy; Performing a life impact analysis on the gear reducer according to the first strategy for optimizing the performance of the reducer, and obtaining a life impact analysis result of the first strategy; Correct the first strategy for optimizing life performance according to the life impact analysis result of the first strategy, and generate a second strategy for optimizing life performance; According to the second strategy for optimizing life performance, the first strategy for optimizing reducer performance is compensated and optimized to obtain the second strategy for optimizing reducer performance.

6. The method according to claim 1, characterized in that Optimizing the performance of the gear reducer according to the second strategy for optimizing the performance of the reducer, further comprising: Perform aging feature identification according to the reducer monitoring result to obtain a reducer aging identification result; According to the reducer aging identification result, an aging degree assessment is performed to obtain a reducer aging coefficient; If the reducer aging coefficient is greater than or equal to the reducer aging threshold, a performance optimization decision is made according to the reducer aging identification result to obtain a first aging characteristic performance optimization scheme; Performing an aging impact analysis on the gear reducer according to the second strategy for optimizing the performance of the reducer, and obtaining an aging impact analysis result of the second strategy; According to the aging impact analysis result of the second strategy, the first aging characteristic performance optimization scheme is adjusted to obtain a second aging characteristic performance optimization scheme; According to the second aging characteristic performance optimization scheme, the second reducer performance optimization strategy is optimized to generate a third reducer performance optimization strategy; The gear reducer is optimized in performance according to the third strategy for optimizing the reducer performance.

7. The method according to claim 1, characterized in that Adaptively correcting the influence of sensor anomalies on the multi-source monitoring data according to the sensor anomaly correction model to generate a reducer monitoring result, including: Extracting the kth parameter of the reducer monitoring according to the multi-source monitoring data, where k is a positive integer; Collecting the sensor device status information corresponding to the kth parameter monitored by the reducer to obtain the kth device status data; Performing anomaly detection on the k-th device status data to obtain k-th device status abnormality data; Based on the kth device state abnormality data and the kth parameter monitored by the reducer, and according to the sensor abnormality correction model, a kth parameter correction result is obtained; The kth parameter correction result is added to the reducer monitoring result.

8. The method according to claim 7, characterized in that Based on the kth device state abnormality data and the kth parameter monitored by the reducer, and according to the sensor abnormality correction model, a kth parameter correction result is obtained, including: Perform sensor abnormality impact evaluation according to the kth device state abnormality data to obtain the kth sensor abnormality impact coefficient; Determining whether the kth sensor abnormality influence coefficient is greater than or equal to a sensor abnormality influence threshold; If the kth sensor abnormality influence coefficient is greater than or equal to the sensor abnormality influence threshold, activating the sensor abnormality correction model; The kth device status abnormal data and the kth parameter monitored by the reducer are input into the sensor abnormality correction model to obtain the kth parameter correction result.

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